ISMI2018 Program and Abstract Book(pdf)



ISMI2018 Schedule file (pdf)




February 7th (Wednesday)


12:00-13:30  Registration (Engineering Building I, 1F)


13:30-16:30  Industrial Visit -- TSMC


18:00-20:30  Reception (Casa de Socrates Café, NTHU蘇格貓底,清華大學)


February 8th (Thursday)


08:30-08:50  Registration (Engineering Building I, 1F)


08:50-10:30  Student Paper Award Finalist (Room 107, 1F)

Session Chairs: Hans Ehm, James R. Morrison, Jesus A. Jimenez



10:30-10:40 Break


10:40-10:50 Opening Ceremony (Room 107, 1F)


10:50-11:50 Keynote Speech 1 (Room 107, 1F)

Title: Smart Manufacturing and Big Data

Speaker: Kusiak, Andrew, The University of Iowa, USA


11:50-13:20 Lunch


13:20-15:00 Best Paper Award Finalist (Engineering Building  I, Room107, 1F)

Session Chairs: Lars Mönch, Yugma Claude, Young Jae Jang




15:00-15:05 Break


15:05-15:45 Keynote Speech 2 (Room 107, 1F)

Title: Optimization of Process Tool Operation for Future Semiconductor Manufacturing

Speaker: Lee, Tae-Eog, Korea Advanced Institute of Science and Technology, South Korea


15:45-16:25 Keynote Speech 3 (Room 107, 1F)

Title: Some Challenges on Integration of Decisions in Logistics

Speaker: Dauzère-Pérès, Stéphane, École des Mines de Saint-Étienne, France


16:25-16:40 Break


16:40-17:20  Keynote Speech 4 (Room 107, 1F)

Title: From Smart Machines to Smart Supply Chains: Some Missing Pieces

Speaker: McGinnis, Leon F., Georgia Institute of Technology, USA


17:20-18:00  Keynote Speech 5 (Room 107, 1F)

Title: Challenges for discrete event and agent based simulation in semiconductor supply chains – an industry view

Speaker: Ehm, Hans, Supply Chain Innovation, Infineon Technologies AG, Germany


18:30-20:00  Welcome Banquet and Awards Ceremony (The Peng’s Banquet新竹彭園會館)


February 9th (Friday)


08:40-08:50 Registration (Engineering Building I, 1F)


08:50-10:20 Technical Sessions A


08:50-10:20 Session A1 (Room 107, 1F)

Topic: SMILE Plenary Talk

Session Chair:  Xiang Li



08:50-10:20 Session A2 (Room 106, 1F)

Topic: Supply Chain System

Session Chair:  Jia-Nian Zheng



08:50-10:20 Session A3 (Room 108, 1F)

Topic: Dispatching and Scheduling

Session Chair:  Hung-Kai Wang



08:50-10:20 Session A4 (Room 103, 1F)

Topic: AI, IoT and CPS Applications

Session Chair:  Chia-Cheng Chen



08:50-10:20 Session A5 (Room 104, 1F)

Topic: Smart Manufacturing

Session Chair:  Jakey Blue



10:20 10:40 Break


10:40 11:20 Keynote Speech 6 (Room 107, 1F)

Title: Blockchain Technology for Smarter Operations

Speaker: Lee, Gyu M., Pusan National University, South Korea


11:20 12:00 Keynote Speech 7 (Room 107, 1F)

Title: Using Multi-Agent Systems for Planning and Control of Semiconductor Supply Chains

Speaker: Mönch, Lars, University of Hagen, Germany


12:00 – 13:00 Lunch


13:00-14:30 Session B1 (Room 107, 1F)

Topic: Equipment Maintenance and Simulation

Session Chair:  Wu-Hsun Chung



13:00-14:30 Session B2 (Room 106, 1F)

Topic: Quality control

Session Chair:  Dong Ni



13:00-14:30 Session B3 (Room 108, 1F)

Topic: Planning and Scheduling

Session Chair:  Thitipong Jamrus



13:00-14:30 Session B4 (Room 103, 1F)

Topic: Big Data Analysis

Session Chair:  Jia-Nian Zheng



13:00-14:30 Session B5 (Room 104, 1F)

