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Last Updated: 25 Nov 2020
Note: Program Uses Singapore Standard Time and is 8 hours ahead of GMT (GMT+08:00)

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CONFERENCE DAY: December 15, 2020

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2:15 PM - 2:30 PM

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Decision Analysis and Methods 1
08:00 - 09:30

Chairs: Carman Ka Man LEE The Hong Kong Polytechnic University, Vinay SINGH ABV-IIITM Gwalior


Stress-based Lattice Structure Design for Additive Manufacturing

Adrian CHUNG BAEK, Namhun KIM

Recent developments in additive manufacturing (AM) have induced the research on development of lattices, engineered structures with high performances. Unfortunately, these structures are very challenging to design as conventional design techniques usually entail rigorous and expensive numerical calculations which halt the overall process. Their intricate frameworks lead to time-consuming and inefficient design processes, especially for inexperienced designers who are yet familiar with AM processes. Furthermore, the resulting designs are often infeasible due to manufacturing constraints, particularly AM constraints, being neglected in the design process. In this research, a novel design method is proposed considering the clarity of the design process as well as the computational efficiency and the manufacturability of the suggested structure design. The proposed method utilizes the stress field information to summon pre-defined cubic cells and generate the lattice structure. This results in a simplified procedure and a reduction of the computational cost. To consider the manufacturing constraints for the AM process, a modified STL slicing algorithm is integrated into the developed framework. Several examples are presented with reasonable solutions that demonstrate the efficiency of the proposed method.


Launch Strategies for Upgraded Products with Consideration of Self-cannibalization

Chao Nan LI, Yan CHEN, Hai Jiao LUAN, Li Shan WANG, Le Xin SUN, Tong Xin WANG, Xin LI

This paper studies optimal pricing and timing of a firm to launch an upgraded product to maximize its revenue, considering self-cannibalization between two generation products. Heterogeneous customers arriving to the market compare utilities resulted from existing and new products and try to maximize their utilities. We model the firm and customers’ decision-makings as a two-stage dynamic game and its equilibrium results are derived. Our numerical analysis shows that technology innovation of the new product significantly improves the firm’s revenue performance. Technology innovation brings added value to the new product, which allows a higher pricing for the upgraded product. However, it has no impact on the firm’s optimal launch time. In addition, depreciation speed of the product has a negative impact on the firm’s total revenue. However, it does not affect optimal launch strategies, which include optimal launch price and launch time.


Using Classification with K-means Clustering to Investigate Transaction Anomaly

Xing Scott TAN, Zijiang YANG, Younes BENLIMANE, Eric LIU

Applications of machine learning and related algorithms in Electronic Commerce (hereafter E-Commerce) have the potential to build robust analytical models that help examine transaction data and successfully detect and predict anomalies. Nonetheless, the robustness of such models can be undermined in the case of highly unbalanced data set. This paper presents a classification method built on K-means Clustering that addresses the issue of highly unbalanced data. In this method, we first pre-process our E-Commerce data and then apply clustering and classifying procedures to create a number of clusters where each resulting cluster includes similar transaction records. Next, four classifiers including Logistic Regression, Naive Bayes, RBFNetwork and NBtree classifiers are used to assess the resulting solution. Findings based on real-word data show that this method provides a better solution for transaction anomaly detection and prediction than traditional approaches. They also show that it straightforwardly resolves classification problems with data imbalance.


Two-Stage Newsvendor Problem with Loss Aversion and Probability Weighting Effects

Cunwu SUN, Hongqing YE, Wei WENG, Qingwei WANG

This paper studies a two-stage newsvendor problem with two suppliers, where the retailer’s loss aversion and probability weighting effects are considered. We solve the model by backward induction and derive the optimal order quantities in the first and second stages. We provide the prerequisites of the retailer’s selection between the two suppliers. We find that the optimal order quantity in the first stage decreases with the loss aversion coefficient and the capacity of the second-stage, and increases with the cost of the second-stage. As the coefficient of the probability weighting function increases, the optimal first-order quantity may increase or decrease.


A Multiple Layer DEA Model for Evaluating Corporate Sustainable Performance Using Lean Manufacturing Practices


The purpose of this study is to generate a quantitative means of assessing the relationship between lean production practices and a company’s sustainable performance. A composite lean index (CLI) is formulated using multi-attribute value theory (MAVT) in order to measure a firm’s lean performance. A composite sustainability index (CSI) is developed using MAVT to measure a company’s sustainable performance considering its quadruple bottom line. Lastly, a multiple layer data envelopment analysis (MLDEA) model is set up to evaluate a company’s sustainable performance using its lean manufacturing practices. The CLI is considered as the virtual input to the MLDEA model while the (CSI) is treated as the virtual output. “Efficient” firms are determined to score high in Supplier and Customer Relationships and Human Resources whereas “inefficient” companies scored low in Supplier and Customer Relationships and Process and Equipment. Furthermore, “efficient” companies are found to exhibit high scores in Worker Training, Parts Delivered JIT by Supplier, and Customer Orders Delivered JIT while “inefficient” companies have low scores in Orderliness and Cleanliness in the Plant, Error-Proof Equipment, and Rigorous Preventive Maintenance.


Application of Lean Six Sigma Methodology and Queueing Theory to Minimize Systemic Variability: A Case Study from Public Services


This manuscript examines the application of queueing theory as a means to minimize systemic variability and implement Lean Six Sigma (LSS) based initiatives in an office and/or knowledge work environment. The purpose of this study is to address and systematically deal with the challenges and utilization of limited resource capacity and to improve functional performance. An exploratory case study is conducted at one Norwegian police district, where participatory involvement by a crime investigation department was initiated. It was qualitatively assessed and identified that operating the public service reception on weekends was not considered value added for their value stream. Data was collected to determine operational measures relative to the reception`s queueing model. The findings demonstrate significantly over-dimensioned use of capacity relative to public demand on weekends. Accordingly, by closing the service reception and re-allocating this capacity to critical crime investigation, there is a utilization potential with relatively low risk of public dissatisfaction, negative media coverage and increased arrivals on weekends.

Technology and Knowledge Management 1
08:00 - 09:30

Chairs: Arnesh TELUKDARIE University of Johannesburg, Tingyao XIONG Radford University


Adoption of Information Technology in Modern Manufacturing Operation

Shu Lun MAK, Chi Ho LI, W. F. TANG, Ming Yan WU, C. W. LAI

The competition of manufacturing industry becomes serious and the enterprises are seeking for new opportunities to improve their competitiveness. The information and automation technologies are one of the possible solutions to improve the efficiency and competitiveness of manufacturers. This paper firstly reviews how both theoretical models, technology acceptance model (TAM) and diffusion of innovation (DOI) to affect the application of technology. Then the German and China governmental policies are discussed and the current situation of using information and automation technology in manufacturing operation are discussed. Finally how the application of technology improving productivity is concluded.


Knowledge Revealing and Organizational Legitimacy: Comparisons between Different Types of Firms in China

Yan XIE, Jiaqi HUANG, Kai XU

Knowledge revealing, as a unique form of open innovation, increasingly attracts interests in academia and practice. Yet, its impacts on firms are still under-researched. Based on open innovation research and impression management perspective, this paper examines the relationship between knowledge revealing and organizational legitimacy, and how the relationship varies between different types of firms in the Chinese context. Using the survey data from 217 manufacturing firms in China, we find that knowledge revealing has positive impacts on both political and business legitimacy. These impacts are stronger for new ventures than for established firms. While knowledge revealing has a stronger influence on political legitimacy for non-state-owned enterprises (non-SOEs) than for state-owned enterprises (SOEs), the effect of knowledge revealing on business legitimacy does not show significant difference between non-SOEs and SOEs. Our study, therefore, uncovers the legitimacy benefit of knowledge revealing in a firm-type-dependent manner.


Promoting the Original Innovation for Disruptive Technology

Xin ZHENG, Yali ZHANG, Liaoliao LI, Nan CHANG, Jianwu XUE

For the “COVID-19” pandemic spreading around the globe, the successful development of nucleic acid testing reagents is critical to disease prevention and control. It has also made governments and institutions aware of the importance of original innovation for disruptive technology. Based on the literature review, this paper discusses the concepts of disruptive technology and original innovation. It then extracts the core elements for original innovation by analyzing two cases of the fundamental research teams of Tsinghua University. Finally, it puts forward a model on achieving original innovation for disruptive technology to a project or team.


How Do Innovation Intermediaries Influence Outbound Open Innovation in China? A Moderated Mediation Mechanism

Yan XIE, Kai XU, Jiaqi HUANG

Although firms devote much attention to outbound open innovation for economic or strategic benefits, how outbound open innovation is conducted effectively is still under-studied in the literature. Our paper introduces innovation intermediaries as an antecedent, and based on capability-based framework for open innovation and institutional theory constructs a moderated mediation mechanism through which innovation intermediaries influence outbound open innovation in China. The results of empirical test show that innovation intermediaries influence outbound open innovation via desorptive capacity, particularly in the form of two parallel routes, namely via identification capacity and transfer capacity. The relationship between innovation intermediaries, desorptive capacity and outbound open innovation is stronger in underdeveloped regions than in developed regions.


A Study on Ambidextrousness of R&D Organization in ICT Companies

Iori NAKAOKA, Yunju CHEN, Yousin PARK, Hirochika AKAOKA, Seigo MATSUNO

The purpose of this research is to examine the reason why Japanese ICT companies have not gained competitive advantages in the global markets by focusing on the problem of R&D organizational form in innovation, especially from the perspective of ambidextrous organization. We propose an exploration degree to measure the ambidextrousness of R&D organization that is calculated by social network analysis and text analytics method. As a result, the exploration degree on Japanese ICT companies are lower than foreign rival company which means Japanese companies tend to emphasize the exploitation type innovation and lack for explorational innovation.


Impact of Reabsorption of Spilled Knowledge on Patent Value

Takafumi MIYAZAKI, Ryo TAKEMURA, Takuya HARADA, Noritomo OUCHI

Innovation is often achieved by recombining existing knowledge. Thus, effective utilization of external knowledge is key for firms creating innovation. The concept of reabsorption, where an originating firm reabsorbs its spilled knowledge, including the advancements made by recipient firms, has attracted attention. Existing studies have not clarified the impact of reabsorption on patent value and have not confirmed whether an originating firm reabsorbs knowledge in the same or different technological fields. Therefore, this study attempts to examine the impact of reabsorption of knowledge on patent value considering technological fields using patent data. Reabsorption of spilled knowledge was tracked using patent citation data, and patent value was evaluated by the number of citations. By conducting negative binomial regression, we concluded that the reabsorption of spilled knowledge has a positive impact on patent value. Furthermore, the impact is greater when a firm reabsorbs spilled knowledge in different technological fields. Our results suggest that a firm has the ability to effectively create innovation by reabsorbing spilled knowledge.

Operations Research 1
08:00 - 09:30

Chairs: Hyeong Suk NA South Dakota School of Mines and Technology, Kuo-Wei WU National Taiwan University


A Robust Optimisation Formulation for Post-departure Rerouting Problem

Miriam BONGO, Charlle SY

In this paper, a robust optimisation approach for air traffic flow management problem involving rerouting of flights under uncertainty is explored. During disruptions in typical aircraft operations, an inherent uncertainty in the capacity of airspace resources becomes evident. Therefore, when flights are en-route, stakeholders are required to arrive at a decision of assigning an alternate route so as to support the continued aircraft operations. Considering that stakeholders of the commercial aviation industry maintain individual interests other than the collective interest of safe operation of flights, a robust optimisation model is developed as a decision support system for rerouting of flights integrating these individual interests. The key results obtained from the illustrative case study conducted show that the route assignment varies significantly with the level of uncertainty. Such variations present important insights to the stakeholders in terms of resource capacity and cost, among others.


Behavioral Model to Understand Hurricane Evacuation Decision Making Affected by Social Influence

Hyeong Suk NA

Over the past few decades, human evacuation departure behavior has been described by various departure time choice models (DTCMs). Although this decision is given at the individual level, social media is transforming the communication channels for sharing the evacuation-related information during the evacuation; therefore, through social media evacuees collectively influence their departure time decisions and this causes high correlation among individual decisions. While it is apparent that this high correlation could be one of the main reasons of the simultaneous departures, there are only very few evacuation studies considering the effect of social influence on the human evacuation behavior. In this paper, to describe the departure behavior of evacuees interconnected by social influence, we propose a DTCM using a time inhomogeneous, discrete-time Markov chain. In other words, the evacuation decision probability of each evacuee at each stage is collectively affecting each other's decision in a social network representing a community of evacuees sharing the evacuation-related information. A numerical case study is provided to understand the effect of the evacuation planning on different social network topologies and the human evacuation behavior.


Job Shop Scheduling Problem Neural Network Solver with Dispatching Rules

Mun Hon SIM, Malcolm Yoke Hean LOW, Chin Soon CHONG, Mojtaba SHAKERI

Job Shop Scheduling Problem (JSSP) is an optimization problem in computer science and operations research. Many problems in real-world manufacturing processes can be translated into JSSP. In recent years, Machine Learning has shown great promises in solving optimization problems and can be used to solve JSSP instances. In this paper, an Artificial Neural Network (ANN) was designed and trained to solve JSSP instances using the priority of the operations as the learning output. Dispatching rules were implemented to break ties during the decoding of the priorities. Our experiment results showed that a hybrid algorithm that combines the best of ANN with dispatching rules and standalone dispatching rule-based heuristic outperforms previously reported results.


Portfolio Selection Utilizing Electronic Company Stocks During the Enhance Community Quarantine Period in the Philippines

Michael N. YOUNG, Godfrey AREVALO, Ezekiel MALLARI

This study presents a portfolio selection framework utilizing electric company stocks during the COVID-19 pandemic in the Philippines. A set of criteria is presented to identify the investment pool composed of electric companies. Returns are estimated through historical returns and assumed to be equally likely. Then, equally weighted portfolio strategy is applied to identify the optimal portfolio. The portfolio is then compared to a counterpart portfolio, a benchmark and the market. Back-test shows that the electric company portfolio outperforms its counterpart portfolio in all aspect, it can also outperform the market in terms of returns and standard deviations and be at par with the benchmark. This may lead to a generic portfolio selection framework for individual investors.


Performance Analysis of an Open Cycle Gas Turbine Power Plant in Grid Electricity Generation


In this study, there was a performance analysis of a working 30MW open cycle gas turbine power plant to get a general overview of role and performance of gas turbines in supply of grid electricity. The historical evaluation of the gas turbine power plant technology is presented with features and application of gas turbines in electricity generation. It was found that a significant amount of energy is lost with the exhaust gases, which are released to the atmosphere. The amount was up to 74.6%, which is wasteful, polluting and unsustainable. Analysis on possible conversion from the open cycle to combined cycle plant demonstrated that subject to practical limitations, specific fuel consumption reduces by 22% and power output increases by 11.53 MW, which is 38% increase from the Rankine cycle turbine. This will improve on specific fuel consumption, generate more revenue and contribute towards greenhouse emission mitigation through avoided use of fossil fuels for extra power generated.


Managing Sustainability in Electricity Generation


This study looked energy sustainability and sustainable development and measures necessary to ensure that electricity generation is sustainable. Current literature in form of published journal articles and conference proceedings as well as scientific and technical reports on the area of sustainable energy and development was examined. It was noted that there can be no development without energy and there can never be sustainable development without sustainable energy. There is need to sustainably exploit the energy resources to meet current energy needs and those of future generations in an environmentally friendly manner while noting that about 35% of the anthropogenic greenhouse gas emissions come from energy related activities in power generation. Technological advances which include smart grids and decentralization of generation as well as energy carrier technologies are rapidly providing electricity access options available beyond the traditional grid. The study showed that electrification of the global energy mix, electrification of the transport sector, energy efficiency measures and enhanced use of low carbon and renewable energy resources for electricity will play a significant role in the future sustainable energy transition. Additionally to realize energy sustainability effective measures include increased use of solar and wind for grid electricity, use of sustainable energy carriers like hydrogen, development and adoption of energy efficiency measures, limiting environmental impact of energy use including carbon sequestration and enhancing socioeconomic acceptability through community involvement and social acceptability, economic affordability and equity, lifestyles, land use and aesthetics. Whereas renewable energy is a solution to sustainable energy and electricity, current technology and limitation make it necessary to have an optimized mix of renewable and low carbon nonrenewable for sustainable grid electricity.

