TY  - BOOK
AU  - Saldanha-da-Gama,Francisco
AU  - Wang,Shuming
ED  - SpringerLink (Online service)
TI  - Facility Location Under Uncertainty: Models, Algorithms and Applications 
T2  - International Series in Operations Research & Management Science,
SN  - 9783031559273
AV  - TS155-194 
PY  - 2024///
CY  - Cham
PB  - Springer International Publishing, Imprint: Springer
KW  - Production management
KW  - Operations research
KW  - Management science
KW  - Mathematical optimization
KW  - Business logistics
KW  - Operations Management
KW  - Operations Research and Decision Theory
KW  - Operations Research, Management Science
KW  - Optimization
KW  - Supply Chain Management
KW  - Logistics
N1  - 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
N2  - 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, and location inventory
UR  - https://doi.org/10.1007/978-3-031-55927-3
ER  -