Topic: Optimization and Decision Modeling

Session Chair:  Li, Li




14:30 14:40 Break


14:40 – 15:40 Tutorial Talk (Room 107, 1F)

Title: Manufacturing and Supply Chain Optimization with Augmented Reality (AR) in Samsung Heavy Industry (SHI)

Speaker: Jang, Young Jae, Korea Advanced Institute of Science and Technology (KAIST), South Korea


15:40 15:55 Break


15:55 – 16:35 Keynote Speech 8 (Room 107, 1F)

Title: Leading Green to Sustainability Era

Speaker: Tseng, Ming-Lang, Professor, Lunghwa University of Science and Technology, Taiwan


16:35 – 17:15  Keynote Speech 9 (Room 107, 1F)

Title: Advances in Hybrid Evolutionary Optimization with Learning for Manufacturing Scheduling

Speaker: Gen, Mitsuo, Fuzzy Logic Systems Institute, Japan


17:15 17:40 Best Presentation Award and Closing Ceremony


17:40 – 19:10 Farewell Dinner (Smile Café, MXIC Building旺宏館斯麥爾咖啡館)


Important dates 

  • Deadline for Full Paper Submission for EI-Indexed Proceedings for IEEE Xplore:      October 31, 2017 (==CLOSE==)

  • Deadline for Presentation-only Abstract Submission:                                                   December 31, 2017 (==CLOSE==)

  • Deadline for Full Paper Submission (for Best Paper/Student Paper Award entry):      December 31, 2017 (==CLOSE==)

  • Early Bird Registration (OPENED!!):                                                                             December 31, 2017 (==CLOSE==)

  •  Camera Ready Manuscript Submission:                                                                       January 14, 2018 (==CLOSE==)


Keynote Speech



Smart Manufacturing and Big Data (pdf)

Andrew Kusiak Professor The University of Iowa Editor-in-Chief Journal of Intelligent Manufacturing

2/8(Thu) 10:40 ~ 11:20

Abstract—Smart manufacturing is an emerging form of production encompassing concepts ranging from cyber-physical systems and internet of things to artificial intelligence and data science. Increasing volumes of data are being collected on materials, products, equipment, and events surrounding supply chains, production, and distribution. The data collected is used to enrich decision making with predictive models anticipating events at different time scales and horizons. The key differentiators of smart manufacturing are the greater use of data, predictive capabilities, resource sharing, networking, and sustainability. Driven by big data, predictive engineering offers a new paradigm in constructing high-fidelity models (digital representations) of phenomena of interest. Such models allow exploring future spaces, some within the realm of existing technology and others that have not been studied. Resource sharing aims at better utilization of the investments made with benefits expressed in fiscal and sustainability metrics. Sustainability is of paramount importance in smart manufacturing. New forms of manufacturing ranging from shared and distributed to autonomous and tightly integrated enterprises will emerge. Insights into anticipated changes in manufacturing are provided. Leading theories applicable to modeling smart manufacturing are presented. The concepts discussed are illustrated with applications. Professor Kusiak is a Fellow of the Institute of Industrial Engineers and the Editor-in-Chief of the Journal of Intelligent Manufacturing. Details of his research can be viewed at

Dr. Andrew Kusiak is a Professor in the Department of Mechanical and Industrial Engineering at The University of Iowa, Iowa City and Director of the Intelligent Systems Laboratory. He has chaired two departments, Industrial Engineering (1988-95) and Mechanical and Industrial Engineering (2010-15). His current research interests include applications of computational intelligence and big data in automation, manufacturing, product development, renewable energy, sustainability, and healthcare. He is the author or coauthor of numerous books and hundreds of technical papers published in journals sponsored by professional societies, such as the Association for the Advancement of Artificial Intelligence, the American Society of Mechanical Engineers, Institute of Industrial Engineers, Institute of Electrical and Electronics Engineers, Nature, and other societies. He speaks frequently at international meetings, conducts professional seminars, and consults for industrial corporations. Dr. Kusiak has served in elected professional society positions as well as various editorial boards of over fifty journals, including five different IEEE Transactions.




