This textbook provides researchers, post-graduate students, and practitioners with a systematic framework for coping with uncertainty when making facility location decisions. In addition to in-depth coverage of models and solution techniques, application areas are discussed.
The book guides readers through the field, showing how to successfully analyze new problems and handle new applications. Initially, the focus is on base models and concepts. Then, gradually, more comprehensive models and more involved solution algorithms are discussed. Throughout the book, two perspectives are intertwined: the paradigm for capturing uncertainty, and the facility location problem at hand. The former includes stochastic programming, robust optimization, chance-constrained programming, and distributional robust optimization; the latter includes classical facility location problems and those arising in many real-world applications such as hub location, location routing, andlocation inventory.
Table of Contents:
Introduction.- Principles and Background.- Discrete Facility Location Problems.- Decision Making under Uncertainty: Ingredients for Modeling.- Optimization under Uncertainty.- Modeling Paradigms and Solution Techniques.- Robust Facility Location.- Stochastic Facility Location.- Facility Location with Chance Constraints.- Distributionally Robust Facility Location.- Extended Models.- Location-Inventory Problems.- Facility Location with Routing Decisions.- Hub Location.- Logistics and Supply Chain Management.- Territory Design.- Comprehensive Uncertainty with Applications.- Bilevel Recycling Facility Location.- Multi-Period Hub Location with Time Series.- Data-Driven Robust Production Planning.
About the Author :
Francisco Saldanha da Gama is the chair of supply chain management at Sheffield University Management School (UK) where he is also the head of the Operations Management and Decision Sciences Research Center. He has published extensively in scientific international journals mostly in the areas of location analysis, supply chain management, logistics, and combinatorial optimization. He is a member of various international scientific organizations such as the INFORMS, the European Chapter on Combinatorial Optimization, the Working Group on Stochastic Optimization, and the EURO Working Group on Locational Analysis, of which he is one of the past coordinators. He is the Editor-in-Chief of Computers & Operations Research as well as a member of the Editorial Advisory Board of the Journal of the Operational Research Society (UK), Operations Research Perspectives, International Journal of General Systems, and Algorithms. His research interests include Supply Chain Management, Logistics, Decision-Making under Uncertainty, Facility Location, and Project Scheduling and Management.
Shuming Wang is a professor of management science at the School of Economics and Management, University of Chinese Academy of Sciences (China). He received his PhD from Waseda University (Japan). His research interests include robust and stochastic optimization with statistical modeling, facility location, supply chain management, and healthcare analytics. His research has been published in internationally reputed journals such as Production and Operations Management, INFORMS Journal on Computing, and Transportation Science. He is an area editor of Computers & Operations Research and an associate editor of Decision Sciences Journal.