Supply Chain Management 1
08:00 - 09:30

Chairs: Adnan HASSAN Universiti Teknologi Malaysia, Yugowati PRAHARSI Shipbuilding Institute of Polytechnic Surabaya


Modeling of an Industrial Ecosystem at Traditional Shipyards in Indonesia for the Sustainability of the Material Supply Chain

Yugowati PRAHARSI, Muhammad ABU JAMI’IN, Gaguk SUHARDJITO, Hui-Ming WEE

Traditional shipyard in East Java Indonesia has produced lot of wooden fishing boats. However, the sustainability of its production process has not yet been discussed. In this study, we aimed to investigate how the model of industrial ecosystem on wooden boat building. We surveyed to the traditional shipyards and interviewed the worker, the owner and the project leader. We also discussed about the activities of sustainable supply chain to support the industrial ecosystem. Finally, some recommendations are proposed to support the regulations on environment, social, and economics sectors to achieve the sustainability of the material supply chain.


The Optimal Choice for Local Content Requirement and Tariff Under Social Welfare Maximization


Local content requirement (LCR) is widely used by governments to protect the local economy in many countries. In this paper, we study a model with one foreign OEM who can source components from a foreign supplier with advanced technology and a local supplier. We consider this problem from the local government’s perspective and maximize the social welfare to derive closed-form solutions for the optimal LCR and tariff. The result shows that (1) the LCR policy is beneficial when the technical gap between local and global suppliers is low, and (2) the optimal LCR and tariff, as well as the corresponding OEM’s after-tax profit and social welfare increase with respect to the market size. Furthermore, (3) the host government benefits from the technology upgrade of global suppliers under tariff mode and losses from it under LCR mode, while the OEM can gain more profit in both modes.


Flood Shelters Location Using P-median Model


In the past recent years, the northeastern part of Thailand called “E-san” has experienced flooding. The death toll in year 2019 flood reached at least 33 people. For proactive flood response, the authority must plan effort schemes to provide shelters and foods. The location of flood relief shelter is a key for assistance to flood victims. This paper is to employ P-median model by incorporating with historical data to determine the suitable flood relief shelters. We also examine two scenarios: in the first one district is served by only one flood relief shelter and in last one the number of flood relief shelter to district depending on the number of residents who dwells in that district. We tested our model with data collected from the district in the southeastern E-san areas.


An Inventory Optimization Model Under Demand Uncertainty for Autonomous Multi-site Inventory Planning with Material Substitutability and Transshipment

Mojtaba SHAKERI, Chi XU, Puay Siew TAN

This paper addresses multi-site inventory planning of raw materials in the packaging printing industry subject to demand uncertainty. Two types of material A and B exist in this setting where material B is converted to material A and material A is used for the main production. In addition, items of type A are substitutable with similar material specifications and can be transshipped across production sites to balance the inventory level in the entire network. The practice is currently carried out purely manually in the packaging printing sites of our industrial partner. With that in mind, we develop a holistic inventory optimization model to automate optimal inventory replenishment of raw materials with substitutability and transshipment support across multiple production sites. The objective is to minimize the global expected cost with respect to stochastic material demand.


Dual Sourcing Problem with Capacities and Setup Cost

Xiaoqian SHI, Linlin JI, Meimei ZHENG

This paper studies the ordering problem of a retailer, sourcing from two suppliers (i.e., domestic and offshore suppliers). Compared to the domestic sourcing, the offshore sourcing is cheaper with larger capacity, but it incurs additional costs (i.e., setup costs), such as cross transaction and quality control costs. We characterize the retailer’s optimal order quantities from domestic and offshore suppliers. Our analytical result shows that the retailer’s ordering policy is not affected by the setup cost when the setup cost is large enough. Numerical analysis reveals that the setup cost and the capacity of the offshore supplier have negative effects on the total order quantity.

Systems Modeling and Simulation 1
10:00 - 11:30

Chairs: Zhiqiang CAI Northwestern Polytechnical University, Yoshinobu TAMURA Tokyo City University


A Mixed-integer Programming Approach to Group Control of Elevator Systems with Destination Hall Call Registration

Yulun WU, Shunji TANAKA

In this research, we focus on group control of an elevator system with destination hall call registration where passengers can directly register destination floors at every elevator lobby. To improve the elevator performance when transporting passengers, finding the optimal passenger-to-car assignment and car routing is considered as a good way. We formulate the problem of optimizing passenger-to-car assignment and car routing as a mixed-integer programming problem to minimize the average waiting time of all passengers waiting at elevator lobbies. Then, we perform computer simulation using a commercial integer programming solver and examine the effectiveness of the proposed optimization model. A conventional approach which is applied in most current elevator system is also compared with our approach.


Additive Manufacturing of Dynamic Lifeboat Hook Assembly

Ulanbek AUYESKHAN, Namhun KIM, Van Loi TRAN, Chung-Soo KIM, Dong-Hyun KIM

Using Additive Manufacturing (AM) technology, Design for Additive Manufacturing (DFAM) enables us to consolidate sophisticated mechanical assemblies. The part consolidation by additive manufacturing has been mainly applied to only static systems such as brackets. In this study, we try to extend the current application area of DFAM to a dynamic product. With this regard, our work aims to redesign and conduct design validation of a lifeboat hook system with a dynamic operation mechanism. The objective is to decrease the number of hook components by leveraging conventional part consolidation including constraints such as heavy load bearing and functional dynamics. Furthermore, along with the side plates and groups which consist of numerous parts such as hook, cam and stopper were decreased by 55%. FEM analysis shows that in case of consolidated hook, even though original safe working load was increased by 2 times, our design seems to be promising candidate for replacing traditional lifeboat hook. Finally, a downscaled consolidated hook system was successfully built by a PBF machine and analyzed for precision.


Effect of Network Structure and Preference Difference on Knowledge Transfer in Inter-organizational R&D Project

Xiaonan WANG, P. GUO, D. WANG

An evolutionary game model of knowledge transfer in inter-organizational R&D projects was established, and its local stability was analyzed. Then, the complex network and preference theory are introduced to establish the game model of knowledge transfer in the cooperation network of inter-organizational R&D projects under the condition of preference differences and different network structures. Finally, the influence of key factors, preference difference and network structures on strategy selection is analyzed. The results show that the cost coefficient has a negative correlation with the level of knowledge transfer, while the increase of other coefficients promote knowledge transfer behavior. The increase of altruistic preference degree and the proportion of altruistic preference agents can promote knowledge transfer behavior, while the increase of competitive preference degree and the proportion of competitive preference agents can inhibit knowledge transfer behavior. Moreover, the level of knowledge transfer is higher in the scale-free network than in the small-world network in most cases. However, punishment plays a greater role in the small-world network.


Container Movement Evaluation Using System Dynamics Simulation

Fajar KURNIAWAN, Siti Nurmaya MUSA, Noor Hasnah MOIN

The paper evaluates the effect of container handling rules to the smoothness of container movement. The movement includes box transfer from into vessel, movement in stacking yard and discharging box in-out the port. High amount container stacked in stacking yard indicates problem in container flow. The goals of this research was to find out the variable that affected balance of container moves, then analyze rules of transfer process and set out the option of reference in order to reduce idle time and excessive stock of containers in stacking yard. The value of this study is to extract factors for reducing idle time and unbalance of container quantity in the chain of transfer area which effect on the port performance through system dynamics simulation. Result discovered reasonable rules could develop for enhancing continuity of container moves are effective arrangement in handling equipment and information sharing among stakeholders.


Electric Vehicle Diffusion in the Indonesian Automobile Market: A System Dynamics Modelling

Erwin Stefano LONAN, Romadhani ARDI

Recently, the increased of energy consumption of the land transportation sector and environmental pollution have rapidly promoted the development of Electric Vehicle (EV). Through Presidential Decree number 55 years 2019 who act as the umbrella policy, the Indonesian Government has shown its commitment to the acceleration of EV Industry. Successful policy for new entry industry depends heavily on scientific perspective to accurately predict impacts. This study developed a system dynamics model to analyze the EV adoption in Indonesia through scenario analysis to gain better understanding of the key factors effecting the early EV adoption. It is found that electric vehicle will have a fast growth in next decades and government policy support through subsidies in infrastructure development, manufacturing, and consumer purchase power are crucial to the mass adoption of EV.


Optimization by Hybridization of Algorithms RGA and ILS to Solve the Container Stacking Problem at Tripoli-Lebanon Seaport


The study in this paper mainly focuses on solving the problem of stacking incoming containers in the storage yard while taking into account several policies and constraints concerning the port of Tripoli Lebanon. A storage strategy is proposed in this paper, which is modeled mathematically with a mixed integer linear program for container stacking problem. As this problem is NP-Hard, large instances cannot be solved by Gurobi. We propose a heuristic hybridization approach between Randomized Greedy Algorithm (RGA) and Iterated Local Search (ILS) to tackle this problem. Numerical results on real size instances taken from the terminal port studied, show the efficiency of this hybridization for small and medium sized instances.

Big Data and Analytics 1
10:00 - 11:30

Chairs: Carman Ka Man LEE The Hong Kong Polytechnic University, Arnesh TELUKDARIE University of Johannesburg


The Prediction of Flight Delay: Big Data-driven Machine Learning Approach

Jiage HUO, Kin Lok KEUNG, Carman Ka Man LEE, Kam K.H. NG, K.C. LI

Nowadays, Hong Kong International Airport faces the issues of saturation and overload. The difficulties of selecting taxiways and reducing the lead time at the runway holding position are the severe consequences that appeared from increasing the number of passengers and increased cargo movement to Hong Kong International Airport but without constructing a new runway. This paper is primarily about predicting flight delays by using machine learning methodologies. The prediction results of several machine learning approaches are compared and analyzed thoroughly by using real data from the Hong Kong International Airport. The findings and recommendations from this paper are valuable to the aviation and insurance industries. Better planning of the airport system can be established through predicting flight delays.


TV Series Adaptations: An AI Toolkit For Success

Anjal AMIN, Landry DIGEON

Our project, born from a humanities scholar and an AI engineer, offers an unprecedented approach to analyze TV series adaptations in order to shed light on cultural practices and provide cues for more successful adaptions. We propose a method called the Multimodal Intercultural Matrix (MIM) model paired with an Artificial Intelligence toolkit called the Möbius Trip. The MIM provides a framework to reverse-engineer a show and to quantify the various elements of the episodes for cultural analysis and comparison. The Möbius Trip allows for the automatic and systematic analysis of the show's episodes by mining big data sets from the television programs. These data sets can recognize aspects of each episode, such as a character's gender, facial expressions, speech patterns, and camera work. Our approach not only provides representations of a culture and its members, but it also informs us on the media, the media industry, and on the TV series industry practices. Our results and expertise will influence the production industry and provide advice to film-makers.


Business Applications for Current Developments in Big Data Clustering: An Overview

Glendon HASS, Parker SIMON, Rasha KASHEF

The world's most valuable resource is no longer oil, but data" announces the headline of the May 6th, 2017 edition of The Economist; the digital revolution is here to stay. The primary currency of this movement is big data. The complexity of big data is defined as the relationships and how the data can be arranged with one another. Facebook has 30 billion pieces of unique information shared each month; this data's sheer size can cause an immeasurable amount of combinations for relational data. Analyzing this big data can reveal various useful insights for decision-makers. With the adoption of clustering analysis, patterns and hidden information can be developed from big raw data that can be used across many business problems and applications. In this paper, an overview of the state of the art of clustering analysis and its adoption in business applications in the era of big data is presented.


Visual-based People Counting and Profiling System for Use in Retail Data Analytics

Meygen CRUZ, Jefferson James KEH, Ramiel DETICIO, Carl Vincent TAN, John Anthony JOSE, EDWIN SYBINGCO, Elmer DADIOS

Data on various key performance indicators (KPIs) are crucial in preventing problems and growing a business. This paper focuses on the feasibility of gathering data on certain restaurant KPIs through an intelligent video analytics (IVA) system. The main challenge lies in maximizing the use of an existing CCTV camera with a fixed viewpoint, which is tailored for security purposes instead of video analytics, by using its footage in the IVA. The researchers partnered with a restaurant in a high-traffic business district to create and test the system. The final system gathered data on foot traffic, customer gender classification, and customer group size. Neural networks such as YOLO, Deep SORT, and InceptionV3 were employed in the implementation. The results show that while it is possible to gather data on these three metrics through the system, the speed and accuracy can still be improved through downsizing the frames, down sampling the videos, and using other algorithms.


Detecting Bursts in Water Distribution System via Penalized Functional Decomposition

Yinwei ZHANG, Kevin LANSEY, Jian LIU

Detecting bursts in water distribution systems, as anomalies from normal daily usage, is of critical importance for urban infrastructure maintenance. Existing methods based on conventional statistical process control fall short of accurate estimations of burst magnitude and starting time. This research combines a functional basis expansion of water flow data stream and a penalized decomposition to parameterize and estimate the normal water usage profile and detect spars burst with a comparatively small magnitude. The effectiveness of the proposed method is demonstrated by a high-fidelity simulation case study.


SARIMA and Artificial Neural Network Models for Forecasting Electricity Consumption of a Microgrid Based Educational Building

Meditya WASESA, Adhya Rare TIARA, Mochammad Agus AFRIANTO, Fakhri Ihsan RAMADHAN, Irsyad Nashirul HAQ, Justin PRADIPTA

We develop Seasonal Autoregressive Integrated Moving Average (SARIMA) and Artificial Neural Network (ANN) models for predicting one-month and one-day ahead electricity consumption of a microgrid based educational building. The prediction models can provide forecasts up to hourly accuracy. For this objective, we use more than two million records of electricity consumption data imported from the smart meter system of a six-floor microgrid based educational building. We use the Hyndman-Khandakar stepwise algorithm, which generates the (1, 0, 1)×(0, 1, 1)24 SARIMA prediction models. For the ANN prediction models, we use a thirty one-neurons input layer, a twenty-neurons hidden layer, and a single neuron output layer. The experiment results indicate that the ANN models produce more accurate and consistent predictions than the SARIMA models both in the one-month ahead and one-day ahead prediction contexts.

Project Management 1
10:00 - 11:30

Chairs: Songlin CHEN Nayang Technological University, Tahir MAHMOOD City University of Hong Kong


Breakthrough Capabilities for Delivering High-performing Project Management Offices (PMOs) in Construction Enterprises


The concept of the project management office (PMO) is well-established in academic literature. This organizational phenomenon has revolutionized practices applied by organizations toward coordinating and overseeing multiple projects throughout the design, engineering, initiation, execution, and handover stages. The construction industry is one of the contexts in which more research is still needed to provide practical guidelines for achieving effective PMO functioning. This study explores several core capabilities of these entities from the perspective of construction project management practitioners working in the contracting sector. In this regard, we solicited expert judgment based on an online questionnaire followed by thematic analysis. Respondents suggested six drivers that can contribute to improving the effectiveness of construction PMOs in practice. This study provides insight into some capabilities that can be employed for delivering high-performing PMOs.


How Could Antecedents to Project Collaborative Effectiveness Through Project War-Room Meetings, Develop Cohesion in Multicultural Teams: While Problem-Solving?