 Optimization of Process Tool Operation for Future Semiconductor Manufacturing (pdf)

Lee, Tae-Eog Professor Korea Advanced Institute of Science and Technology (KAIST) President,Korean Institute of Industrial Engineers (KIIE)

2/8(Thu) 15:05 ~ 15:45

Abstract—Cluster tools are widely used for most wafer fabrication processes. A cluster tool consists of several sing-wafer processing chambers, a wafer handling robot, and loadlocks for loading and unloading wafer cassettes. Wafers go through chambers and return after completing all process steps at the chambers while there is no intermediate waiting place for wafers, except robot arms. Cluster tools have many different architectures and scheduling requirements. Therefore, tool operation is rather complicated. Wafer delays within chambers as well as cycle time can be significantly improved by proper scheduling. There are numerous works on tool scheduling optimization. We briefly review tool architectures, scheduling requirements, modeling as discrete event systems, fundamental principles, and scheduling rules and optimization. We also explain future fab requirements and scheduling challenges. We also present how such principles and methods can be generalized to discrete event systems such as Petri nets and max-plus linear systems.

Professor Tae-Eog Lee joined Department of Industrial & Systems Engineering, KAIST in 1991 after his Ph.D. study at Ohio State University. He is Dean of Education and Director of Center of Excellence in Learning and Teaching at KAIST. He has made efforts to transform conventional lecture-based learning & teaching into interactive and student-participative ones and disseminated the strategies and experiences through almost 100 keynotes or invited talks. The effort was reported in Forbes, Nature, etc. He is a member of The Korean Academy of Science and Technology(KAST) and President of Korean Institute of Industrial Engineers (KIIE). His academic works on scheduling and control theory and application of discrete event dynamic systems and automated cluster tools for semiconductor manufacturing appear in IEEE transactions, etc. He won “Award for The Month’s Scientist and Engineer” from Korea Research Foundation and Ministry of Science, ICT, and Future Planning in 2015. He was an associate editor of IEEE Transactions on Automation Science and Engineering (2004~2008).



Some Challenges on Integration of Decisions in Logistics (pdf)

Stéphane Dauzère-Pérès Professor École des Mines de Saint-Étienne

2/8(Thu) 15:45 ~ 16:25

 Abstract—In this talk, we first present the notions of horizontal integration and vertical integration of decisions in logistics, with their main characteristics and motivations. Challenges related to each integration type are then discussed using examples based on academic and industrial research conducted by the author. The integration of decisions in production planning and scheduling and a railway transportation example are discussed for vertical integration. A production planning and vehicle routing problem and a maritime supply chain example are presented for horizontal integration.

Stéphane Dauzère-Pérès is Professor at the Center of Microelectronics in Provence (CMP) of the École des Mines de Saint-Étienne in France and Adjunct Professor at BI Norwegian Business School in Norway. He received the Ph.D. degree from the Paul Sabatier University in Toulouse, France, in 1992, and the H.D.R. from the Pierre and Marie Curie University, Paris, France, in 1998. He was a Postdoctoral Fellow at the M.I.T., U.S.A., in 1992 and 1993, and Research Scientist at Erasmus University Rotterdam, The Netherlands, in 1994. He has been Associate Professor and Professor from 1994 to 2004 at the École des Mines de Nantes in France. He was invited Professor at the Norwegian School of Economics and Business Administration, Bergen, Norway, in 1999. Since March 2004, he is Professor at the École des Mines de Saint-Étienne. His research interests broadly include modeling and optimization of operations at various decision levels (from real-time to strategic) in manufacturing and logistics, with a special emphasis on semiconductor manufacturing. He has published more than 65 papers in international journals and contributed to more than 120 communications in conferences. Stéphane Dauzère-Pérès has coordinated multiple academic and industrial research projects, and also five conferences.


From Smart Machines to Smart Supply Chains: Some Missing Pieces(pdf)

Leon F. McGinnis Professor Emeritus Georgia Institute of Technology

2/8(Thu)16:40 ~ 17:20

Abstract—In product design, the “digital thread” concept is transforming the way designs are created and shared across the supply chain, based on the emergence of standards for representing products and for sharing representations across different software platforms.  This progress, along with rapidly maturing information and computing technologies is encouraging the development of “smart” factories and supply chains, where real-time information about processes can be shared and used to drive faster, better operational decision making.  The vision of the “smart supply chain” where information is shared in real-time and products are delivered on-time with minimal inventories, minimal transportation costs, and no excess capacity is indeed appealing. There is at least one thing missing, and this talk will identify that missing element, and discuss what is needed to supply it.

