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Facility location under uncertaintymodels, algorithms and applications /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Facility location under uncertaintyby Francisco Saldanha-da-Gama, Shuming Wang.
其他題名:	models, algorithms and applications /
作者:	Saldanha da Gama, Francisco.
其他作者:	Wang, Shuming.
出版者:	Cham :Springer International Publishing :2024.
面頁冊數:	xvi, 534 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Industrial location.
電子資源:	https://doi.org/10.1007/978-3-031-55927-3
ISBN:	9783031559273$q(electronic bk.)

Facility location under uncertaintymodels, algorithms and applications /
Saldanha da Gama, Francisco.

Facility location under uncertaintymodels, algorithms and applications /[electronic resource] :by Francisco Saldanha-da-Gama, Shuming Wang. - Cham :Springer International Publishing :2024. - xvi, 534 p. :ill., digital ;24 cm. - International series in operations research & management science,v. 3562214-7934 ;. - International series in operations research & management science ;v. 140..

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.

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.

ISBN: 9783031559273$q(electronic bk.)

Standard No.: 10.1007/978-3-031-55927-3doiSubjects--Topical Terms:

187006
Industrial location.

LC Class. No.: HD58

Dewey Class. No.: 338.6042

Facility location under uncertaintymodels, algorithms and applications /
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505 0 $a 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.
520 $a 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.
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