This study focused on using War-Room Meetings (WRM) as a vehicle to developing team cohesion in multicultural project teams in Oil and Gas civil, and marine infrastructure projects. Although the concept of collaboration and multicultural diversity is extensively viewed as organisational social structures, most theories of developing cohesion are sceptical about the social process within which it might occur. We explored the theory of combing daily WRM and problem-solving while strengthening the social-structures in diverse, multicultural-teams. We started by examining mainstream literature to develop conceptual theories. This study also used interviews and an Action Learning group to collect data and evaluate ideas into action. Our findings suggested, team building, problem-solving, multicultural-teams, language barriers, and networking could improve by using WRM. These concepts influenced ambidexterity which balances between exploitation and exploration; therefore, manipulating ideas and using flexibility to test a variety of shared beliefs.


Evaluation of the "Internet +" Environmental Public Welfare Project Based on Value Co-creation

Jie ZHANG, Yali ZHANG, Haixin ZHANG, Guanghui HE

The emerging "Internet +" environmental public welfare projects have been developing rapidly in recent years. However, there is no consensus reached on how to measure their effectiveness. From the perspective of value co-creation, this paper analyzes the value created by stakeholders of "Internet +" environmental public welfare projects in the whole life cycle of these projects, develops the evaluation index of project success, identifies the influencing factors based on "time-quality-cost", and designs the questionnaire containing 39 measurement items. On the ground of the exploratory factor analysis of 219 valid responses, the evaluation index system of the "Internet +" environmental public welfare project is established, including the preliminary preparation, implementation process, and subsequent influence. This set of indicators combines the traditional triple constraints, project stakeholders and project life cycle, and provides a reference for the development of "Internet +" environmental public welfare projects. The instrument is also useful for the evaluation research of public welfare projects participated by the whole people in the digital era.


Gearing Food Manufacturing Industry Towards Lean Six Sigma Implementation: An Exploratory Study on Readiness Factors


Lean Six Sigma (LSS) is a technique for business improvement that has flourished over the past decade due to its ability to minimise variations and increase efficiency, which enhanced the business performance and market credibility. However, LSS in the food industry was far away behind compared with other manufacturing settings. This paper focuses on the stage of LSS pre-implementation, with an intent to fill in the gap in the literature as to what type of competency is necessary to implement LSS in the food manufacturing settings. The objective of this study is to identify factors that influencing the readiness of LSS implementation in the food manufacturing industry. The semi-structured interviews were conducted with twelve food industry practitioners involved in quality management activities. Six LSS readiness factors are customised for the food manufacturing industry identified through 29 nodes. This study helps develop the LSS readiness framework and self-assessment tool, which can be a future reference for quality practitioners in the food industry.


Project Performance Evaluation System: A Case Study in a Construction Enterprise in Colombia

Sassha RICO, Luciana ALENCAR

Due to the increase of market competitors in different sectors, organizations currently seek new ways for continuous improvement. Companies initially focus on their internal processes that could captivate customers and stakeholders, mainly interested in the acquisition of products or services. To this extent, having processes that enhance differences from competitors is important to gain an advantage in the market. In this context, this paper aims to propose a methodology for evaluating the performance of projects, aligned with strategic organizational planning. The evaluation proposed in this paper is based on a literature review resulting in the application of the proposed methodology with a case study in a construction company in Colombia. The results demonstrate that the methodology developed in the company brought benefits in the studied areas; further allowing the identification and characterization of the critical processes that deserved greater attention, metrics, indicators, and those responsible, confirming better controls in the processes. After applying the methodology throughout the project's life cycle, the company relies on data that support decision making ensuring better use of resources for future projects.


Application of Fuzzy Set Theory to Evaluate Large Scale Transport Infrastructure Risk Assessment and Application of Best Practices for Risk Management


This study explores the factors that pose risks to the efficiency with which capital is utilized in large-scale transport infrastructure investments and suggests for risk management best practices that may be applied in relation to each. The literature review categorizes risks into nine major categories. Four practices that enhance capital usage efficiency are selected: namely, value engineering, asset portfolio management, life cycle costing, and quality assurance. Fuzzy set theory is applied to analyze and evaluate risks and the effect of adopting best practices in relation to them. By linking each risk to a best practice, potential enhancements to capital usage efficiency in infrastructure investment projects are observed. Survey responses of 83 engineers involved in land transport infrastructure projects in Sri Lanka form the dataset of this study. Results demonstrate that quality assurance reduces the planning fallacy and political influence, while value engineering reduces risks associated with technical know-how and change order. The study concludes that risks in large scale transport infrastructure investments may be managed through efficiency-enhancing practices to reduce.

Manufacturing Systems 1
12:00 - 13:30

Chairs: Hyeong Suk NA South Dakota School of Mines and Technology, Wen WANG Shanghai Jiao Tong University


Energy Management: Sustainable Approach Towards Industry 4.0

A. S. M. Monjurul HASAN, Andrea TRIANNI

Industry 4.0 concept is captivating the attention globally in a substantial rate that encompasses industrial digitalization with the means of advanced technical features. Maintaining the highest standard of industrial processes, Industry 4.0 demands to ensure energy efficiency also. Industries must ensure energy efficiency during the process flows, keeping in mind about the energy cost, carbon emission, and resource efficiency. Unfortunately, despite a significant potential for energy efficiency exists, that can be addressed by industries, implementing energy management practices. However, industries are still disinclined to take advantages of such opportunities. Research in this domain has little explored the potential relationships between energy management and Industry 4.0. In the paper, we aim at offering an overview of industrial energy management and related tools as well as Industry 4.0, preliminary discussing potential opportunities and synergies.


Critical Success Factors for Internet of Things (IoT) Implementation in Manufacturing Companies in Indonesia: Literature Review and Future Research

Inaki M. HAKIM, Moses L. SINGGIH, I. Ketut GUNARTA

The implementation of IoT considers several factors to determine whether a company can apply IoT or not. There are endless possibilities, and numerous companies have already started their IoT journey. The implementation of IoT encourages increased productivity and quality, mainly supported by the use of technology. The application of IoT is an effort to automate and digitize production process with a marked increase in connectivity, interaction and boundaries between people, machines, and other resources through information and communication technology. Some of manufacturing companies in Indonesia has implemented IoT, so the industry will grow and be more competitive. Some implementation challenges of the IoT are data security issues, specific human characteristics, robust IoT platforms, data management, the need for a high level of stability and reliability in system integration. This article aims to highlight some critical factors companies need to consider for a successful IoT implementation in Indonesia.


Digital Twin Application for Production Optimization


This paper is focused on the digital twin solution to validate the performance optimization of production lines. We propose three steps to approach the digital twin for production lines. As the first step, a simulation model is created to imitate real world behavior. After that, automation engineering is applied to communicate between real world and virtual model. A programmable logic controller (PLC) code is embedded as a communication language. Digital twin realization is shown by integration of simulation and automation. A case study presents how to bring practical benefits by applying the digital twin solution in production line virtual commissioning.


Adaptive Task Sharing in Human-Robot Interaction in Assembly

Christina SCHMIDBAUER, Sebastian SCHLUND, Tudor B. IONESCU, Bernd HADER

The ongoing development of collaborative robots (cobots) bears great potential for assembly processes by enabling new possibilities of task allocation between humans and machines. A detailed, empirically based evaluation of how cobots can live up to these possibilities in industrial practice is still missing in the manufacturing literature. In this paper some of the existing shortcomings -referred to as “the productivity gap” - in current assembly automation processes are discussed. To cope with this gap, adaptive human-cobot task sharing is proposed and implemented as a complementary task allocation model. Its potential to increase productivity and flexibility in new and existing assembly applications is evaluated.


Enterprise-wide Value Stream Mapping: From Dysfunctional Organization to Cross-Functional, Collaborative Learning and Improvement


A value stream is defined as the set of all the specific actions required to bring a product through the three critical management tasks of any business: the problem-solving task, the information management task, and the physical transformation task. However, a headlong rush into adopting lean tools and techniques on the shop floor has resulted in the improvement of the information management and physical transformation tasks only, and has led many organizations towards a state of static process optimization rather than one of sustained lean growth. In this paper, we draw on practical insights from a multiple-firm action research initiative in two companies to present an alternative method for value stream mapping that also incorporates the problem-solving task. This technique has allowed the organizations to achieve not only sustainable improvement in operational performance, but also significant growth in people productivity. What emerged was a product-centric approach to cross-functional learning and improvement, which has implications for both lean theory and practice.


Design and Development of a Hydrothermal Reactor for Bio Coal Production for Application in Solid Waste Management Technologies


This paper focuses on the design and development of a hydrothermal carbonization (HTC) reactor for processing biomass (brewers spent grains) to 69 tons per day of bio coal at 220 ℃ as the carbonizing temperature. A batch operation was used and a reactor with a height of 4.7m and a diameter of 3.1m. Stainless steel was chosen as the material of construction and a residence time of 2 hours in the HTC reactor for optimum conversion to bio coal. The HAZOP and process control was done to ensure health and safety in the plan.

Supply Chain Management 2
12:00 - 13:30

Chairs: Adnan HASSAN Universiti Teknologi Malaysia, Yugowati PRAHARSI Shipbuilding Institute of Polytechnic Surabaya


Optimal Planning of a Closed-loop Supply Chain with Recovery Options and Carbon Emission Considerations

Fareeduddin MOHAMMED, Adnan HASSAN

Climate change, increased carbon regulations, and globalized supply chains are driving industry practitioners and decision makers to implement various carbon policies to reduce carbon emissions. One of the effective approaches to mitigate carbon emissions is the implementation of closed-loop supply chain (CLSC). This paper proposes a deterministic mixed integer linear programming (MILP) model for a multi-period and multi-product closed loop supply chain network with multiple recovery, quality returns and carbon emission considerations. Transportation mode selection decision for logistic activities is also incorporated in the model. Results show that the model captures trade-offs between the total cost and carbon emission. Further, results suggest that carbon price directly effects on the total cost. Conversely, in carbon trading policy, due to having carbon buying and selling flexibility, both total cost and carbon emission are significantly reduced. Sensitivity analysis shows that the operational costs of various recovery activities impact on the total cost. This study provide evidence that besides achieving optimal closed-loop supply chain network design (CLSCND) and planning, it also reduces carbon emissions significantly without increasing the total cost.


Evaluation Framework in Sustainable Cold Chain for Perishable Food Products: A Literature Review

Shuo DANG, Dong LI

The global food system today faces a great challenge of feeding fast growth population and higher levels of service quality. The researches regrading sustainable cold chain for perishable food products are tremendously growing with increasing number of publications, but always conceptual and quite a few have addressed issues of sustainability. There is a need to understand how continuous improving of food security and other sustainable performance can be translated to support optimisation of food waste management, environmental impacts and cold chain structure designs. Content analysis base literature review of 136 selected research articles, published between 2010 to 2019, was used to identify the theory connection of food security and sustainable cold chain, and create an overview of the evaluation theory used for cold chain management, optimisation and decision-making. In this study, some of the key findings unveil (1) energy efficiency is alternative way to evaluate food industry; (2) circular economy is a sustainable solution to reduce food waste and increase energy efficiency across the cold chain.


Choosing the Right Communication Medium for Knowledge Transfer in Global Production Networks


Producing companies have been facing the challenge of increasingly changing worldwide market conditions. As inter-national presence has gained greater importance, many companies have spread their production network globally. To coordinate such rapidly growing structures, the systematic transfer of in-house production knowledge is essential. Thereby, one of the main influence factors on knowledge transfer is communication. Hence, a large potential lies within the optimization of cross-site communication. This paper presents a systematic approach for the optimized selection of a situation-specific suitable communication medium for knowledge transfer tasks in global production networks along distinct and rateable criteria. The approach is then validated by a survey.


Modeling the Recall Process of Bulk-Liquid Industry: A Linear Programming Approach


This article presents a decision-making model in food recall. Four options can be selected for treating recalled products: disposed, redirected for other use, downgraded, and reprocessed. The model will help stakeholders to decide how the recalled products should be allocated for each follow-up action to minimize the recall cost. The model has been tested on a real recall case in the edible oil industry. The model is proven to be able to find the optimal allocation for recalled products to produce a minimum recall cost. The sensitivity analysis shows that the recall cost can also be reduced by a product pricing strategy. Downgrading is the most favorable decision in the real case example.


Framework for the Proactive Identification of Adaptation Needs in the Configuration of Global Production Networks


Due to increasingly globally distributed value chains, companies today operate in global production networks. At the same time, the business environment is subject to growing volatility and uncertainty, forcing companies to constantly adapt. Changes are often detected too late, resulting in a delay in adapting the production network. This paper summarizes requirements for the proactive identification of adaptation needs in the configuration of global production networks and derives its key challenges. Furthermore, an overview of the most recent approaches is given and a framework for an efficient and user-friendly detection of adaptation needs is proposed.


Impact of Internal Factors on the Implementation of Halal Logistics


The purpose of this research is to examine the impact of internal factors on the implementation of halal logistics in the Indonesian Food and Beverage Industry. This research adopts the Partial Least Square (PLS) method to predict the dependent variable by involving a large number of independent variables. This research uses 100 food and beverage companies as the sample. The result of this research indicated that image and reputation, social responsibility, and integrity of halal has a significant impact on the implementation of halal logistics.

Decision Analysis and Methods 2
12:00 - 13:30

Chairs: Yaqiong LV Wuhan University of Technology, Vinay SINGH ABV-IIITM Gwalior


An Analysis of Decision to Retrofit Coal Based Power Plant with Carbon Capture Technology Having Uncertain Parameters

Shalini KUMARI, Sasadhar BERA

The increasing industrialization since its inception has drawn attention towards the impact of industrial activities on the global environment. The increasing concern of global warming and rising earth's temperature has driven the institution of the Paris Agreement to examine the threshold limit of emission of carbon dioxide, which is the major component of greenhouse gases (GHG). Carbon capture and storage (CCS) technologies play a vital role in achieving net-zero carbon emission. The motivation behind this paper is to review the execution of CCS innovation in thermal power plants with the help of a mixed-integer nonlinear programming model. The problem of uncertainty of emission information in the thermal power plant is solved using the fuzzy technique. The results presented here demonstrate the option of selection of technology in a coal-fired power plant.


Prediction of Raw Material Price Using Autoregressive Integrated Moving Average


In a highly competitive manufacturing industry, it is necessary to reduce logistics cost for remaining competitiveness and increasing business profitability. One of several causes primarily influencing logistics cost is inventory to support fluctuation of raw material price and decision makers when and how much raw material is purchased. These hence require time-series prediction of raw material price. For a small-sized manufacturing case, its main raw material of copper is predicted using Autoregressive Integrated Moving Average (ARIMA). It returns Mean Absolute Percentage Error (MAPE) less than 5 percent.


Energy Efficiency Measures and Production Resources: Towards an Integrative Classification Framework for Decision Makers


The adoption of energy efficiency measures (EEMs) is a significant area of concern for today’s industrial organisations. Whilst literature on this subject has soared in recent decades, there remains a gap in understanding the extent of their impact on an organisation’s operations as well as the manner, and whether, they are adopted in the first place. This paper provides a preliminary attempt at addressing these concerns by investing attention into the notion of production resources as a mechanism through which a deeper appreciation of EEM impact on operations could be provided through the development of a generalised decision-making framework. We end this paper with conclusions and areas for further work.


A Game-theory Based Parking Pricing Policy

Shijin WANG, Ting HU

With the rapid increase of vehicles in China, traffic jams and parking problem have been becoming the major concern of the urban cities. A large number of studies have shown that parking fees, as economic leverage, can play a vital role in regulating the dynamic traffic demands. This paper proposes a pricing model that applies the principal-agent theory in parking pricing problem. A real-world case of a public parking lot in Shanghai is presented to show the reasonability and applicability of the policy.