Leon McGinnis is Professor Emeritus in the Stewart School of Industrial and Systems Engineering at Georgia Tech where he remains active in teaching and research.  He is internationally known for his leadership in the material handling research community and his research in the area of discrete event logistics systems.  A frequent speaker at international conferences, he has received several awards from professional societies for his innovative research, including the David F. Baker Award from IIE, the Reed-Apple Award from the Material Handling Education Foundation, and the Material Handling Innovation Pioneer award from Material Handling Management Magazine.  He is author or editor of eight books, one journal special issue, and more than 110 technical publications.  At Georgia Tech, Professor McGinnis has held leadership positions in a number of industry-focused centers and multi-disciplinary programs, including the Material Handling Research Center, the Computer Integrated Manufacturing Systems Program, the Manufacturing Research Center, the Sustainable Design and Manufacturing Program, the Tennenbaum Institute for Enterprise Transformation, and the Model-Based Systems Engineering Center.  His current research explores the adaptation of Model Driven Architecture, formal systems modeling methods and model-to-model integration to support model based decision making in the engineering of discrete event logistics systems.  Professor McGinnis is a Fellow of the Institute of Industrial Engineering.



Challenges for Discrete Event and Agent Based Simulation in Semiconductor Supply Chains – An Industry View(pdf)

Hans Ehm Lead Principal Supply Chain Supply Chain Innovation, Infineon Technologies AG, Germany

2/8(Thu)17:20 ~ 18:00

Abstract—Semiconductors play a vital role in the world economy and enable via the Electronic system innovations in the Automotive, Industrial and Medical industries. Semiconductor manufacturing is highly capital intense, the demand is difficult to forecast, many of its products have short product life cycle despite intrinsic long internal cycle time. To master those challenges Flexibility is needed on Fab and supply chain level, which ends up in a global manufacturing network and an integrated business planning process. A tremendous amount of data and a combination of high tech equipment, sophisticated planning tools and well educated human actors give room for optimization. Discrete event and agent based simulation on all four levels: Equipment/Cluster, Factory, internal supply chain and End-to-End supply chain has the potential to drive those optimization. Although discrete event simulation has shown to be beneficial since decades on Level 1 & 2 there are challenges on the principal setup of simulation, on Fundamentals & Enablers, on the Simulation Applications and the collaboration setup with universities, educations and financing. Semantic web technologies is upcoming and could support solutions for the challenges on the data structuring level

 Hans Ehm is Lead Principal Supply Chain and responsible for supply chain innovations of Infineon Technologies AG. He studied Physics in Germany at the University of Applied Science in Munich, where he received the Dipl. Ing. (FH) diploma in 1983. After that he received a Fulbright scholarship for the US and he finished his study there with a Master of Science in Mechanical Engineering from Oregon State University in 1985. Further university activities included studies of computer science at Fern-university Hagen and studying political science at LMU in Munich. In his 30+ years of experience in the Semiconductor Industry he was granted managing and consulting Positions at innovation, manufacturing, and for the global Supply Chains. Hans Ehm was part of JESSI (Joint European Submicron Silicon Initiative) in the 90’s  and provided in this capacity Know How and Data for MIMAC, a Reference Data Model for simulation in Semiconductor Manufacturing. Since about that time he supports MASM (Microelectronics And Simulation for Manufacturing) as an industry advisor. A reference Model for semiconductor supply chain will be published soon by him and Professor Lars Mönch. He was responsible from 2009 onwards for the European Leadership Team of the SCC (Supply Chain Council) and coordinated since 2013 additionally the global team. After the merger of SCC with APICS in 2015 he entered the APICS SCC Board and he is now acting as a past Board Member of APICS SCC. He collaborates with Universities and Associations around the globe – e.g. he initiated in 2009 a long distance curriculum with the UL (University of Limerick/Ireland). Hans lectures at Universities (beyond others: University of Applied Science Munich, TUM, KLU, MMU/Malaysia, UL and he supervises ~ a dozen Master and PhD students every year since more than a decade. Hans Ehm is since 2000 a Board member of camLine Holding AG, an IT company providing software for quality and supply chains – camLine is the #1 Software supplier for statistical process control in semiconductor wafer fabs. He is leading since 2013 the working group SCM of the ZVEI – the German association for electronic components and one of the largest industry associations in Europe. Hans Ehm has 30+ scientific publications in the field of semiconductor manufacturing and the supply chain with an h-index of 6 since 2012. He is a frequent speaker at conferences and received the LEO Award for Supply Manager of the year for Germany from the DVZ Media group in 2015.



