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001 978-3-031-55927-3
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020 _a9783031559273
_9978-3-031-55927-3
024 7 _a10.1007/978-3-031-55927-3
_2doi
050 4 _aTS155-194
072 7 _aKJMV
_2bicssc
072 7 _aBUS087000
_2bisacsh
072 7 _aKJMV
_2thema
100 1 _aSaldanha-da-Gama, Francisco.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_978177
245 1 0 _aFacility Location Under Uncertainty
_h[electronic resource] :
_bModels, Algorithms and Applications /
_cby Francisco Saldanha-da-Gama, Shuming Wang.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2024.
300 _aXVI, 534 p. 22 illus., 9 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aInternational Series in Operations Research & Management Science,
_x2214-7934 ;
_v356
505 0 _aIntroduction -- 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 _aThis 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.
650 0 _aProduction management.
_967141
650 0 _aOperations research.
_957943
650 0 _aManagement science.
_957941
650 0 _aMathematical optimization.
_965522
650 0 _aBusiness logistics.
_961225
650 1 4 _aOperations Management.
_967304
650 2 4 _aOperations Research and Decision Theory.
_967143
650 2 4 _aOperations Research, Management Science.
_965527
650 2 4 _aOptimization.
_965528
650 2 4 _aSupply Chain Management.
_966371
650 2 4 _aLogistics.
_968350
700 1 _aWang, Shuming.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_978178
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031559266
776 0 8 _iPrinted edition:
_z9783031559280
776 0 8 _iPrinted edition:
_z9783031559297
830 0 _aInternational Series in Operations Research & Management Science,
_x2214-7934 ;
_v356
_969480
856 4 0 _uhttps://doi.org/10.1007/978-3-031-55927-3
912 _aZDB-2-BUM
912 _aZDB-2-SXBM
999 _c60480
_d60480