Least-distance Data Envelopment Analysis Model for Bankruptcy-based Performance Assessment


In this paper, the use of the Data envelopment analysis (DEA) as a quick-and-easy approach for bankruptcy-based performance assessment is presented. The attractive advantage of DEA is that it can provide an efficient target (improvement goal) for inefficient decision-making units (DMUs). The DMUs under evaluation are divided into two groups: efficient and inefficient, regarding cases of bankruptcy analysis, they are divided into nondefault firms and default firms. Moreover, the least-distance (LD)- DEA model has been actively researched and applied, because it can provide the closest efficient target that is achievable with the least effort. Thus, using the LD-DEA model for bankruptcy based performance assessment can give an early warning of a firm’s financial performance and provide an improvement goal that can be easily achieved for default firms. As a case study, we demonstrate this approach using financial data of 61 Japanese banks. From the results, we find that our approach provides an improvement goal that can be achieved with fewer total modifications of inputs and outputs compared with that provided by slacks-based measure (SBM) model.


Experimental Study on Visual Variables Influencing Icon Similarity

Shiyuan DING, Haiyan WANG, Chengqi XUE

The study on icon similarity can provide a powerful reference for designing more unique and highly recognizable icons, to help people interact with machines better with the help of a graphical user interface. This paper extracted a series of important icon visual variables and studied their influence on icon similarity judgment. Through the experiment of icons classification tasks, the strategy adopted by subjects to classify similar icons was obtained, and it was found that the subjects gave priority to the visual variables of the background and then paid attention to the visual variables of the icon body in the classification process. Through the comparison behavior experiment, the order of the four visual variables of icon body whose influence degree on icon similarity judgment was obtained. Combined with eye movement index of the subjects during the comparison experiment, it is concluded that the icons with high similarity receive more attention during the comparison. The results are of great significance for research on similarity recognition of icons.

Reliability and Maintenance Engineering 1
12:00 - 13:30

Chairs: Danping LIN Shanghai Maritime University, Xin WANG City University of Hong Kong


Reliability and Safety Assessment of Automated Driving Systems: Review and Preview

Kuo-Wei WU, Chung-Chih LIAO, Wen-Fang WU

In 2018, SAE International released a revised version of ADS (Automated Driving System) classification standard—SAE J3016 and divided it into six different classification levels. Many people doubt about the reliability of AV (Autonomous Vehicles with ADS), ADS, and HAD (Highly Automated Driving). They may wonder if and when the public is ready to enter different SAE levels. To illustrate and resolve some of their questions, this paper divides “ADS reliability and safety” into the following four segments: (1) AV hardware reliability, (2) HAD reliability, (3) Integration reliability by road tests, and (4) resilience & CPS (Cyber-Physical System) reliability. The paper tries to answer the following RQs. RQ1: Are the reliability and safety of vehicle hardware sufficient for the current SAE level 0-2 vehicles? RQ2: Are HAD’s decisions reliable in all current workable scenarios? RQ3: Is the failure rate of ADS significantly better than that of human-driven vehicles in public testing? RQ4: Do human drivers have to intervene or participate in while driving AVs?


An Integrated Approach for Fuzzy Failure Mode and Effect Analysis Using Fuzzy AHP and Fuzzy MARCOS

Soumava BORAL, Sanjay K. CHATURVEDI, Ian HOWARD, Kristoffer MCKEE, V. N. Achuta NAIKAN

Failure mode and effects analysis (FMEA) is a potential risk evaluation tool in reliability engineering. The RPN based FMEA approach has been criticized for its multiple drawbacks. Recently, the Multi-Criteria based Decision Making (MCDM) methods have proven their efficiency to overcome the limitations of RPN based FMEA approach. This work develops an integrated fuzzy MCDM FMEA approach by employing Buckley’s Fuzzy Analytical Hierarchy Process (FAHP) and modified Fuzzy Measurement of Alternatives and Ranking according to Compromise Solution (FMARCOS). Both FAHP and modified FMARCOS methods are capable to deal with the linguistic evaluations made by multiple experts and to arrive at a rational decision for risk ranking of the failure modes. A benchmark FMEA case is utilized to feature the superiority and robustness of the suggested approach. Finally, sensitivity analysis is done and the found risk ranking results are contrasted with other popular fuzzy MCDM approaches.


Mechanization of Qualitative Risk Based Inspection Analysis


The need for enhancing Risk-Based Inspection (RBI) strategies has received significant attraction of many researchers and practitioners in the offshore/onshore oil and gas. Qualitative RBI (QRBI) has many applications in risk assessment of the aging assets, screening of the asset based on their risk level, and also in full risk assessment analysis of the items in the absence of proven quantitative RBI procedure. Traditionally, Subject Matter Engineers (SMEs) perform qualitative RBI and so the procedure is vulnerable to human biases and errors. Unreliability also causes due to the performer-to-performer output variation. Mechanization of the QRBI process improves the quality of the analysis by reducing the effects of human biases, enhancing the accuracy and speed of the calculations and increasing the repeatability. This manuscript first discusses the evolution of the QRBI process and presents recent trends in mechanization of the QRBI process. Then, the application of Gray Relational Analysis (GRA) method in mechanizing of the QRBI process is presented. In order to validate the results from GRA based QRBI, they compared by the results obtained from commercial software of RBLX.


Modelling and Analysis of Accelerated Degradation Testing with Practical Issues Considered

Qingpei HU

In this talk, the modeling and analysis for accelerated degradation testing will be explored, with considerations on the practical issues. Firstly, due to the inconsistency of manufacturing, the performance of samples under testing will vary and the degradation process should incorporate the initial status into the modeling framework. Secondly, under some circumstances, the measurement for the testing samples of ADT can only be conducted under normal stress levels. Then performance recovery may occur for some failure modes. Recovery effects should be incorporated into the degradation process when modeling for accurate reliability evaluation. Thirdly, for some complex failure mode like a sequential hard and soft failure, the degradation starts after some event happens which can be modeled by some lifetime model. For such a case, a lifetime delayed degradation process should be considered. Practical case studies would be presented for each circumstance.


A Deterministic Analysis Method of Embedded System Based on Event-driven

Xianchen SHI, Yian ZHU, Xiangyu ZHANG, Lian LI, Zonglong QI, Jihuan DOU

This paper elaborates on the deterministic analysis method for event-driven embedded system to reduce the impact of uncertain factors at the design stage. By abstracting each unit of the embedded system, the framework of the embedded system is built. Margin analysis of time resource and space resource is carried out to provide accurate reference for the designers and realize reasonable planning of resources, task priority and task scheduling.


Life Prediction of Self-Locking Nut for Aeroengine Based on Survival Analysis and Bayesian Network

Zhiqiang CAI, Yuhang WANG, Huiying CAO, Zhengjie TIAN

The self-locking nut is an advanced fastening part, which can be locked by friction. It has been widely used in the connection of aeroengine system nowadays. The performance and reliability of self-locking nut will directly affect the tightness of various aeroengine components, and play an important role in the operation of aeroengine. In this paper, based on the collected lifetime data of aeroengine self-locking nut, the survival analysis method is introduced to discover the influence of each individual variable on the life of self-locking nut at first. Then, after eliminating the irrelevant variables according to survival analysis, a novel Bayesian network (BN) based life prediction model is established to support the decision making of extending service life and improving system reliability. Finally, by comparing with the general BN model with all variables, the performance of the proposed model is verified, and the applications of the model are also given.

Service Innovation and Management 1
14:30 - 16:00

Chairs: Nagesh SHUKLA University of Technology Sydney, Annapoornima M SUBRAMANIAN National University of Singapore


Organizational Learning from Failure Can Augment Ambidexterity: Evidence from Japan


It is important for companies to adapt to changes in the business environment to gain or maintain a sustainable competitive advantage. In other words, they need to augment their management base by upgrading their current businesses and finding new business opportunities in line with future corporate growth and life cycles. However, it is difficult to achieve these objectives concurrently. In this study, we present a case of a Japanese company that has grown as a measure of information sharing and organizational learning from failure. We aim to explain how organizational learning from failure has led to ambidexterity, which consists of exploration and exploitation; we also examine whether organizational learning from failure is effective in exploration and exploitation. We used a quantitative approach through surveys and analysis of covariance structure. As a result, we find that organizational learning from failure is more effective for exploitation than for exploration in this company.


The Business Ecosystem Management Canvas


Increasing digitalization and the associated servitization lead to changes in user behavior and understanding of benefits. Customers are asking for faster adjustments to the value proposition and a higher degree of individualization. Companies in the mechanical and plant engineering sector are forced to develop innovative end-to-end solutions, so-called product service systems based on new business models. The integration of new types of competencies and resources is essential for this. Dynamic, cross-company systems are emerging as a new organizational form of economic activity, so-called business ecosystems. It becomes clear that there is an increased need for control and coordination, the business ecosystem management. The aim of the paper is to identify design fields and activities by means of inductive category building based on a literature research and expert workshops as well as the formation of the Business Ecosystem Management Canvas. The canvas is designed as an orientation guide and supports the responsible actors in the orchestration of business ecosystems, especially in their emergence and diversification phase. This was validated using a real case example.


Lean Management 4.0 Proposition for the Evolution of Managerial Criteria


At a time when industry is experiencing its 4th revolution, the question of the evolution of the organization is raised and with it the role of operational excellence in this digital transformation. Lean management for the most part implies manufacturing organization and we can legitimately question ourselves on its relation to the concepts of digitalization. Our aim here is to methodologically analyze the managerial concepts of lean management and highlight them in relation to Industry 4.0. After reviewing the literature and the method laid out, we will evaluate the managerial concepts, individually confronting them with our operational experience and then propose a reflection on western 4.0 lean management, with two distinct choices and points of comparison. The article will conclude with perspectives for additional research.


Business Model and Organization – Interdependencies for Customer-Centric Continuous Innovation in Subscription Business


Subscription business models are increasingly gaining attention in the manufacturing industry. By consequently focusing on solving the customers’ problems instead of focusing on products, machine manufacturers may establish a long-term relationship with their customers and gain a stronger competitive position. In order to be successful, the business model needs to include the ability for continuous innovation and thus steadily increase the customers’ benefits. At the same time, the introduction of a new business model comes along with a complex set of implications for the provider’s organization. Literature suggests that mutual dependencies between the provider’s business model, its organization and customers exist. Yet no management model so far is able to illustrate the complexity of these interdependencies in subscription models. The presented work intends to do so by including both the direct interdependencies between business models and organizations as well as indirect interdependencies which are caused by continuous innovation of offers and customer interactions. The model serves manufacturers of the machinery and equipment industry as an organizational guideline in managing subscription business models.


Measuring Information Technology Service Levels: A Literature Review

Franziska SCHORR, Lars HVAM

Due to their growing dependency on information technology (IT), firms have realised the need for IT services that optimally support the firms’ business processes. To provide optimal service performance, the firms’ IT departments must continuously manage IT service levels using suitable metrics. As IT services consist of a combination of technology, people, and processes, defining appropriate service level metrics has been a complex problem to solve for both researchers and IT managers. Using a systematic literature approach, we investigate the metrics that IT departments should utilise to measure IT service levels. This analysis suggests that service availability and responsiveness metrics are the most commonly employed metrics for approximating the IT service level. In the literature, we observe the need and trend to measure the impact of IT service levels on business performance and user satisfaction.


A User-centered Evaluation and Redesign Approach for E-Government APP

Danni CHANG, Fan LI, Leni HUANG

Nowadays, electronic government (E-gov) plays an increasingly important role to provide policy information and offer government services. Hence, the quality of E-gov services and the public experience with the E-gov applications have received increasing attention from both the government and society. This work is thusly intended to study the design and management of E-gov Apps. Particularly, the user-centered perspective is integrated to evaluate and improve the public experience of E-gov App. A comparative analysis of E-gov App design between China and other countries was presented, in which the existing design problems, especially in Shanghai government Apps, have been identified. Taking the E-gov App of Shanghai Minhang District as a practical case, a user-centered evaluation approach combining user survey and user experiments was deployed to examine the design problems of Minhang App. Based on the pain points in usability and aesthetics identified, an improved App design was generated. To validate the design quality of the new App, user tests were conducted, and it showed that the improved design can achieve better performance.

Technology and Knowledge Management 2
14:30 - 16:00

Chairs: Kah Hin CHAI National University of Singapore, Leif OLSSON Mid Sweden University


Cyber-Physical Operator Assistance Systems in Industry: Cross-Hierarchical Perspectives on Augmenting Human Abilities

Mirco MOENCKS, Elisa ROTH, Thomas BOHNÉ

In production systems, manual tasks need to be considered more than the sum of repetitive sub-tasks which can simply be taken over by autonomous systems. Despite technological advances in automation, the presence of human operators remains essential on future shop floors. Consequently, it is of interest for manufacturing organizations how cyber-physical operator assistance systems (C.O.A.S.) can augment skills of operators on the shop floor. However, there is a limited understanding of how relevant stakeholders in manufacturing organizations assess the suitability of COAS. This is crucial in so far as the adoption of COAS significantly depends on the approval of stakeholders throughout the respective manufacturing organization. This paper explores how stakeholders in manufacturing organizations assess the role of COAS on future shop floors. This is realized by conducting an exploratory, multi-method, qualitative study encompassing interviews of executives, instructors, and operators. Additionally, the study incorporates ethnographic observations in industrial education. A result of the study is that informants expect COAS to be promising for manufacturing organizations if systems augment cognitive abilities of operators, rather than their physical abilities.


Case Study for the Integrated Development of a Modular System for Vehicle Superstructures of Battery Electric Light Commercial Vehicles


Considering fossil fuel scarcity, future transportation is a core challenge for industry and society. With increasing urbanization, the issue of emission-free transportation is of particular relevance for metropolitan areas. To reduce pollution, environmental zones have been set up in numerous German cities. These affect small and medium-sized companies (SMEs) that operate within the city centers, but whose logistics are diesel-based. At the same time, battery electric light commercial vehicles (BELCVs) are more expensive than diesel vehicles. Apart from component costs, this is partly due to lower volumes and limited economies of scale. In the context of vehicle structures in BELCVs, new possibilities for installation space design arise. This paper aims to modularize this installation space tailored to SMEs’ needs. Since purchasing is the primary cost driver for SMEs, the presented case study pursues the goal of realizing "car-sharing" for BELCVs via modularization, so that capital investments can be distributed among several parties. Thus, the objectives of the present paper are sharing the lessons learned of the case study and contributing to the validation of the modular system design methodology used.


The Enhancement of E-learning for the Boring Process to Leverage the Knowledge Management Maturity

Zulma Luklu Il MAQNUN, Fadel MUHAMMAD, Amelia KURNIAWATI, Mochamad Teguh KURNIAWAN

To catch up with the dynamics of business needs, organizations need to maintain and regularly enhance the performance of their information system and human capital. An aerospace industry company is expected to have a sustainable implementation of knowledge-based e-learning. The company needed to enhance the e-learning that covers the entire content of the boring process. This study aimed to design the enhancement of e-learning of the boring process and to review the company’s KM maturity. The knowledge conversion process retrieved thirty-one tacit knowledge and converted them as part of best practice and the knowledge content of e-learning. Afterward, the software development process was commenced to develop the adjustment of the e-learning structure and new features required. The review showed e-learning has contributed to the company’s KM maturity on level 3 – Aware. By evaluating the KM maturity, the company can determine the initiatives improvements needed to leverage the KM practice.


The Impact of Business Intelligence on Decision-Making in Public Organisations

Aron BERHANE, Mohamad NABEEL, Christine GROßE

This study investigates how business intelligence (BI) affects decision-making processes and the basis for decisions. Therefore, the inquiry includes literature from the field of BI and interviews with three Swedish agencies. It concentrates specifically on three fundamentals of BI-driven decision-making: data quality, data analysis and the human factor. The results emphasise BI’s impact on decision-making and interrelated processes. Although BI does not reduce the volume of decisions, it enables a decision-maker or organisation to control and monitor the decision basis, which suggests that decision quality increases if the decision concerns issues that rest on statistics and facts. Based on theoretical and empirical findings, this paper contributes to an increased understanding of the impact of BI on decision-making at Swedish agencies.