Blockchain Technology for Smarter Operations(pdf)

Gyu M. Lee Professor Pusan National University Editor-in-Chief International Journal of Industrial Engineering: Theory, Applications and Practice

2/9(Fri)10:40 ~ 11:20

Abstract—The talk discusses about the hot-grazing topic, the blockchain technology, and how it can improve our operations in a smarter way, by sharing the data transparently and securely. The blockchain technology, initiated from a recently popular cryptocurrency, Bitcoin, ensures the trust among distrusting entities. Therefore, to build autonomous interoperable control systems for smart factories with various IoT devices or for smart digitized supply chains, the blockchain technology can provide an important backbone. The speaker emphasizes that this technology is not just one of information technologies for the future but this technology also enables the decentralized self-governing paradigm against the centralized one.

Gyu M. Lee is Professor. is a professor in Department of Industrial Engineering at Pusan National University. He received the BS and MS in Industrial Engineering from Seoul National University and the Ph.D in Industrial Engineering from The Pennsylvania State University. He held a professorship in University of Arizona, Northeastern University, University of Illinois, Oregon State University and POSTECH before he joined Pusan National University. He is an editor-in-chief of International Journal of Industrial Engineering: Theory, Applications and Practice (SCI) and an associate editor of Decisions Science (SSCI). His research interests include the operations research, data analytics, logistics and manufacturing, computational engineering and information technology.



Using Multi-Agent Systems for Planning and Control of Semiconductor Supply Chains (pdf)

Lars Mönch Professor University of Hagen

2/9(Fri)11:20 ~ 12:00

Abstract—In this talk, we discuss the design and the implementation of the S2CMAS multi-agent system prototype that supports planning and control activities in semiconductor supply chains. The proposed system extends the FABMAS prototype for production control of single wafer fabs by additional enterprise-wide planning-related decision-making agents and staff agents. We discuss the requirements for a rich communication of the agents by means of an ontology for planning tasks in semiconductor supply chains. Web services are used to implement certain parts of the new planning functionality. Results of some simulation experiments with the proposed multi-agent system are presented that indicate that the proposed approach is feasible.

Lars Mönch received the master’s and Ph.D. degrees in applied mathematics from the University of Göttingen, Germany, and the Habilitation degree in information systems from the Technical University of Ilmenau. He is a Full Professor with the Department of Mathematics and Computer Science, University of Hagen, Germany. His current research and teaching interests are in production planning and control of semiconductor wafer fabrication facilities, applied optimization and artificial intelligence applications in manufacturing, logistics, and service operations. He has authored over 75 refereed journal papers and book chapters, two monographs, and one edited book. He serves as an Associate Editor for the IEEE Transactions on Semiconductor Manufacturing, the IEEE Transactions on Automation Science and Engineering, the European Journal of Industrial Engineering, Business & Information Systems Engineering, and the Journal of Simulation.



Leading Green to Sustainability Era (pdf)

Ming-Lang Tseng Professor Lunghwa University of Science and Technology

2/9(Fri)16:10 ~ 16:50

Abstract—This leading practices features the green business practices and the sustainability programs, environmental compliance, and green marketing strategies. Firms are taking a greater interest of sustainable and green business practices as consumers continue to express a preference for products and materials that have been produced in environmentally friendly ways. This presentation introduces the concept of green and sustainability and addresses important trends and also addresses insights from the resource solutions the sustainable business practices and triple-bottom line guidelines.

