Title: Enhancing Service Facility Reliabilityagainst the Threat of Disruptions
Yanfeng Ouyang, University of Illinois at Urbana-Champaign
Abstract: While planning service networks to serve spatially distributed customers, we consider the case where built facilities are subject to probabilistic failure (due to reasons such as adverse weather or disasters). If a facility fails, its customers either lose service or incur higher costs. We focus on planning facility location as well as customer service strategies in the context oflogistics systems, and the goal is to minimize the expected system costs under normal and failure scenarios. We start with the case where site-specific and independent facility disruptionsmay occur whenservicesaredelivered via either direct visits or vehicle routing.Then we show recent extensions wherefacility disruptions exhibit positive and/or negative spatial correlations (e.g. when facilities are exposed to similar hazards or share the same support infrastructure).Wewillpresent compact mixed-integer program models that can be solved by customized algorithms based on relaxation and decomposition, and ways to address the challenge of correlation. We will also briefly explain continuum approximation models that not only serve as an effective way to find near-optimum solutions for large-scale systems, but also help provide managerial insights.
Bio: Yanfeng Ouyang is Professor, Paul F. Kent Endowed Faculty Scholar of Civil and Environmental Engineering, and Donald B. Willett Faculty Scholar at the University of Illinois at Urbana-Champaign (UIUC). He received his Ph.D. in civil engineering from the University of California at Berkeley in 2005. His research mainly focuses on transportation and logistics systems, infrastructure systems, and applications to energy, sensor, and agricultural industries. He currently serves as a Department Editor of IIE Transactions, an Area Editor of Networks and Spatial Economics, an Associate Editor of Transportation Science, an Associate Editor of Transportation Research Part C,an Associate Editor of Transportmetrica B. He received a Walter L. Huber Research Prize from the American Society of Civil Engineers in 2015, a High Impact Project Award from the Illinois Department of Transportation in 2014, an Engineering Council Outstanding Advisor Award from UIUC in 2014, a Xerox Award for Faculty Research from UIUC in 2010, a Faculty Early Career Development (CAREER) Award from the National Science Foundation in 2008, and a Gordon F. Newell Award from Berkeley in 2005, among others.
James M. Tien
Title:The Sputnik of Servgoods: Autonomous Vehicles
James M. TIEN , College of Engineering, University of Miami, Coral Gables, Florida 33146, USA
In an earlier paper [Tien 2015], the author defined the concept of a servgood, which can be thought of as a physical good or product enveloped by a services-oriented layer that makes the good smarter or more adaptable and customizable for a particular use. Adding another layer of physical sensors could then enhance its smartness and intelligence, especially if it were to be connected with each other or with other servgoods through the Internet of Things. Such sensed servgoods are becoming the products of the future. Indeed, autonomous vehicles can be considered the exemplar servgoods of the future; it is about decision informatics and embraces the advanced technologies of sensing (i.e., Big Data), processing (i.e., real-time analytics), reacting (i.e., real-time decision-making), and learning (i.e., deep learning). Since autonomous vehicles constitute a huge quality-of-life disruption, it is also critical to consider its policy impact on privacy and security, regulations and standards, and liability and insurance. Finally, just as the Soviet Union inaugurated the space age on October 4, 1957, with the launch of Sputnik, the first man-made object to orbit the Earth, the U. S. has inaugurated an age of automata or autonomous vehicles that can be considered to be the U. S. Sputnik of servgoods, with the full support of the U. S. government, the U. S. auto industry, the U. S. electronic industry, and the U.S. higher educational enterprise.
Bio. After 8 years as Dean of the College of Engineering at the University of Miami, Coral Gables, Florida, Dr. James M. Tien stepped down in 2015; he remains a Distinguished Professor. He received the BEE from Rensselaer Polytechnic Institute (RPI) and the SM, EE and PhD from the Massachusetts Institute of Technology (MIT). He has held leadership positions at Bell Telephone Laboratories, at the Rand Corporation, and at Structured Decisions Corporation (which he co-founded). He joined the Department of Electrical, Computer and Systems Engineering at RPI in 1977, became Acting Chair of the department, joined a unique interdisciplinary Department of Decision Sciences and Engineering Systems as its founding Chair, and twice served as RPI’s Acting Dean of Engineering. Dr. Tien has published extensively, been invited to present dozens of plenary lectures, and been honored with both teaching and research awards, including being elected a Fellow in IEEE, INFORMS and AAAS and being a recipient of the IEEE Joseph G. Wohl Outstanding Career Award, the IEEE Major Educational Innovation Award, the IEEE Norbert Wiener Award, the IEEE Richard M. Emberson Award, and the IBM Faculty Award. He received a Doctor of Engineering (honoris causa) from Canada’s University of Waterloo and is also an Honorary Professor at over a dozen non-U.S. universities. Dr. Tien is also an elected member of the U. S. National Academy of Engineering.
Title : Evaluation and Analysis of Heuristic Methods
Marc E. Posner, Nick G. Hall, Rutwik Vaidya
Abstract: Heuristics are used for a wide variety of purposes in numerous and diverse optimization applications. In many of these applications, there is a need to measure and evaluate heuristic performance. The purposes for measurement include choosing between heuristics, obtaining guidance about heuristic design, and understanding how problem characteristics affect performance. The variety of both heuristic purposes and details of the application complicate the selection of an appropriate heuristic and performance measure. As a result, the literature contains numerous examples of problematic evaluation and analysis. To improve the process, we propose a detailed theory of heuristic measure selection. This theory provides a foundation for the selection of an appropriate approach to evaluate heuristic performance.
Bio: Marc Posner is a Professor in the Department of Integrated Systems Engineering at The Ohio State University, where he specializes in discrete optimization methods, including integer programming and heuristic analysis. He currently serves as Departmental Editor of Scheduling and Logistics for IIE Transactions, and is on the Editorial Board of The Journal of Scheduling and Naval Research Logistics. Dr. Posner has worked on a variety of applications of optimization methods to logistical decision problems in areas such as production scheduling, facility layout, and supply chain management and has written over forty scholarly papers in these areas. He received a bachelor’s degree from Brandeis University and a master’s and Ph.D. in Operations Research from the University of Pennsylvania.