Development and Evaluation of a Blockchain Concept for Production Planning and Control in the Semiconductor Industry


Blockchain technology promises several benefits for operations management, but reported use cases, concepts and applications are rare. This contribution indicates the findings from a case study of analyzing the suitability of blockchain technology for semiconductor manufacturing at the 300 mm wafer frontend facility of Infineon Technologies in Dresden, Germany. This plant contains one of the most highly automated fabrication lines in the industry. Here, several different and isolated digital software tools are used to control manufacturing, all accessing centralized databases with different views and restrictions. We assess if and how a blockchain solution could and should be designed to enhance the analytic capabilities for this facility using the design science approach and evaluate the economic value of the designed application using the analytical hierarchy process.


Methodology for the Assessment of Complexity in Corporate Value Networks

Michael RIESENER, Christian DÖLLE, Julian KREß, G. SCHUH

Nowadays, companies are forming corporate value networks to meet the increasing demands of customers and to implement new business models for the generation of new revenue streams. Nevertheless, managing a larger number of partnerships can often be challenging for companies. In this context, methods are required to manage network complexity. The literature neglects the holistic assessment of complexity in corporate value networks. In this paper, a methodology to assess complexity in corporate value networks within the mechanical and plant engineering industry is proposed. Various complexity drivers contributing to overall network complexity have been identified from the scientific literature and quantified using a verbalized ordinal scale. Different complexity dimensions are operationalized and visualized by the means of complexity vectors, which consist of the assessed complexity drivers. The proposed methodology enables the user to derive holistic measures in order to deal with complexity in corporate value networks.

Information Processing and Engineering 1
14:30 - 16:00

Chairs: Gabriel FUENTES Centre for Applied Research at NHH, Seung Ki MOON Nanyang Technological University


Research on Layout Design of Situation Interface Based on Comprehensive Importance Evaluation of Nodes

Hao WU, Haiyan WANG, Xiaojiao CHEN, Chengqi XUE

To optimize the layout design of the situation interface, this paper proposes a layout design method of situation interface based on comprehensive importance evaluation of nodes. Taking the AIS information service platform as an example, this paper first analyzes the typical tasks of the interface, and construct an information graphic cell network according to the association between the task and the interface information graphic cells; then build a node comprehensive importance evaluation model and classify the node importance levels; Finally, Re-layout the information graphic cells of different importance levels according to the interface layout insights and use CogTool to compare the performance of the typical tasks of the original interface and the redesigned interface, the results show that: the layout design of interface information elements based on the evaluation of the comprehensive importance of nodes can effectively improve the user's task operation efficiency. The research results can provide a reference for the layout optimization of the information graphic cells of the situation interface.


Exploring AI-Driven Business Models: Conceptualization and Expectations in the Machinery Industry


Companies collecting and creating valuable insights with artificial intelligence (AI) are able to secure a decisive competitive advantage. Business model innovation serves as a strong competitive differentiator and together with technological progress, new businesses fueled by AI are created every year. The main scientific result of this research is the conceptualization of AI-driven business models and an overview about the target state and potentials of these business models in practice based on a descriptive study. AI-driven business models can be conceptualized as business models that use artificial intelligence technologies to build at least one of the business model components. Compared to data-driven business models, which constitute a subgroup of business models, AI-driven business models are based on a set of techniques, that learn and improve their performance without humans having to explicitly program them. The contribution to practice is made as the proposed potentials of developing these business models can support decision-makers in evaluating the use of AI technologies. Innovating its business model with the help of AI can help to survive in a highly competitive business environment.


A Pilot Study of Industry 4.0 Asset Interoperability Challenges in an Industry 4.0 Laboratory

Sune Chung JEPSEN, Thomas Ingemann MØRK, Jakob HVIID, Torben WORM

System integration is a crucial concept in the Industry 4.0 (I4.0) vision, where information processes supporting flexible production are digital. System integration paves the way for leveraging the Industrial Internet of Things, big data analysis, simulation, cloud computing, and augmented reality. The first step towards system integration is to examine the assets (machine software) ability to exchange information in an I4.0 setting. This paper aims to analyze challenges for asset interoperability by conducting asset integration in the University I4.0 laboratory (I4.0 lab). Conducting asset integration has been a part of building an Information Backbone (IB) as a minimum viable product in the I4.0 lab. An IB is a software infrastructure that involves integrating into various assets, e.g., warehouse, transport, and robotic systems, and providing communication among them. The pilot study reveals that the maturity of assets interoperability readiness are at very different levels, e.g. missing external interfaces, poor documentation, and varying technologies. These challenges need to be further addressed to collect architectural requirements for system integration, and establish a common vocabulary and understanding of I4.0 concepts.


Addressing Supply Chain Vulnerability by Supporting Emerging IT: An Analysis Based on SCOR Framework

Mengdi WU, Zhaojun YANG, Jun SUN, Xueping GONG

Due to the uncertainty of the external environment and the counter-globalization trend caused by the COVID-19 pandemic, supply chain vulnerability has become a prominent problem. Under this background, the development of emerging information technology (IT) provides a new means for enterprises to deal with the situation. A review of the existing literature shows that emerging IT can effectively improve the ability of enterprises to respond to supply chain risks. Based on the supply chain operation reference (SCOR) framework, this paper summarizes the factors pertaining to supply chain vulnerability. It then identifies five relevant emerging technologies: big data analysis (BDA), Internet of Things (IoT), blockchain (BC), radio frequency identification (RFID), additive manufacturing (3D printing). Their functional characteristics help alleviate the related vulnerabilities in the entire SCOR procedure of a supply chain. The analysis provides useful insights for enterprise managers to mitigate supply chain vulnerability with emerging IT technologies.


A Customized Smart Medical Mask For Healthcare Personnel

Noori KIM, Joslyn Jun Wei LIM, John Jie Ming YING, Haining ZHANG, Seung Ki MOON, Joonphil CHOI

A medical mask is one of the vital Personal Protective Equipment (PPE) used by Healthcare Personnel (HCP) to protect themselves, patients, and others while providing their essential services to the public. HCPs all around the world are fighting on the frontlines of COVID-19. While they are saving people’s lives from Coronavirus, it is also critical to monitor the HCPs’ health conditions continuously. In this study, we propose a framework to develop a customized smart medical mask system to monitor the HCP’s temperature and strain on the face. Aerosol Jet Printing (AJP) technology is applied to develop the mask that embeds 3D printed sensors with wireless function. The proposed design process utilizes a 3D scanned picture of an individual face, then analyzing its geometrical attributes to determine the adjusted places for the sensors on the mask and optimize the design paramenters of the sensors. The two types of sensors, temperature, and strain are fabricated using the AJP technology. The temperature monitoring is to detect respiratory breathing fever and irregular, which is one of the symptoms of respiratory diseases. And strain monitoring is for alarming possible face irritation and bruising caused by tight sealing of masks. The sensing data is transmitted to the cloud for real-time monitoring purposes. This paper showcases the customized yet affordable additive manufacturing built on the Internet of Things technology for a personalized healthcare application to alarm workload and body condition of HCP.


A Spatial Framework for Extracting Suez Canal Transit Information from AIS


The Suez Canal is one of the world’s most important maritime routes, as shown by the almost 19,000 transits made every year. Despite its importance to seaborne trade, few statistics about the operation are available. This paper outlines a method to generate transit information from the matching of Automatic Identification System (AIS) ship tracking data and the modeled spatial environment of the Suez Canal. Additionally, important features such as the waiting time at anchor and the access routes to the Canal are extracted from adjustments to Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. The algorithm is designed to be deployed in a distributed setting for the handling of big data sets.

Production Planning and Control 1
14:30 - 16:00

Chairs: Songlin CHEN Nayang Technological University, Aries SUSANTY Diponegoro University Indonesia


Multi-factory Job Shop Scheduling With Due Date Objective


The existing literature on distributed scheduling mainly focuses on the performance measures makespan, total completion time, or costs. Due date related objectives that are gaining in importance in the industry have not been considered to a similar extent. In this contribution, we present a model formulation of the distributed job shop scheduling problem with due date consideration and present adapted greedy heuristics as well as a genetic algorithm to solve large problem instances. Computational experiments are carried out to assess the performance of the model and the algorithms. The heuristics and metaheuristics show promising results in reasonable computation times, with the genetic algorithm outperforming the other heuristics. The results indicate that managers should consider incorporating due date related objectives in the decision-making process of production planning and scheduling in distributed manufacturing.


Risk Assessment and Treatment Planning for Energy-flexible Production Systems Using an Additional Cost Model

Stefan ROTH, Markus WEBER, Andrea HOHMANN, Gunther REINHART

As a result of the energy transition in Germany, companies can increasingly benefit from adjusting electricity consumption to fluctuating electricity prices. Through energy-oriented production planning, production processes are scheduled depending on price forecasts. The resulting electricity demand is procured with the obligation to consume the electricity within certain tolerances. The procurement entails risks, as faults or other unexpected events can lead to deviations in electricity consumption. These can be associated with high penalty costs, if contractual tolerances or the peak load are violated. To take a holistic view on the additional costs, the effects on production goals must also be considered. This paper shows the assessment of production risks and delivers an approach for suitable preventive or reactive measures by changing the production plan. The approach was applied on a use case of a foundry with exemplary risks, with the result of minimized fault-related additional costs through the selection of a cost-optimal measure.


Automated Data Acquisition and Processing for Factory Layout Planning

Dominik MELCHER, Benjamin KÜSTER, Ludger OVERMEYER

Factory planning is an important tool for manufacturing companies to raise their efficiency and to maintain their competitiveness by changing market or customer requirements. A special challenge is the acquisition of layout data and the processing of this data in suitable planning tools. Current approaches still measure manually or have to transfer acquired data from laser scanners by hand into planning tools, which leads to a high effort and error proneness. This paper presents a holistic concept for automated and systematic data acquisition and processing for factory planning processes.


Planning of Available Resources Considering Ergonomics Under Deterministic Highly Variable Demand


In this paper, a method for hybrid short- to long-term planning of available resources for operations is presented, which is based on a known or deterministically forecasted but highly variable demand. The method considers quantitative measures such as the performance and the availability of resources, ergonomically relevant KPI and ultimately process costs in order to serve as a pragmatic planning tool for operations managers in SMEs. Specifically, the method enables exploiting the ergonomic advantages of available flexible automation technology (e.g. AGVs or picking robots), while assuring that these do not represent a capacity bottleneck. After presenting the method along with the necessary assumptions, mainly concerning the availability of data for the calculations, we report a case study that quantifies the impact of throughput variability on the selection of different process alternatives, where different teams of resources are used.


Why a Systematic Investigation of Production Planning and Control Procedures is Needed for the Target-oriented Configuration of PPC

Alexander MÜTZE, Simon HILLNHAGEN, Philipp SCHÄFERS, Matthias SCHMIDT, Peter NYHUIS

The target-oriented configuration of production planning and control (PPC) confronts companies with major challenges. While there are already many publications dealing with the effect of specific procedures of PPC tasks on the logistic objectives, there is still a lack of a framework allowing a comprehensive and relatively simple examination of the target conformity of PPC configuration on the level of procedures. Furthermore, such a framework would thereby also enable companies to position their PPC among conflicting production and logistic objectives. This contribution presents the current state of knowledge on target-oriented and holistic PPC configuration and points out why a systematic investigation of the generally valid interdependencies of PPC procedures with each other as well as the impact on the most important objectives is necessary. Further, it is outlined which future research activities are planned and with a demonstrative example illustrated how complex cause-effect relationships within PPC on the level of procedures arise.


Reverse Logistics with Disassembly, Assembly, Repair and Substitution


A reverse logistics planning problem is modeled and analyzed. The model considers returns of a particular electronic device from customers. Some of the collected products are remanufactured or refurbished. Others are disassembled for their key parts which can be considered as good as new. New products are assembled either using new parts or extracted ones. There are two types dynamic demands: demands for remanufactured/refurbished products and demands for new products. Demand of remanufactured/refurbished products can be satisfied using new products in case of shortage. This is a one way downward substitution. The objective is to minimize total costs while satisfying all demands. This problem is formulated as a MILP. The numerical results show that: i) it is hard for a solver to find optimal solutions for the problem in reasonable computational times for several instances with relatively small time horizons and ii) substitution is justified for a certain range of cost and demand parameters.

Systems Modeling and Simulation 2
16:30 - 18:00

Chairs: Szu Hui NG National Univeristy of Singapore, Nagesh SHUKLA University of Technology Sydney


Cloud-based Cyber-Physical Robotic Mobile Fulfillment Systems Considering Order Correlation Pattern

Kin Lok KEUNG, Carman Ka Man LEE, Ping JI, Jiage HUO

Ordering picking is the most time- and cost-consuming operation in the Robotic Mobile Fulfillment System (RMFS) and affects the entire supply chain operation efficiency and effectiveness. With the aid of digital operations, Cyber-Physical Systems (CPS) provide a nearly real-time control and response in the virtualized environment, thereby conducting a virtual prototype for near real-time simulation and prediction. The research presented in this paper explores the application of CPS in RMFS, considering the order correlation pattern. Four algorithms: Apriori algorithm, Frequent Pattern Growth algorithm, ECLAT algorithm and k-modes algorithm are introduced to reduce robotic conflicts of robots and enhance the capacity management in RMFS. The total completion time based on frequent itemset assignment is less than that based on random storage assignment. However, the dock grid conflicts are increased because the most frequent items are concentrated in a particular area.


A Simulation-based Method for Predicting the Time-varying Passenger Demand at Metro Rail Transit Line 3 Using Monte Carlo Simulation


Simulation methods consider the inherent randomness and uncertainty in modeling a system. The purpose of the current study was to provide a practical approach to estimate and predict the monthly inbound passenger flow of the Manila Metro Rail Transit Line 3 (MRT-3) by utilizing the Monte Carlo Simulation (MCS) method. MCS was used to generate random numbers from this distribution and was able to produce a reasonably forecasted model. By using the monthly passenger data, it was found that Logistic distribution had the best fit to estimate the given data. The results derived in this study could provide valuable insights, particularly on capacity planning, which can be used to improve the efficiency and sustainability of the existing rail system of MRT-3.


Simulation Modeling of Production System Considering Human Behavior

Fansen KONG, Xiangdong KONG, Haoyang WU, Yixin ZHANG, Peidi FANG

With the advent of industry 4.0, the manufacturing and assembly industry is increasingly pursuing digitization and automation, but it is still inseparable from human participation. People play an important role in the manufacturing and assembly industry. In the academic literature on human-machine research, more attention has been paid to human-machine interaction, and little attention has been paid to the impact of human behavior on the production system. The purpose of this paper is to consider the impact of time pressure on operating workers from the point of view of operating workers, and then to explore the impact of operating workers on the performance of the production line. For this reason, the theoretical operators of fuzzy mathematics are assisted to quantify the time pressure from two aspects. Then, the human behavior model is embedded into the production system for simulation, and the necessary data are obtained for analysis. The results show that the time pressure has a significant effect on the working efficiency.


Towards a European Hyperloop Network: An Alternative to Air and Rail Passenger Travel

Deep VIjay MERCHANT, Stanislav CHANKOV

Hyperloop is a novel transportation mode that can transform the way we travel. While entrepreneurs are working on turning this disruptive concept into reality, there is a lack of research on the Hyperloop route networks. Effective Hyperloop routes are essential for this concept as once built, they cannot be altered. The objective of this paper is to develop Hyperloop network routes for Europe and investigate their potential for passenger travel as an alternative transport mode to rail and air travel. The paper proposes two potential networks with different Hyperloop routes. An initial simulation study is conducted with four basic scenarios derived by varying the number of passengers (low and high) and the purpose of travel (using Hyperloop as an alternative to air or rail travel). The evaluation of the Hyperloop networks depends on four key performance indicators: (1) average travel time, (2) utilization, (3) passenger service level, and (4) revenue generation. The findings indicate that both networks have potential for the European passenger market, but their success depends on the behavior of the passengers using Hyperloop.


