Prof. Dr.-Ing. Wilhelm Bauer
Fraunhofer Institute for Industrial Engineering IAO
Institute for Human Factors and Technology Management (IAT) at University of Stuttgart

Executive director of Fraunhofer Italia Research s.c.a.r.l.

As the Institute Director, Prof. Dr. Bauer manages a research organization with about 500 employees. He is in charge of research and implementation projects in the fields of innovation research, technology management, live and work in the future, Smarter Cities. As a member of various committees, he advises government and industry. As an author, he has over 280 scientific and technical publications to his name. He is an associate lecturer at the Universities of Stuttgart and Hanover. In 2012, Prof. Dr. Bauer received the honour of the State of Baden-Württemberg as "Tomorrow Makers". He also leads the Fraunhofer Initiative "City of the Future" and is a member of the "National Platform for future city" of the federal government.

"Industry 4.0 – an Economy based on the Internet of Things"
The digital transformation changes business and private life likewise – in a radically and sustainable way. Topics related to the networking of the internet by far have the most economical potential worldwide. The world becomes more and more digitally. This is the Big Business of the future. Also Production is currently facing a new upheaval. "Industry 4.0" stands for the comprehensive implementation of Information and Communication Technology as well as their networking to the Internet of Things, Services and Data. But not only in technology many things are changing, humans and the society transforms, too. We can be actively involved in the development of the Production of the Future using the techniques of "Industry 4.0" to foster our capabilites to be innovative, to advance the successful concept of lean manufacturing and to integrate the potential of our employees.

Michel Gendreau is Professor of Operations Research at the Department of Applied Mathematics and Industrial Engineering of École Polytechnique de Montréal (Canada). His main research area is the application of operations research methods to energy and transportation systems planning and operation. Dr. Gendreau has published more than 250 papers on these topics in peer-reviewed journals and conference proceedings. He is also the co-editor of six books dealing with transportation planning and scheduling, as well as with metaheuristics. Dr. Gendreau has completed his 6-year term as Editor in chief of Transportation Science on December 31st, 2014. He received in 2001 the Merit Award of the Canadian Operational Research Society in recognition of his contributions to the development of O.R. in Canada. He was elected Fellow of INFORMS in 2010.

"Transportation, Logistics, and the Environment"
For decades, transportation of persons and freight has been causing severe negative impacts on the environment. One of these impacts that immediately comes to mind is the very high level of emissions of greenhouse gases and other pollutants resulting from congested highways and cities. In the area of logistics, other negative impacts include the waste resulting from poorly designed and recovered products. Following the heightened environmental consciousness of the 1970's, efforts aimed at minimizing the environmental footprint of transportation- and logistics-related activities have slowly built up. Over time, this trend has accelerated and more systematic initiatives have targeted the development of "environment-friendlier" transportation and logistical systems. Several of these systems rely on advanced operations research models. In this talk, we will review some of the most significant efforts in this direction. These include, among others, the emergence and development of the fields of City Logistics and Reverse Logistics. We will also discuss briefly the area of Intelligent Transportation Systems, in which a large fraction of the systems that are developed should lead to an important reduction in emissions of pollutants. The end of the talk will be devoted to the recent emergence of planning problems that specifically target reduced emissions, such as, for instance, the Pollution Vehicle Routing Problem, in which one seeks distribution routes that minimize emissions, accounting for several important factors.

Stanley B. Gershwin is a Senior Research Scientist at the MIT Department of Mechanical Engineering. He received the B.S. degree from Columbia University in 1966; and the M.A. and Ph.D. degrees in Applied Mathematics from Harvard University in 1967 and 1971. In 1970-71, he was employed by the Bell Telephone Laboratories, where he studied telephone system capacity. At the C. S. Draper Laboratory in Cambridge, Massachusetts, from 1971 to 1975, he investigated problems in manufacturing and in transportation. He worked at the MIT Laboratory for Information and Decision Systems between 1975 and 1987. He was also Professor of Manufacturing Engineering at the Boston University College of Engineering in 1986-1987. Dr. Gershwin teaches MIT courses in Manufacturing Systems. He is a member of the MIT Laboratory for Manufacturing and Productivity and he is also affiliated with MIT's Leaders for Global Operations (formerly Leaders for Manufacturing Program) and the MIT Operations Research Center. He has been affiliated with the Singapore-MIT Alliance, the MIT-Portugal Program, and the MIT-Skoltech Initiative. Dr. Gershwin is the author of “Manufacturing Systems Engineering” (Prentice-Hall, 1994) and numerous papers in international journals. He is a co-editor of “Analysis and Modeling of Manufacturing Systems” (Kluwer, 2002). One of his papers was awarded both the Best Paper Award for the IIE Transactions focus issues on Design and Manufacturing for 2000, and the Outstanding IIE Publication Award for 2000-2001. He is a co-author of a paper that was awarded the Best Paper Award for the IIE Transactions focus issues on Design and Manufacturing for 2006. Dr. Gershwin’s research interests include real-time scheduling and planning in manufacturing and recycling systems; hierarchical control; dynamic programming in hybrid (discrete and continuous state) systems; and decomposition methods for large scale systems. His major research goals are the development of an engineering theory of manufacturing systems and an engineering theory of recycling systems. He and his students have performed research projects and consulted for numerous companies, including Boeing, General Motors, Hewlett Packard, Johnson & Johnson, Peugeot, Polaroid, and United Technologies. Dr. Gershwin is a member of the IEEE Control Systems Society, the IEEE Robotics and Automation Society, INFORMS (formerly the Operations Research Society of America), the Institute of Industrial Engineers, and the Society of Manufacturing Engineers. He has been an Associate Editor of several international journals, including International Journal of Production Research, Operations Research, and IEEE Transactions on Automatic Control. He has been a member of the Scientific Committee of the 1997 - 2015 series of Stochastic Models of Manufacturing and Service Operations (SMMSO) Conferences. Dr. Gershwin is a Fellow of the IEEE.

