Facility location under uncertainty: models, algorithms and applications

By:

Saldanha-da-gama, Francisco

Contributor(s):

Wang, Shuming

Material type: Text

TextSeries: International Series in Operations Research & Management Science (ISOR, volume 356)Publication details: Springer Cham 2024Description: xvi, 534 pISBN:

9783031559266

Subject(s):

Algorithms and applications

DDC classification:

658.403 SAL

Summary: 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. (https://link.springer.com/book/10.1007/978-3-031-55927-3)

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Holdings
Item type	Current library	Collection	Call number	Copy number	Status	Date due	Barcode
Book	Indian Institute of Management LRC General Stacks	Operations Management & Quantitative Techniques	658.403 SAL (Browse shelf(Opens below))	1	Available		007884

Table of contents:
Front Matter
Pages i-xvi
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Introduction
Francisco Saldanha-da-Gama, Shuming Wang
Pages 1-7
Principles and Background
Front Matter
Pages 9-9
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Discrete Facility Location Problems
Francisco Saldanha-da-Gama, Shuming Wang
Pages 11-36
Decision-Making Under Uncertainty: Ingredients for Modeling
Francisco Saldanha-da-Gama, Shuming Wang
Pages 37-50
Optimization Under Uncertainty
Francisco Saldanha-da-Gama, Shuming Wang
Pages 51-92
Modeling Paradigms and Solution Techniques
Front Matter
Pages 93-93
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Robust Facility Location
Francisco Saldanha-da-Gama, Shuming Wang
Pages 95-121
Stochastic Facility Location
Francisco Saldanha-da-Gama, Shuming Wang
Pages 123-179
Facility Location with Chance Constraints
Francisco Saldanha-da-Gama, Shuming Wang
Pages 181-201
Distributionally Robust Facility Location
Francisco Saldanha-da-Gama, Shuming Wang
Pages 203-226
Extended Models
Front Matter
Pages 227-227
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Location-Inventory Problems
Francisco Saldanha-da-Gama, Shuming Wang
Pages 229-254
Facility Location with Routing Decisions
Francisco Saldanha-da-Gama, Shuming Wang
Pages 255-291
Hub Location
Francisco Saldanha-da-Gama, Shuming Wang
Pages 293-369
Logistics and Supply Chain Management
Francisco Saldanha-da-Gama, Shuming Wang
Pages 371-413
Territory Design
Francisco Saldanha-da-Gama, Shuming Wang
Pages 415-436
Comprehensive Uncertainty with Applications
Front Matter
Pages 437-437
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Bilevel Recycling Facility Location
Francisco Saldanha-da-Gama, Shuming Wang
Pages 439-469
Multi-Period Hub Location with Time Series
Francisco Saldanha-da-Gama, Shuming Wang
Pages 471-488
(https://link.springer.com/book/10.1007/978-3-031-55927-3)

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.

(https://link.springer.com/book/10.1007/978-3-031-55927-3)

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