A Cross-disciplinary Model-Based Systems Engineering Workflow of Automated Production Systems Leveraging Socio-technical Aspects


With the increasing integration of multiple disciplines, Model-Based Systems Engineering (MBSE) has become more beneficial for the development of automated production systems. However, even though discipline-specific models can be connected via a system model, how to apply MBSE in a cross-disciplinary context in the industry remains challenging due to complex organization, inefficient communication and limited knowledge of the staff. In this paper, a workflow of automated production system development combining MBSE, sociology and team collaboration is proposed and leveraged against the traditional workflow. BPMN+I is applied for modeling cross-disciplinary collaborations and assessing team and organizational factors.

Project Management 2
16:30 - 18:00

Chairs: Hichem Haddou BENDERBAL IMT Atlantique, Nadjib BRAHIMI Rennes School of Business


Empirical Classification of Advanced Information Technology Towards Their Support of Leadership Behaviors in Virtual Project Management Settings


In times of globalization and societal change on the topic of working remotely, virtual collaboration of project teams has a high relevance. The continuous improvement of advanced information technology makes it possible to work globally distributed and in home office. This paper focuses on the usage of communication tools to support leadership behaviors and on the behavior’s requirements of media richness and media synchronicity. Managers (N=10) name in qualitative interviews communication tools, they are using for the communication with their employees concerning different leadership topics. These tools are classified in a theoretical model of leadership behavior. Building upon these results, a second study with project managers (N=75) who work in virtual teams rate these communication tools according to their usage for different leadership behaviors. Results show, that the demand on advanced information technology and their media richness and media synchronicity depends on the displayed leadership behavior. The results give implications for future investigations on the role of communication tools for the leadership of virtual teams.


Towards an Extended Team Model for Agile Development of Complex Products


In order to handle the increasing complexity in product development projects, agility has established as a promising solution approach. A large number of agile methods from software development have aroused great interests among industrial users. However, deviating conditions in the development of complex mechanical engineering products pre-vent simple adoption of these agile methods. Therefore, this paper aims to present a robust transdisciplinary under-standing of agility for a complex product development. Using a systematic literature review, the paper derives central agile factors along different structural dimensions of an extended team model. In a final empirical evaluation, the derived agile factors are subjected to a practical assessment among development project managers. For further re-search, the results should allow the translation of the practically confirmed factors into measurable indicators of agility in product development.


A Taxonomy for Engineering Change Management in Complex ETO Firms


In this paper, we study a specific type of Engineer-to-Order (ETO) firms called Complex ETO characterized by one-of-a-kind products with high complexity and low volumes. Such firms are at high risk of encountering significant engineering changes due to their characteristics. The management of engineering changes have a large impact of time, cost, and quality of the project. The purpose of this paper is therefore to provide a holistic taxonomy for Engineering Change Management (ECM) that can guide the companies with set of actions to prevent, handle, and manage engineering changes. The study is based on literature and empirical findings from a single case study conducted in an offshore platform producer, which resulted in development and verification of the taxonomy.


Sustainability in the Civil Construction based on Emergy Analysis Theory (EmA): A Systematic Review of Literature

Luanda LIMA, Marcelo ALENCAR, Luciana ALENCAR

In recent years, the demand for natural resources has increased worldwide. Given this scenario, to assess sustainability in a process, some tools were developed. Emergy Analysis is an alternative tool, which presents in its concept the list of all the energy needed for a system to produce a product/service, based on the energy consumed from its resources. Thus, a systematic literature review on Emergy Analysis in the construction industry is carried out through an analysis of articles published between 2000 and 2019. Data and information are compiled and presented, considering topics such as the number of papers published, the research institutions that investigate this issue, and the baseline adopted in previous studies. Besides, it was possible to identify that the planning and construction stages of sustainable urban projects adopt EmA and the characteristics of the main mathematical models used in this tool since due to the complexity and difficulty of interpretation of the data, it is necessary to address the assumptions of the models used, to calculate indicators of sustainability.


Investigating the Effects of Modular Product Structures to Support Design Decisions in Modularization Projects

Erik GREVE, Christoph FUCHS, Bahram HAMRAZ, Marc WINDHEIM, Lea-Nadine SCHWEDE, Dieter KRAUSE

By using modular product structures, the external product variety required by customers can be realized with a minimum of internal variety of components and processes. Understanding the effects of this special product structuring strategy is essential in the early product planning phase to optimize the product structure for the entire value chain. For this purpose, an empirical study was conducted and is reported in this paper. Four real cases were analyzed to identify the effects of modular product structures considering effect occurrence and project-specific boundary conditions. The results are visualized in an extended impact model of modular product structures and traced back to possible measures of product structuring as well as essential target values in the company. These causal relationships serve product developers as decision support in planning and development of modular product structures.

E-Business and E-Commerce 1
16:30 - 18:00

Chairs: Krishna KOTTAKKI Bundl Technologies, Linda ZHANG IESEG School of Management (LEM-CNRS 9221)


Digital Transformations in the Apparel Value Chain for Mass Personalization


Mass Personalization (MP) is being adopted in the apparel industry to provide customized product solutions to the customer. Customer co-creation is the key opportunity provided during the initial design phase. The best way to evaluate these new products is to create virtual prototypes based on the digital environment. Therefore, this article examines the digital approaches to the development of apparel prototypes by systematically reviewing the literature. The findings reveal that existing digital approaches are primarily aimed at visualizing a new product based on a 3D avatar created using scanned body measurements of a customer, although user experience is required for evaluation. The most important factor in the design process is the digital solutions to enhance the user experience of the new product (comfort of clothing) in its infancy. Therefore, this study explores digital technologies used in other industries to get an understanding on the possibilities of adopting the same in the apparel industry.


A Constrained Clustering Algorithm for the Location of Express Shops

Xilin ZHANG, Xiao LIU, Jing JIANG

For many express companies, express shops are the first line of serving customers. Reasonable location of express shops is very important to improve customer satisfaction. To balance the operation cost for the express company and the convenience of customers, we need to shorten the distance of customers to their closest shop while maintaining an appropriate express amount at each shop. In order to optimize the location of express shops, a heuristic clustering algorithm considering the constraints of service scope and service capability is proposed. The validity of the model is validated by DB Shanghai regional data, and the constrained clustering algorithm is compared with immune genetic algorithm and K-means method. The results show that, within the ideal service capacity and distance constraints, the proposed clustering algorithm can cover 31%-35% more demand than immune genetic algorithm, 1.4%-13% higher than K-means method.


Efficient Detection of Shilling’s Attacks in Collaborative Filtering Recommendation Systems Using Deep Learning Models


Recommendation systems, especially collaborative filtering recommenders, are vulnerable to shilling attacks as some profit-driven users may inject fake profiles into the system to alter recommendation outputs. Current shilling attack detection methods are mostly based on feature extraction techniques. The hand-designed features can confine the model to specific domains or datasets while deep learning techniques enable us to derive deeper level features, enhance detection performance, and generalize the solution on various datasets and domains. This paper illustrates the application of two deep learning methods to detect shilling attacks. We conducted experiments on the MovieLens 100K and Netflix Dataset with different levels of attacks and types. Experimental results show that deep learning models can achieve an accuracy of up to 99%.


Relative Importance of Determinants Towards Users’ Privacy Disclosure on Social Network Sites by Privacy Invasion Experience Based on Construal Level Theory

Li-Ting HUANG, Jun-Der LEU

Even users know the privacy risks of information disclosure, many still fail to adopt protection mechanisms or decrease information disclosure behavior on social network sites (SNSs). We explain this phenomenon by considering users’ rational calculus, affection, as well as perception towards platform. The awareness towards privacy leaks gradually fades out with the time passing. Accordingly, we proposed that users’ self-disclosure behaviors on SNSs are a decision-making process determined by individual rational, emotional, or situational factors. Users’ choices of persuasion routes depend on psychological distance of privacy invasion events based on the perspective of construal level theory. Results from analyzing collected 241 usable records show three findings. First, users’ decision of privacy disclosure is based on institutional trust and cognitive absorption, rather than perceived benefits. Second, cognitive absorption influenced by perceived playfulness is critical to privacy disclosure behavior. The influence of cognitive absorption on privacy disclosure varies from the time period of personal bad experience happened. Third, the influence of trust on privacy disclosure is getting more important when users have bad experience and when users have modified privacy settings.


An Integrated Scheme for Robot E-procurement

Yafei NIE, Shurong TONG

E-procurement is an important way to purchase robots in recent years. Robot e-procurement requires efficient matching among buyers and sellers. Based on the characteristics of robot e-procurement, combined with the theories of e-procurement and transaction matching, the method of unidirectionally searching for seller’ supply information is expanded. An integrated robot e-procurement framework including the catalog procurement, the inquiry procurement and the bidding procurement is proposed to achieve bi-directional search and procurement between buyers and sellers. Based on the selection of core attributes and calculation of user satisfaction, the matching method for robot procurement is put forward for users to efficiently match products in accordance with their requirements.


Customer Satisfaction with Order Fulfillment in E-Retail Supply Chains in China: An Empirical Study

Yilin XIE, Linda L. ZHANG

The Internet has changed the way how customers make their purchases and how retailers do business. With the development of B2C e-commerce, the retailers compete intensively to improve customer satisfaction and further secure their loyal customer bases. In this study, we investigate the relationship between customer satisfaction and customer loyalty in retail supply chains in B2C e-commerce in China. A conceptual model is developed to reveal the relationships and the factors influencing B2C e-customer satisfaction. Partial Least Squares is employed to assess the relationships between observable and unobservable variables and between unobservable variables. More specifically, SmartPLS is adopted to test and visualize the relationships. Based on the results, we suggest that e-retailers in China should improve their after-sales service activities and strengthen flexible and responsive order fulfillment activities in the hope of satisfying customers and further increasing loyal customer base.

Operations Research 2
16:30 - 18:00

Chairs: Ripon CHAKRABORTTY UNSW Canberra at ADFA, Gitae KIM Hanbat National University


Performance Analysis of Greedy-based Construction Heuristics on Classical Vehicle Routing Problem

Yandong HE, Mingyao QI, Fuli ZHOU, Huilin LI

Vehicle Routing Problem (VRP) has been studying in the past 60 years, many versions of basic VRP has also been extended. In this paper, we introduce and design some greedy-based construction heuristics to obtain better initial feasible solutions for some classical VRP with time windows (VRPTW). This construction heuristics include basic greedy heuristics (BG), global K-greedy heuristics (GKG), random greedy heuristics (RG) and random K-greedy heuristics (RKG). At last the performance analysis of these heuristics is given about basic VRPTW and VRPTW with roaming delivery locations (VRPTW-RDL). Some interesting conclusions is given.


Optimization of Capacitated Vehicle Routing Problem for Recyclable Solid Waste Collection Using Genetic and Seed Genetic Algorithms Hybridized With Greedy Algorithm

Gevorg GULOYAN, Ridvan AYDIN

There has been a growing interest in collecting recyclable waste to reduce total carbon emissions, generate economic growth, and promote total lifecycle sustainability. This paper studies capacitated vehicle routing problem (CVRP) related to recyclable solid waste collection. The problem differs from the classical CVRP in terms of considering a separate recycling station in addition to the main depot. Genetic Algorithm (GA) and Seed Genetic Algorithm (SGA) hybridized with Greedy Algorithm are proposed. The objective of this study is to determine the optimal routes for the collection and delivery of recyclable solid waste. Hybrid GA and hybrid SGA are used to find the optimal solution while minimizing the total traveling distance. In addition, a web-crawling bot is developed to generate the matrix of real distances rather than considering the Euclidean distances. A real case of collecting recyclable waste in Yerevan, Armenia by an NGO has been studied to evaluate the effectiveness of the proposed approach. The results show that SGA provides better solutions than GA, and that these algorithms are better than the solution adopted by the NGO.


A Large Neighbourhood Search Approach to Airline Schedule Disruption Recovery Problem

Kam K.H. NG, Kin Lok KEUNG, Carman Ka Man LEE, Yuk Ting Hester CHOW

The occurrence of unplanned aircraft shortages and disruption of flight schedules during the day-to-day operations of airlines is inevitable. When equipment failure causes unsafe flight, the aircraft will be grounded or temporarily delayed when the weather shuts down the airport or the required flight crew is unavailable. Real-time decisions must be made to reduce revenue loss, passenger inconvenience and operating costs by reallocating available aircraft and cancelling or delaying flights. A large neighbourhood search algorithm is used in this research to construct a feasible and efficient solution to the airline schedule disruption recovery problem. We aim to reduce the aircraft turn-around times, including total delay time, the number of flight adjustments and the number of flights delayed for more than one hour, as an objective function. Ten real-life cases are solved, and the proposed approach yields an approximate 50% improvement in solution quality.


Model for Hazardous Material Transportation Problem via Lane Reservation Under Considering Environmental Risk

Zhen ZHOU, Haoyan ZHAO

This paper investigated a hazardous material transportation problem via lane reservation under considering environmental risk. A new bi-objective model for it is formulated. Then two delicate preprocessing techniques are developed based on some properties of the investigated problem. On this basis, the investigated problem is solved by an improved ε-constraint method. Finally, the performance evaluation of the proposed model and method is given by a real-network-topology-based instance. The computational results show that the proposed model and method are effective.


A Multi-objective Emergency Scheduling Model for Forest Fires with Priority Areas

Lubing WANG, Peng WU, Feng CHU

With global warming, the probability of forest fires is increasing greatly, and more research attention has been paid to forest fires emergency scheduling. This paper addresses a resource-constrained emergency scheduling problem for dealing with forest fires with priority disaster areas. It aims to determine an optimal fire-fighting scheduling plan for multiple forest fire points to minimize the total transport distance and the fire extinguishing rescue time, simultaneously. To effectively solve this problem, we formulate a multi-objective mixed-integer linear programming model, and an iterative and fuzzy logic decision-making based on ε-constraint -constraint method is designed to obtain a preferred emergency scheduling scheme. Finally, the computational results on benchmark and randomly generate test instances verify the effectiveness and feasibility of the proposed model and method.


An Order-First Split-Second Approach to a Novel Variant of the Cardinality-Constrained Covering Traveling Salesperson Problem

Chantal SCHÖNI, Philipp BAUMANN, Norbert TRAUTMANN

We deal with the following application of the cardinality-constrained covering traveling salesperson problem. A company offers the valuation of real-estate properties, which includes an on-site visit by a contractor. Each contractor visits several properties during a tour, which must comprise not less than a minimum and not more than a maximum number of visits and must not exceed a prescribed length. Given a set of properties, the planning problem is to determine the respective tours such that the total relevant cost of all tours is minimized; for each tour, this cost consists of some fixed costs plus some variable costs proportional to the total distance of the tour. We propose a novel order first split-second approach which at first devises a giant tour, then splits this tour into feasible tours, and eventually tries to improve these tours individually. Our computational results for a set of test instances from the literature indicate that the proposed approach runs much faster than the reference approaches and devises good feasible solutions; for the largest instances, the proposed approach even outperforms the reference approaches.

Manufacturing Systems 2
19:00 - 20:30

Chairs: Hichem Haddou BENDERBAL IMT Atlantique, Junfeng WANG Huazhong University of Science and Technology


Diagnosis on Energy and Sustainability of Reconfigurable Manufacturing System (RMS) Design: A Bi-level Decomposition Approach


Sustainability and energy consumption awareness led industrial sector to reduce energy consumption. This reduction is regarded as a solution to reduce greenhouse gas emissions. Moreover, international regulations about maintenance activities involve hazardous energy-any electrical, mechanical, nuclear or other energies that can harm personnel- as a rising threat. Thus, energy audits and diagnosis of existing manufacturing systems are crucial to achieve energy efficiency. Future manufacturing paradigms as reconfigurable manufacturing system (RMS) have shown high responsiveness to cope with new challenges such as sustainability. This paper proposes a sustainable RMS design through process plan generation. The approach is developed to generate a process plan while diagnosing energy flow and assigning preventive maintenance activities related to reliability reduction in system components. More specifically, a mixed-integer non-linear program is proposed, then solved using a bi-level decomposition approach. The lower-level considers process plan generation following parts requirements and guided by energy loss as an objective. Afterwards, the upper-level diagnoses the reliability of the lower-level selected machines and tools. Moreover, it checks if preventive maintenance is required due to the level of hazardous energy and maintenance plan. The approach applicability is validated through an illustrative example.