"Engineering and the Design and Operation of Manufacturing Systems"
The history of manufacturing has been the history of technological change in response to increases in demand or shortages of material or energy. Today, manufacturers are also faced with short product lifetimes, large product diversity, and impatient customers. As a consequence, manufacturing systems are particularly vulnerable to the effects of variability, uncertainty, and randomness. Variability is due to the dynamic nature of production. Uncertainty comes in many forms including inaccurate estimates of machine parameters such as the mean times to fail and to repair and the partial knowledge of the wear condition of machines. Many events occur at random times (such as the arrival of supplies and breakage of cutting tools) and many quantities are random (such as the yield of production processes that undergo rapid technological change). Variability, uncertainty, and randomness are expensive. Where they exist, their effects can only be mitigated by inventory, excess capacity, or excess production. Consequently, once the importance of variability, uncertainty, and randomness were understood, much effort was devoted to reducing them. Many practical common sense techniques were developed to reduce them, including the Toyota production system, the theory of constraints, and lean manufacturing. However, as factory performance requirements (for high quality products, short and reliable delivery times, and minimal production and environmental costs) become more stringent, further improvements will only be achieved when factory designers and operators develop a profound and quantitative understanding of the behavior of these complex systems. Developing such an understanding requires a research team: a close collaboration between people who have experience in designing and operating factories and people with strong mathematical and engineering skills in the modeling of random dynamics systems. With the support of software builders and other IT professionals, such a collaboration will produce practical computational tools for the analysis of proposed factory designs and real-time control policies. Such efforts will also foster the development of new intuitive insights on the behavior and optimization of factory designs. These tools and insights will be widely disseminated to factory professionals. Intuition is a necessary requirement for all engineers, including those who design and operate manufacturing systems. Intuition that is based on knowledge and experience can help an engineer provide a good starting point for design, and it can help to improve an existing system. This is important for manufacturing systems engineers. Software can assist engineers in refining their intuitive designs, but factory designers and operators cannot rely on black box software to substitute for intuition. Furthermore, engineering professionalism requires that engineers understand what assumptions the algorithms implemented in the software are based on. The manufacturing systems research community has already created many practical tools for factory design and operation which are not widely known outside of the research world. The research teams will bring these tools to the attention of industry practitioners. They will also develop new tools in response to needs that emerge from the collaboration between researchers and practitioners. Future factories must be designed and operated to meet future demands. For them to be effective, their designers and operators must have technical tools that are based on the most current engineering and analytical methods. Such tools can only be built by teams comprised of factory professionals, skilled technical researchers, and IT specialists.

Dr. Kathryn E. Stecke teaches in the School of Management at University of Texas at Dallas as the Ashbel Smith Professor of Operations Management. Previously she taught for 21 years at The University of Michigan Business School. She received an M.S. in Applied Mathematics, and an M.S. and Ph.D. in Industrial Engineering from Purdue University. She has authored numerous papers on various aspects of FMS planning and scheduling in numerous journals including The FMS Magazine, Material Flow, International Journal of Production Research, European Journal of Operational Research, IIE Transactions, IEEE Transactions on Engineering Management, Annals of Operations Research, Performance Evaluation, International Transactions on Operational Research, Naval Research Logistics, Production and Operations Management, Manufacturing and Service Operations Management, Management Science, Operations Research and several proceedings and book contributions. She is an INFORMS Fellow. She is the Editor-in-Chief of both the International Journal of Flexible Manufacturing Systems and Operations Management Education Review. She is on the Editorial Board, Area Editor, or Associate Editor of many journals. She was on the POMS Board of Directors (April 2006 - April 2008 and April 2014 to April 2016 (currently)). She served on the Board of Directors of INFORMS as Vice President from January 2003 to December 2004 and also served on the Board of Directors of INFORMS from January 1999 to December 2001.