Dr Ming-Lang Tseng is currently Professor at Department of Business Administration, Lunghwa University of Science and Technology, Taiwan. He will be Chair Professor and Director of Institute of Innovation and Circular Economy in Asia University, Taiwan on August 01, 2018. In addition, he is an Adjunct Professor in De La Salle University, Manila, Philippines; Adjunct Professor, Chongqing University, Chongqing, China; Adjunct Professor, Beijing JiaoTong University, Beijing, China; Visiting Professor, Anhui University of Finance, Anhui, China and Distinguished Professor (Haitien Scholar) in Dalian University of Technology, Panjin Campus, China. He held various executive positions in international groups (Textile manufacturing and Real Estate Development) in Asia, East and South Africa for more than 5 years’ experience before returning to academia in 2005. His research interests include Green Supply Chain Management, Sustainable Consumption and Production, Sustainable Supply Chain Management, Service Innovation and Novel Multi-Criteria Decision Making method. He has published more than 130+ journal articles, 130 conference papers (h-index= 26, G-index: 34) and awarded 25 projects in 10 years. In addition, he was a Research Fellow in the Institute of Applied Ecology at Chinese Academy of Sciences, China, in 2012-2013 and visiting scholar at University of Derby, UK, in 2015 (Funded by MOST, Taiwan). He handled several special issues on Sustainable Consumption and Production topics in JCLP, IJPR, IJPE, RCR, IEMS, MEQ, Sustainability, etc. Currently, he serves as Associate Editor of Journal of Cleaner Production (SCI, If:5.7) and Associate Editor of Management of Environmental Quality: an international journal (Scopus and INSPEC) and Editorial Board Member of ISI journals (ASOC, IMDS, RCR etc). He is also on the trustee member of several international organizations (APIEMS, ICPR Asia Chapter and ISBITM) and President of Chinese Institute of Innovation management and Development, Taiwan.



Advances in Hybrid Evolutionary Optimization with Learning for Manufacturing Scheduling (pdf)

Mitsuo Gen Senior Research Scientist Fuzzy Logic Systems Institute Visiting Professor Tokyo University of Science

2/9(Fri)16:50 ~ 17:30

Abstract—Many combinatorial optimization problems (COPs) in the real-world manufacturing systems impose on more complex issues, such as complex structure, nonlinear constraints, and multiple objectives to be handled simultaneously and make the problem intractable to the traditional approaches because of NP-hard combinatorial problems. In order to develop an efficient solution algorithm that is in a sense "best solution" that is, whose reasonable computational time for NP-hard COPs met in practice, we have to consider the following very important issues: 

- Quality of solution,

- Computational time and

- Effectiveness of the nondominated solutions for multiobjective optimization problem (MOP).

Evolutionary Algorithms (EAs) has attracted significantly attention with respect to complexity scheduling problems, which is referred to evolutionary scheduling. However, EAs differ in the implementation details and the nature of the particular scheduling problem applied. In order to have an effective implementation of EAs for a scheduling, this talk focuses on making a survey of researches based on using hybrid EAs. Starting from scheduling description, we identify the classification and graph representation of scheduling problems. Then we present the various representations, hybridization techniques, and machine learning (ML) techniques to enhancing EAs. Finally, we also present successful applications in manufacturing scheduling based on ML concept.


Dr. Mitsuo Gen is a senior research scientist at Fuzzy Logic Systems Institute and visiting professor at Research Institute for Science and Technology, Tokyo University of Science, Japan.

PhD in Engineering, Kogakuin University in 1975 and PhD in Informatics, Kyoto University in 2006. Faculty: Ashikaga Institute of Technology 1974-2003 and Waseda University 2003- 2010.

Visiting Faculty: University of California at Berkeley 1999-2000, Texas A&M University 2000, Hanyang University 2010-2012 and National Tsing Hua University 2012-2014, Fuzzy Logic Systems Institute 2009-current and Tokyo University of Science 2014-current.