Industrial Wastewater Treatment Configuration: Insights from a Northern Italy Textile Manufacturing District

Marta NEGRI, Enrico CAGNO, Caterina SALEMME, Andrea TRIANNI

Industrial wastewater treatment is getting increased attention from academics, practitioners and regulators, due to the environmental hazard of discharging poorly treated wastewater into the environment. This paper analyzes the case of Como’s textile district in Italy, to explore what factors are considered by firms in selecting the most appropriate wastewater treatment system configuration. The case studies highlighted that Como’s wastewater consortium benefits the firms in the district, and it is a better solution compared to the presence of sub-optimal private treatment plants. The firms mentioned internal stakeholders, factors related to the wastewater and technology, and economics as the most relevant.


A Data Model to Apply Process Mining in End-to-End Order Processing Processes of Manufacturing Companies


To master ongoing market competitiveness, manufacturing companies try to increase process efficiency through process improvements. Mapping the end-to-end order processing is particularly important, as one needs to consider all order-fulfilling core processes to evaluate process performance. However, process mapping in manufacturing companies is mostly applied in partial processes and not on the end-to-end order processing. Process mining provides a data-based description of processes and their performance and thus objectively and effortlessly maps real as-is processes. The data basis for process mining is an event log. Nevertheless, the generation of an event log in end-to-end order processing is not as trivial as in partial processes, as different data from different information systems must be merged. This paper discusses the development of a data model through an Action Design Research (ADR) method, derived and validated across ADR-cycles. The data model presents, which data can be extracted from integrated database sources to create the required event log for process mining in end-to-end order processing.


Small Series Production and Geometric Analysis of Sheet Metal Car Body Parts Using Forming Tools Made of Fused Filament Fabricated PLA


The automotive industry is currently facing increased pressure to innovate by developing and producing several drive concepts in parallel. This results in increased demands on product development. Rapid, cost-reduced implementation of tool changes during the product development process is becoming a decisive advantage in an increasingly competitive market. Other players are also entering vehicle development, which are digital, dynamic, flexible and close to the customer. The increasing number of small and medium-sized series and the trend towards mass customization are motivating the need for a flexible and cost-efficient production technology for car body parts. One solution to these challenges is the application of polymer-additive manufactured functional elements in forming tools for thin sheet metal. In this paper, the forming of several demonstrators is described. Therefore, thin sheet metals are deep drawn with tools made of PLA. The experiment represents a small series production of an exemplary car body part. This will be validated by optical measurement of the produced car body parts as well as the tool.


Exploring Reconfigurability in Manufacturing Through IIoT Connected MES/MOM

Soujanya MANTRAVADI, Jagjit SRAI, Thomas Ditlev BRUNOE, Charles MØLLER

This paper explores the role of manufacturing execution systems (MES) with ISA 95 functionalities for the reconfigurability in a manufacturing enterprise. The work is aimed at supporting digitalization based on Industry 4.0 and the Industrial Internet of Things (IIoT) concepts. For this, we use the quality function deployment method to link ISA 95 MES functionalities and reconfigurability needs, based on a case example of a cyber-physical factory (AAU Smart Lab). Accordingly, we present a framework to assess reconfigurability for smart factory development. The paper identifies reconfigurability approaches using IIoT connected MES/MOM for tackling severe market disruptions (e.g. the one caused by the ongoing COVID-19 pandemic).

Big Data and Analytics 2
19:00 - 20:30

Chairs: Philipp BAUMANN University of Bern, Oliver STRUB University of Bern


A Dynamic Feedback System Analysis on the Mechanism of Shipping Freight

Xiwen BAI, Ming Qi XU, Haiying JIA

This study aims to investigate how congestions at ports affect the shipping freight market using a System Dynamics model, particularly in the liquified petroleum gas (LPG) maritime transportation market. We utilize maritime big data derived from the Automatic Identification System (AIS) for vessel tracking in the analysis. Our model captures the positive impact of port congestion, at its high level, on freight rate volatility when the shipping market is relatively in a tight condition. The proposed model provides insights into the shipping freight market development by innovatively considering port congestion level. The findings provide practical guidance for industrial practitioners to anticipate future freight rates based on the current congestion level and for port authorities to plan for infrastructure upgrades accordingly.


Optimal Feature Selection for Support Vector Machine Classifiers

Oliver STRUB

Binary classification is a fundamental task in machine learning. It consists of learning a relationship between observable features of a set of training objects and their observable membership to either of two classes to predict as accurately as possible the class membership of new test objects whose features are observable but whose class membership is unknown. One of the most successful methods for binary classification is the support vector machine classifier that aims at finding a hyperplane in the feature space separating the training objects of the two classes. However, the accuracy of this classifier in predicting the correct classes strongly depends on the features selected for determining the hyperplane. In this paper, we propose the first exact approach, which is based on mixed-integer quadratic programming and delayed constraint generation, to identify an optimal set of relevant features for determining the hyperplane. The results of a computational experiment demonstrate that the proposed approach is able to successfully select an optimal set of relevant features in a short running time even for classification tasks with over 10,000 objects and 100 features.


News Media Sentiments and Stock Markets: The Indian Perspective

Shweta AGARWAL, Utkarsh GOEL, Shailendra KUMAR

Technological advancements accompanied with the gradual decrease in the cost of communication systems have ensured that the humankind is constantly bombarded with the information from around the world. Internet has become Information Super Highway on which any information travels with a lightning speed and easily permeates into all spheres of life. Capital markets also prosper on information and the information revolution has transformed these markets. This paper attempts to investigate the impact of information diffused through the news media on the stock markets of an emerging economy, with evidences from India.


Feature Engineering for Supply Analysis in Ocean Transportation


In recent years, much effort has been put into the application of ship tracking data for the purpose of shipping market modeling. Such high-frequency spatial data enables real time monitoring of the whereabouts of the global fleet and the activity along the networks on which it trades. However, for spatial data to be of use for price prediction it needs to be enhanced with commercial data, only part of which is publicly available. In this paper we expand on the inherent challenges in deriving the true demand and supply balance in the freight market and show examples of advanced feature engineering using merged spatial and commercial data.


Using of Social Media Data Analytics for Applying Digital Twins in Product Development

Abdiladif Ahmed OLAD, Omid FATAHI VALILAI

Product development as an iterative combination of different disciplines like design, process planning, engineering, manufacturing, logistics, marketing and sales is a challenging issue. Researchers have argued that high product mortality rate, is not only down to factors related with the design of a certain product, but also the fact that consumer engagement and customer behavior are decisive in the product development stages. Dealing with consumers is a big challenge due to the enormous variety of feedback data and qualitative aspects of them. Mostly, the communication channels with consumers are through Social Media (SM) which are usually unstructured and hard to be analyzed. This research has investigated the importance and influence of the emotional behavior of users in accepting new products through a social media analytical approach. The paper has developed a framework to relate the user emotions and new product feature development plan through SM platforms such as Twitter. It is discovered that emotions can be considered as one the important elements for identifying the subjective nature of product attributes. The paper shows that diverse emotions and their role in New Product Development (NPD) processes, specifically in pre-launch phase. A case study has been designed to show the capabilities of the proposed framework in this research. The pre-launch and post-launch emotion Comparisons indicate the possibility of shifting users’ behavioral focus for the new product while considering the required market indicators.


A Binary Linear Programming-Based K-Means Algorithm For Clustering with Must-Link and Cannot-Link Constraints


Clustering is probably the most extensively studied problem in unsupervised learning. Traditional clustering algorithms assign objects to clusters exclusively based on features of the objects. Constrained clustering is a generalization of traditional clustering where additional information about a dataset is given in the form of constraints. It has been shown that the clustering accuracy can be improved substantially by accounting for these constraints. We consider the constrained clustering problem where additional information is given in the form of must-link and cannot-link constraints for some pairs of objects. Various algorithms have been developed for this specific clustering problem. We propose a binary linear programming based k-means approach that can consider must-link and cannot link constraints. In a computational experiment, we compare the proposed algorithm to the DILSCC algorithm, which represents the state-of-the-art. Our results on 75 problem instances indicate that the proposed algorithm delivers better clusterings than the DILSCC algorithm in much shorter running time.

Human Factors 1
19:00 - 20:30

Chairs: Michel ALDANONDO Toulouse University / IMT-Mines Albi, Linda ZHANG IESEG School of Management (LEM-CNRS 9221)


The Effect of Gender, Hand Anthropometry, Hand Dominance, and High School Grade on Hand Grip Strength in Filipino Teenagers Aged 15-18: A Structural Equation Modeling Approach

Yogi Tri PRASETYO, Rod Vincent L. CORTES, Franklin S. BAUTISTA, Kenneth C.E. PIGUING, Aaron Josh A. BERMUDEZ, Charlotte N. MONTEIRO

Hand grip strength, or widely also known as Maximum Voluntary Contraction (MVC), is an important part in physical ergonomics. The purpose of the study was to establish Filipino teenagers’ hand grip strength data. In addition, another objective of the study was to investigate the causal relationships of gender, hand anthropometric, hand dominance, and high school grade on hand grip strength in Filipino teenagers by utilizing the Structural Equation Modeling (SEM) approach. A total of 100 male and 100 female students were voluntary recruited from Mapúa Senior High School students to perform MVC. SEM indicated that gender was found to have the most significant effect on the hand grip strength, followed by hand anthropometric, and hand dominance. Interestingly, hand dominance was found to be significant on hand grip strength. This study is the first study that establish the hand grip strength among Filipino teenagers and it would be very beneficial for human factor engineers, hand therapists, and even medical doctors. Finally, the proposed SEM approach could be applied and extended in analyzing the hand grip strength in other countries.


Influence of Presentation Mode on User's Mental Map in Temporal Sequence Data Visualization

Wenlu WANG, Ningyue PENG, Haiyan WANG, Chengqi XUE

Spatial aggregation and small multiples are two mainstream visualization presentation methods for temporal sequence data. This study compares the effect of spatial aggregated and small multiples in mental map construction with three task genres: peak value identification (Task 1), variation tracking (Task 2), and trend comparison (Task 3). Spatially aggregated timestamps get transited through animations. Task completion time favors small multiples in Task 1, but spatial aggregation with animated transition is more favorable in Task 2 and Task 3 in terms of completion time and error rate. Subjective rating results are aligned with behavioral performance measures. This research can guide future temporal sequence data visualization design.


The Importance of Including Qualitative Data in Technology Evaluation - Investigating the Technology Implementation Evaluation Score (TIES)


The recent increase in welfare technology implementation calls for the development of evaluation methods. Previous research has identified the need for a coherent evaluation model to create indicators that combine individual, economic and organizational dimensions. The Technology Implementation Evaluation Score (TIES) works in this way. It provides a score by combining statistical and monetary data (quantitative data) with patient and caregiver opinions (qualitative data). This paper aims to investigate the effects of including qualitative data on the outcome of the TIES model, compared to only using quantitative data. Various combinations of inputs are investigated by performing a sensitivity analysis using extreme value scenarios. The result shows that the TIES model is more affected by the number of data types created and the weight attached to each input than the data used as inputs. The assessment shows that the model is appropriately constructed for the current home care case.


Young Consumers’ Perception Towards Downstream Green Supply Chain Practices

Vaishnavy PERINPARAJAH, H. Niles PERERA, Jayani Ishara SUDUSINGHE, Uthpalee HEWAGE

Organizations, suppliers and consumers are highly sensitive towards environmental impacts arising from their operations today. Under this context, green consumption and the green packaging are gaining popularity among consumers. This research evaluates young educated consumer perception towards green packaging in Sri Lanka. A survey was conducted targeting university students who are reading for an undergraduate degree in Sri Lanka. Demographical information, attitudes and beliefs, knowledge about environmental sustainability, environmental awareness, and factors influencing their consumer behavior towards green packaging were collected through the survey. The data was analyzed using the Analytical Hierarchical Process. The findings of the research help understand main factors which influence the young educated consumer behavior towards green packaging and the level of influence of each factor. The analysis also reveals the hindrances and challenges withholding the use of green packaging.


What Makes a Robot Robotic? Application of Speed, Fluidity and Animation Principles to Define Human Versus Robotic Movement

Andrew PRAHL, Bernhard SCHMITT

This study aims to define what robotic movement is. A great deal of research has been done on nonverbal communication and the perception of robot movements, but this large body of research is yet to have a solid definition of what “robotic” movement is in the first place. We present a series of 8 stop motion video clips to a general population sample who rates the movement on a robotic-human continuum and reports the amount of surprise they would experience if a robot or human were to move in the manner portrayed in the clip. We manipulate the type of robot as either industrial or humanoid. Additionally, we manipulate movement by applying principles of speed, fluidity and animation in order to generate a definition of robotic movement. Results suggest that asynchronous, separated axis movement produces higher perceptions of robotic movement whereas animation principle applications such as hesitation can suppress perceptions of robotic movement.


Comparing Design Preference of Guide Road Signs by Native Arabic Speakers and International Speakers in the State of Qatar

Asma MAHGOUB, Pilsung CHOE

Guide road signs are used to help drivers. These signs should be designed properly and provided effectively to maximize the transmitted information in a short time in driving. The design of guide road signs should be made carefully when multi-languages are used, which is the case of Qatar. To compare the preference of a guide road sign design between different language speakers, an online survey was conducted. Participants rated a design using a 7-Likert scale. They preferred road signs having a background of different colors and a vertical text layout. A group t-test was conducted for comparisons between native Arabic speakers and international speakers whose native language is not Arabic. The results showed that there is no significant difference between the two groups. Based on the result, the proposed design can be effectively used for all drivers regardless of their native language in Qatar.

Poster Session 1
10:00 - 11:30

Chairs: Ai Chin THOO Universiti Teknologi Malaysia, Roger JIAO Georgia Institute of Technology


The Key Success Factor for Attracting Foreign Investment After the Popularity of COVID-19

San-Weng HUANG, James LIOU, Gwo-Hshiung TZENG

COVID-19 has had a devastating impact on the world economy. In order to stabilize the economic situation, governments of various countries have proposed economic development strategies. In the past, promoting investment has been one of the important means by which governments of various countries promote national economic development. After the epidemic, global resources will be redistributed, and how to increase advantages to attract foreign capital will be crucial. In the past, scholars used statistical and economic models for discussion. However, factors in the real world should influence each other. Therefore, this study will use the decision experiment and evaluation laboratory (DEMATEL) method to explore the key factors that enhance the national advantage after the outbreak. The case will be comprehensively discussed with countries with excellent international epidemic prevention performance.


Productivity Evaluation of Asia Textile Industry

H. T. TSAI, T. H. HO, Chia-Nan WANG

In recent years, textile industry is considered "key" by the advantages: less investment, payback fast, cheap and abundant labor, solves many jobs and contribute largely on exports, nearness to growing markets. Beside that recent liberalization of the world's textile and apparel trade policies facilitate to the Asian region has become a potential global center for textile and clothing exports. This research develops an integrated method based on data envelopment analysis (DEA) to establish an assessment model management performance of the group countries market, which has been one of the Asia biggest and most attractive marketplace and by using the Malmquist productivity index (MPI), we can estimate the productivity change in the textile industry.