In February 2004, INFORMS compiled a list of 475 papers that have 50 or more citations from all papers published in the journal Management Science in the last 50 years. All of her Management Science papers are on this list. Then INFORMS selected 50 of these as those papers that "represented the most significant research published in Management Science the last ½ century". One of her papers is on that select list.

"Seru Production System: An Organizational Extension of JIT"
A seru system is a new type of production system, widely used in Japan but unknown outside of Asia. Developed by Sony, it is used in all of Canon’s factories. It is more flexible, efficient, and productive than conventional manufacturing systems, for the industries in which it is appropriate. Seru’s history, development, and benefits will be described and discussed

Xiaolan Xie received his Ph.D degree from the University of Nancy I, Nancy, France, in 1989, and the Habilitation à Diriger des Recherches degree from the University of Metz, France, in 1995.

Currently, he is a distinguished professor of industrial engineering, the head of the department of Healthcare Engineering of the Center for Health Engineering and the head of IEOR team of CNRS UMR 6158 LIMOS, Ecole Nationale Supérieure des Mines (ENSMSE), Saint Etienne, France. He is also a chair professor and director of the Center for healthcare engineering at the Shanghai Jiao Tong University, China. Before Joining ENSMSE, he was a Research Director at the Institut National de Recherche en Informatique et en Automatique (INRIA) from 2002 to 2005, a Full Professor at Ecole Nationale d'Ingénieurs de Metz from 1999 to 2002, and a Senior Research Scientist at INRIA from 1990 to 1999. His research interests include design, planning and scheduling, supply chain optimization, and performance evaluation, of healthcare and manufacturing systems. He is author/coauthor of over 250 publications including over 90 journal articles and five books. He has rich industrial application experiences with European industries. He is PI for various national and international projects including ANR-TECSAN HOST on management of winter epidemics, NSF China key project on planning and optimization of health care resources, French Labex IMOBS3 project on home health cares, FP6-IST6 IWARD on swarm robots for health services, FP6-NoE I*PROMS on intelligent machines and production systems, the FP5-GROWTH-ONE project for the strategic design of supply chain networks, the FP5- GRWOTH thematic network TNEE on extended enterprises.

Dr. Xie is a fellow of IEEE. He has been an associate editor for IEEE Transactions on Automation Science & Engineering, IEEE Transaction on Automatic Control, IEEE Transactions on Robotics & Automation and International Journal of Production Research. He has a Guest Editor of various special issues on healthcare engineering and manufacturing systems. He is general chair of ORAHS'2007 and IPC chair of the IEEE Workshop on Health Care Management WHCM'2010.

"Daily surgery scheduling and end-of-the-day guarantee"
In this talk, I will focus on daily surgery scheduling in a multi-OR setting where OR stands for Operating Rooms with random surgery durations. We first address the dynamic assignment of a given set of surgeries with planned surgeon arrival times also called appointment times. Surgeries are assigned dynamically to ORs. The goal is to minimize the total expected cost incurred by surgeon waiting and OR idling/overtime. We first formulate the problem as a multi-stage stochastic programming model. An efficient algorithm is then proposed by combining a two-stage stochastic programming approximation and some look-ahead strategies. A perfect information-based lower bound of the optimal expected cost is given to evaluate the optimality gap of the dynamic assignment strategy. Numerical results show that the dynamic scheduling and optimization with the proposed approach significantly improve the performance of usual static scheduling and First Come First Serve (FCFS) strategies. We then address the optimization of surgeon appointment times for a sequence of surgeries. Surgeries are assigned to ORs dynamically on a FCFS basis. It materially differs from past literature in the sense that dynamic assignments are proactively anticipated in the determination of appointment times. A discrete-event framework is proposed to model the execution of the surgery schedule and to evaluate the sample path gradient of a total cost incurred by surgeon waiting, OR idling and OR overtime. The sample path cost function is shown to be unimodal, Lipchitz continuous and differentiable w.p.1 and the expected cost function continuously differentiable. A stochastic approximation algorithm based on unbiased gradient estimators is proposed and extensive numerical experiments suggest that it converges to a global optimum. A series of numerical experiments are performed to show the significant benefits of Multi-OR and properties of the optimal solution with respect to various system parameters such as cost structure and numbers of surgeries and ORs. The last part of this talk is devoted to the daily overtime management to provide OR-teams with different degrees of guarantee of on-time end of the day. This problem is modelled as two stochastic bin packing problems with chance constraints: a stochastic programming model and a robust formulation with moment information. We present a tailored branch-and-price approach based on a column generation reformulation for which CVaR and probabilistic sets are employed to speed-up the resolution of the pricing problems. For the distributionally robust formulation the worst-case distribution is shown to be a three-point distribution, analytical expression of the worst chance probability derived and simple mixed integer linear program model obtained. We implement a series of real-data-based numerical experiments to show that the benefits of optimal allocation and illustrate the value of robust solution.