Tutorial Talk


Manufacturing and Supply Chain Optimization with Augmented Reality (AR) in Samsung Heavy Industry (SHI) (pdf)

Young Jae Jang Associate Professor Korea Advanced Institute of Science and Technology (KAIST)

2/9(Fri)15:15 ~ 15:55

Abstract—The IT Convergence Team at SHI and researchers at KAIST teamed up to develop the SCM-AR solution which consists of the following four functions: 1) automatically analyzing the structure of the assembly topologies and determining the critical level of each part, 2) evaluating the delivery uncertainty level for each supplier–part pair based on past delivery history and part critical levels, 3) dynamically scheduling the assembly processes and prioritizing the urgent requests to suppliers, and 4) visualizing the assembly schedule with three-dimensional (3D) animation with an AR device so that field engineers and managers effectively understand the job sequence and schedule. The SCM-AR was deployed in 2016. A private LTE (4G) network infrastructure installed in the shipyard enabled the transfer of large volumes of data with the AR devices. The system allowed field engineers and managers to review which parts were delivered and which were not, and they could easily learn the delivery status of the delayed part. Then, they could run optimal job scheduling for a given delivery and available part information. Moreover, the AR-based 3D visualization/animation helped them clearly understand the optimal job scheduling and sequence. Its academic contribution is also clear. First, this is one of the few actual cases dealing with assembly for a supply-chain problem. Although there are some successful cases in integrating the design process with manufacturing in the form of design-for-assembly (DFA) and manufacturing-for-assembly (MFA), not many practical cases have been reported for design, assembly, and supply chain integration. In our problem, the part design structure, assembly configuration, and supply chain are considered together. A matrix presentation of the part-assembly structure was developed to effectively quantify the complexity of the part structure and assembly process together. This matrix was then used to effectively evaluate the feasibility of the assembly sequences in the optimization models. Another academic contribution of this work is the convergence of advanced IT technology with the industrial engineering method. The AR device shows the assembly sequence in a proactive way and how the optimal solution is generated. It was found that the decision was effectively delivered to the field engineers and managers. This is one of the first industrial AR use cases.

Dr. Young Jae Jang received the Ph.D. degree in Mechanical Engineering from MIT (2007); M.S. degree in Operations Research from MIT Sloan Management School (2003); and M.S. degree in Mechanical Engineering from MIT (2001). He received B.S degree in Aerospace Engineering from Boston University (1997) with Summa Cum Laude. He is currently an Associate Professor in the Industrial and Systems Engineering Department at Korea Advanced Institute of Science and Technology (KAIST). Dr. Jang is also affiliated with Deloitte Consulting Korea as an Analytics Advisor and has been working on various industry business consulting and engineering projects with global companies. His current research includes Stochastic Modeling of the Complex Systems; Automated Material Handling Systems; and Supply Chain Network Design.



  • This recognition will be awarded in two categories: Best Paper Award (with $600 USD honorarium) and Best Student Paper Award (with $500 USD honorarium).


  •  To be eligible, the candidates should have selected one of the following options to present an accepted full paper at ISMI2018 and IEEE SMILE2018 ( and should present it during the conference.


  1.  Regular: to be eligible for the Best Paper Award
  2.  Student: Student: to be eligible for the Best Student Paper Award. Student must be the first author or corresponding author, and the presenter.   


  •  Candidate papers should be submitted through the regular submission process by selecting one of the topics. All papers must be written in English with a max length of 8 pages . For paper format, submission, and related information, please visit: .


  •   According to the average evaluation scores of paper review, less than 20% of the candidates will be nominated as the finalists.


  •   Each of the finalists will make a presentation at one of the award competition sessions on February 8 (Thursday).


  •  The jury will be nominated and chaired by the committee of ISMI and IEEE SMILE.


  •  The names of the winners will be announced during the Banquet and Award Ceremony Dinner of the conference. The winners will obtain a diploma, $600 USD honorarium (Best Paper Award) or $500 USD honorarium (Best Student Paper Award).



Feb. 7 Wed. 13:30 – 16:30 Industrial Visit -- TSMC

Theme: TSMC Mueseum of Innovation

Meeting Point: at reception in Engineering Building I, National Tsing Hua University

Gathering time: 13:20


Feb. 8 Thur. 13:30 – 14:10 Library Guide Tour

Meeting Point:  at reception in Engineering Building I, National Tsing Hua University

Gathering time: 13:20  

Feb. 9  Fri.   13:00 – 13:40 Camps tour/13:40– 14:20 Library guide tour

Meeting Point:  at reception in Engineering Building I, National Tsing Hua University

Gathering time: 13:20  



Last update:02/08/2018