Advance Selling Decision for Perishable Products in the Presence of Strategic Consumers

Yanli FANG, Zhuoyi REN, Zhaobin WEI

The advance selling strategy is often used in the sales process of perishable products with a short life cycle. The advance sales can encourage more strategic consumers to buy the product in advance with a discount price due to the extended sales period, but may also bring more profits for the seller. The seller often faces a newsvendor problem, i.e., they must order the joint decisions about an advance-selling price and inventory with uncertain demand in a two-period season, where the first period is the advance selling period and the second is the spot selling (and consumption) period. This paper establishes a two-period theoretical model to discuss the interaction between the advance selling decisions and consumers’ strategic purchasing behaviors. The game model show how the seller should optimally respond to consumers’ strategic behavior and characterize a cost threshold above which they should whether to advance sell. Finally, it is found that the advance selling strategy is not always optimal, but is related to the parameters of the cost per unit, the consumers’ valuations and heterogeneity.


Resource Allocation of Internet Display Advertising Considering Multiple Metric Constraints

Hanmin WANG, Xinglu LIU, Wai Kin (Victor) CHAN

In this work, we investigate the resource allocation problem in contract display advertising with a learning and planning approach. We formulate the problem as a mixed integer nonlinear programming, aiming to determine the resource allocation strategy in each advertising slot with CPC (cost per click), CPA (cost per action), CPM (cost per mille), CTR (click through rate), and budget constraints, thus maximizing the total page view. Since that the page view (PV) prediction is extremely complex, we assume our PV prediction function is concave and then we learn the PV prediction function for each slot using historical data. The concave assumption makes our objective function nonlinear, and we employ a piecewise linear approach to approximate PV function. To test the proposed model, we conduct numerical experiments with real data from the company. Results reveal that the proposed model outperforms the current allocation strategy. Moreover, our approach significantly increases the total contract page view and lowers the overall CPM.


Perceived Sustainability Performance of Eco-Industrial Park Through Environmental Consciousness and Strategic Intention

Ai Chin THOO, Jin Ming NGANG, Zuraidah SULAIMAN, Norhayati ZAKUAN, Hon Tat HUAM

In Malaysia, there are an average of approximately 13,000 tonnes of industrial waste produced every day with low solid waste recycling rate of 15%. This study aims to examine the levels and components of environmental consciousness (e.g. attitude, subjective norm, perceived behavioral control), eco-industrial park strategic intention and perceived sustainability performance of Malaysian manufacturing industries on implementation of eco-industrial park. A total of 78 samples were collected using quantitative method through manual questionnaires distribution and Google Form. Purposive sampling technique was employed to select respondents who involved in manufacturing industries. Structural equation modeling (SEM) was employed for data analysis. The findings showed that attitude and subjective norm are not positively related to eco-industrial park strategic intention while perceived behavioral control has a positive and significant relationship with eco-industrial park strategic intention. In addition, eco-industrial park strategic intention is positively related to perceived sustainability performance on implementation of eco-industrial park. This study is expected to contribute to Malaysia government, practitioners and academicians about environmental consciousness level, eco-industrial park strategic intention and perceived sustainability performance on implementation of eco-industrial park in Malaysia.


Applying K-Means Technique and Decision Tree Analysis to Predict Taiwan ETF Performance

Keng-Chieh YANG, Wen-Ping CHAO

Exchange traded fund (ETF) is called stock index fund or exchange-traded fund. ETF is a mutual fund that passively tracks the performance of an index and is listed on a stock market, like ordinary stock trading for trading. ETF index funds are buying a basket at a time including stocks, bonds, foreign currencies, etc. This study explores the performance and investment decision analysis of Taiwan ETFs. We use Weka software for data exploration and analysis. The data is downloaded from Taiwan Economic Journal (TEJ) database. A total of 229 ETF data are analyzed. This study uses K-mean analysis to measure the performance of ETFs in Taiwan. In addition, decision tree analysis is used for further analysis. We also propose good performance ETFs. The findings can provide investors for ETF selection.


What Core Competence Can Students Learn from Off-Campus Internship?

Feng-Ming SUI, Jen-Chia CHANG, Hsi-Chi HSIAO

The purpose of this study is to determine the viewpoints of the industry and academe on core competencies expected of students after completing off-campus internships and the gap in expectations and realizations. Results show that compared to the academia, the industry gave greater emphasis to core competencies. The “general core” competency dimension consist of two competencies: “Understanding the overview of internship institution operations” and “Understanding guild regulations.” the “professional core” competency dimension consists of three competencies: “Related system testing and application competency,” “The ability to collect and analyze data” and “The ability to discover practical and technical problems”. It is suggested that schools should set up an off-campus internship counseling unit to equip students with internationalization, globalization and industry and community-based work experiences. Skills, attitude, and behaviors are emphasized in learning how to manage workplaces in different fields, thereby assisting students in gaining an insight into and planning their careers. Schools should also regularly make arrangement for instructors to go to companies for professional or technology-related seminars or researches, thereby enhancing the practical teaching competency of instructors.


Internet-based Collaborative Design, Manufacturing, and Supply Chain for Manufacturing Companies

Xin YUAN, Yiwen CHEN, Qibo ZHANG, Xinguo MING

Collaborative framework in manufacturing has drawn wide attention. There exist huge methods and discussions on this topic under designing, supply chain, and manufacturing. With a motivation to improve system efficiency and give higher capital turnover, this work provides the following two parts. Firstly, this work designs a collaborative framework and methods in the designing, supply chain, and manufacturing. Next, this work plans potential collaboration methods with any two of those three components and gives illustrations. The result of this work, the six designed framework and methods aims to potentially achieve the intelligent and automated production logistics system, the entire process information management, fast capital turnover, transparent cost, accurate, smart manufacturing system, and the intelligent interconnection of equipment and human-machine systems with the least investment, quick output, and large output


Personal Health Mention Identification from Tweets Using Convolutional Neural Network

Yue WANG, Xiang LI, Daniel Y. MO

The past decade witnesses the unprecedent growth of social media users worldwide. People express health related outcomes, information, and views on social media platforms. This provides many opportunities to utilize the data source for health monitoring and surveillance, and digital epidemiology in real time. Personal health mention (PHM) is among one of the critical tasks for such purpose. It tries to identify whether a person’s health condition is mentioned in a sentence. However, social media texts contain noises, many creative and novel phrases, sarcastic Emoji expressions, and misspellings. This poses challenges to detect PHM from social media text. This paper explores the PHM identification task for six diseases from twitter using convolutional neural network (CNN). Specifically, word embeddings are used to encode the twitter text. Then they are fed into CNN structure to train the classifier for PHM identification. We also explore how the performance of different methods are affected by data imbalance issue and training sample size.


Optimized Layout of Emergency Monitoring for Sudden Marine Pollution Accidents

Xiaotian LIANG, Yu GUO, Weitao XIONG, Qingqing YANG, Jiang JIANG

The selection of monitoring sites in emergency monitoring of sudden marine pollution accidents is directly related to the reliability of monitoring results. Firstly, this paper creatively puts forward the concept and calculation model of the effectiveness of monitoring sites, which is described in terms of the pollutant concentration and the sensitivity of the region. Then, taking the maximum of the effectiveness of monitoring sites and the minimum of the number of monitoring sites as the objective function, and considering the constraints such as monitoring resource capacity and monitoring risk, a multi-objective optimization model is constructed, and the Pareto frontier is obtained by using NSGA-II algorithm, after which the most satisfactory scheme is selected by the weighted TOPSIS method. Finally, an example is given to illustrate the feasibility of the model, which can quickly give a scientific, reasonable, economical and efficient scheme for the layout of monitoring sites and provide decision support for marine emergency monitoring.

Poster Session 2
19:00 - 20:30

Chairs: Seung Ki MOON Nanyang Technological University, Omid Fatahi VALILAI Jacobs University Bremen gGmbH


What Makes Consumers More Strategic? Evidence from an Experimental Study

Yi LIU, Qiyuan LI, Yan CHEN

In a business environment full of dynamic pricing schemes and complex marketing strategies, modern customers get more strategic with their purchase decisions. Ignoring strategic consumer behavior can seriously damage a firm’s business performance. Previous studies have proposed some countermeasures to alleviate the negative impacts of strategic consumer behavior. In this study, a scenario-based experimental study is conducted to identify possible factors which will impact customers’ strategic waiting. Our experimental results show that the higher the price of the product, the more likely the customers will postpone their purchase and wait for price cut. In addition, high discount rate encourages customers to wait for the anticipated future discounts, while late discount impedes them to do so. In addition, we find that the information display strategy plays a completely different role when product price is different. Specifically, displaying only one impede customers’ strategic waiting when product price is low, while it actually encourages customers’ strategic waiting when product price is high, which is typically not desired by retailers.


Classification and Weighting of Strategic Projects in Organizations Under Multi-Criteria Decision-Making Situations

Fernanda SOUZA, Thais RODRIGUES, Rodolfo CARDOSO, Edwin MEZA, Carlos Frederico BARROS

Strategic Planning Alignment of all company organizational units facilitates a systemic holistic vision that favors efficient management. Thus, identifying each organizational unit’s contribution is an essential step toward strengthening integration among Brazilian regulatory agencies on a vast set of different regulatory objects. This article addresses the Strategic Planning Realignment of a Brazilian regulatory agency from 2020 to 2030, by presenting a Voting Method, based on an Analytical Hierarchical Process for weighting and ranking projects linked to the strategic objectives of said agency. As a result, a high adherence to priorities (over 60%) arising from the hierarchical choice of Board of Directors for a strategic monitoring of projects and a high adherence of participants to the methodology (over 60%), denotes the concern with strategic effort selection.


Trade-terms Based Pricing Method for Export Commodity

Shijin WANG, Jiaolong WANG

By far, the researches on the pricing methods of export commodity from China are very limited. This paper proposes a pricing model for export commodity by taking risk factors and trade terms into account. More specifically, the pricing of bulk export commodities is affected by many factors, including cost factors, market supply and demand factors and risk factors. Cost factors include production costs and logistics costs, market supply and demand factors represent that commodity prices are affected by market supply and demand conditions, and risk factors mainly mean that different choices of trade terms, cost, insurance and freight (CIF) or free on board (FOB), will cause suppliers to bear different risks. In this paper, the economic order quantity (EOQ) model is used to quantify production costs, the “base price + freight index fluctuation” model and the “base price + fuel price fluctuation” model are used to quantify logistics costs, and the risk costs under the influence of risk factors are calculated based on historical data. Finally, a pricing model for commodities based on different trade terms (CIF or FOB) is established, and the optimal prices can be determined.


Research on the Relationship Between Self-identity and Organizational Citizenship Behavior of the New Generation Knowledge Workers - The Mediating Effects of Organizational Identification

Shuyan GONG

The paper tends to study the influence of self-identity of the new generation knowledge workers on organizational citizenship behavior(OCB) and the mediating effects of workers’ organizational identification from the perspective of Social Identity Theory. The research is carried out on a total of 610 valid questionnaires with the object of 800 new generation knowledge workers from 10 enterprises respectively located in China’s Shanghai, Nanjing, Zhengzhou and Xi’an. The data obtained is processed by Exploratory Factor Analysis and Confirmatory Factor Analysis and the overall model is analyzed through Structural Equation Modeling. Finally, the article concludes that the self-identity of new generation knowledge workers has a significant positive impact on OCB during which workers’ organizational identification acts as a mediator.


Research on Key Factors of Total Social Welfare System of Car Hailing Industry-Based on DEMATEL Method

Huafeng CONG, Rui MIAO

Car hailing service is an important part of urban governance. The passengers, the enterprise and the government are three main participants of car hailing service. To evaluate and improve the total social welfare of car hailing industry, an index system considering the three participants as three dimensions is established, in addition, the DEMATEL(Decision Making Trial and Evaluation Laboratory) method is used to identify the key factor of the system and research the association rules among different dimensions. Finally, through an investigation case of a car hailing company, the key factors of total social welfare system are found and analyzed, from where the service operation methods for different dimensions are proposed.


A Procedure for Product Variety Reduction that Considers Linked Revenue

Tobias Kondrup ANDERSEN, Anders HAUG, Lars HVAM

Manufacturing companies are facing rising complexity due to customer demands for customized products and additional support services. This complexity comes at a high cost, and the benefits to be gained from product variety reduction projects are therefore significant. Several methods for the reduction of product complexity have been pro-posed in the literature. Such methods, however, to a large extent fail to consider the role of “linked revenue”, i.e., the revenue from the sales of product variants that is lost if other product variants bought by the same customers are eliminated. To address this gap in the literature, this paper develops a procedure for product variety reduction that considers linked revenue. The procedure supports managers in (1) identifying unprofitable products that can be pruned without risking the linked revenue of vital customers, (2) systematically evaluating the profitability and potential of these products, and (3) collecting feedback from affected account managers, before finally deciding which products to eliminate. The procedure is tested using a case study, which shows promising results.


Using Online Big Data for Determining the Importance of Product Attributes


The proliferation of e-commerce websites in recent times have spurred the number of online reviews on products generated online. This rapid development of e-commerce is usually accompanied by the desire of consumers to search for more information on a product before making any purchase. These two activities undertaken by consumers who tend purchase products online, generates large volumes of data that provides some useful insight for product manufacturers. The data generated from online reviews and consumers online search activity on a product could help product manufacturers to determine the needs and requirements of consumers. Using data mining methods, this study proposes a methodology to estimate the value of the importance of product attributes. These product attributes are the customers’ needs and requirements mined from online reviews and consumers’ online search data. This study proposes to use Shapley value and Choquet integral to determine the importance of product attributes from online big data.


An Application on Building Information Model to Procurement Strategy of Copper Raw Material with Big Data Analytics

Sheng-Tun LI, Kuei-Chen CHIU, Tsung-He CHIU

This research uses big data analysis to find the key factors of copper futures price fluctuations, successfully predicts copper price fluctuations, and applies them to the purchase strategy of copper raw materials for plant construction to help reduce plant construction costs. Since copper is an indispensable raw material in all building structures and pipeline configuration, the control of prices will help manufacturers from all walks of life to control the fluctuations in the price of copper raw materials and reduce the cost of building plants in the early stage of plant construction. Manufacturers "win at the starting point."


Aerosol Jet Printed Temperature Sensor for Wireless Monitoring

Joslyn Jun Wei LIM, Joonphil CHOI, Seung Ki MOON, Haining ZHANG, Noori KIM

Due to the current outbreak globally, Covid-19, it is crucial to keep monitoring temperatures for all those who have a chance of infection. As fever is one of the common symptoms of this disease, it is necessary to measure each individual's temperature, especially in the area where more people are present. Therefore, a three-dimensional (3D) printed temperature sensor with wireless function for medical applications is proposed. The sensing prototype is fabricated to detect and transmit users' body temperature to the cloud for real-time monitoring. The wireless temperature sensor is printed using the Aerosol Jet Printing (AJP) technique onto the substrate, polyimide (PI) with conductive ink, silver (Ag). This poster showcases the development of the temperature sensor using the AJP technique for medical applications. The strengths and limitations of various 3D printing methods, printed materials, and sensing prototype design are briefly discussed. Lastly, this work will end with a conclusion and future perspective of the field. The system has been suggested to be implemented in the clinical environment to lighten medical professionals' workload by providing efficient patient health monitoring.


The Managers Kit Turbo: Setting Up and Establish Teams in Case of an Emergency of a Manufacturing Cell


This paper describes the managers support Kit to promptly install teams in case of an emergency of a manufacturing cell according to known best practices in industrial engineering aspects and allowing them to operate quickly and sustainably. The objective of the project is to provide full guide to the team leaders for a (very) rapid groups/teams creation in emergency infrastructure, field hospitals, temporary logistics sites, reception of refugee populations, etc. The structure, operation and animation of the teams are essential to ensure that operational performance is achieved as quickly as possible and supported by mechanisms of continuous improvement and respect for the limited capacity of the resources employed. The project will synthesize two tools developed over several years of research-action at the Polytechnique Montreal laboratory CIMAR-LAB: the FUSÉE guide and the FOCALE guide. These tools were adapted to be integrated into the situation of this pandemic COVID-19. A case study at two Canadian manufacturing organizations was used to collect empirical data and to analyses important aspects of this project. This guide is named TURBO: All United, Resilient, Better Organized.