03704nam 22005655 450 991104922540332120260114034900.0981-9560-35-710.1007/978-981-95-6035-6(CKB)44770015700041(MiAaPQ)EBC32470842(Au-PeEL)EBL32470842(DE-He213)978-981-95-6035-6(EXLCZ)994477001570004120260102d2026 u| 0engur|||||||||||txtrdacontentcrdamediacrrdacarrierUncertain Multi-objective Decision Making: Methods and Industrial Models /by Hamed Fazlollahtabar1st ed. 2026.Singapore :Springer Nature Singapore :Imprint: Springer,2026.1 online resource (471 pages)Engineering Series981-9560-34-9 Fuzzy Multi-Objective Optimization by α-cut method -- Fuzzy Multi-Objective Optimization by utility-based maximum technique -- Integrated Fuzzy PROMETHEE and Fuzzy linear Multi-Objective Program -- Fuzzy Multi-Objective AHP-TOPSIS Method -- Fuzzy Multi-Objective Mathematical Programming using Ranking Method -- Vague Multi-Objective Optimization by Branch and Bound method -- Possible Vague Multi-Objective Optimization in Queue System -- Possible Vague Multi-Objective Optimization in Queue System -- Rough Multi-Objective Optimization using Best-Worst method -- Multi-Objective Possibility Theory -- Bayesian Multi-Objective Optimization -- Stochastic Multi-Objective Optimization -- Artificial Intelligence Application for Multi-Objective Optimization.This book explains multi-objective optimization as an area of multicriteria decision making that deals with mathematical optimization problems involving more than one objective function that must be optimized simultaneously. Multi-objective optimization is used in many fields of science, including engineering, economics, and logistics, where there is a need to make optimal decisions in the presence of trade-offs between two or more conflicting objectives. Uncertain optimization refers to contexts where there is uncertainty in models and data. It potentially has various applications in different domains such as portfolio selection, inventory management, pollution reduction, sustainable development, resource allocation and reallocation, and performance analysis. The book encompasses various types of uncertainty in decision making namely fuzziness, possibility, Bayesian, stochastic, roughness, vagueness, and artificial intelligence and develops application areas in industrial cases. It includes 12 chapters presenting multiobjective decision models under one of the uncertainty types. In each chapter an implementation study is illustrated to show the applicability of he model.Engineering SeriesMathematical optimizationStatisticsLogic, Symbolic and mathematicalOptimizationStatistical Theory and MethodsMathematical Logic and FoundationsMathematical optimization.Statistics.Logic, Symbolic and mathematical.Optimization.Statistical Theory and Methods.Mathematical Logic and Foundations.519.6Fazlollahtabar Hamed720818MiAaPQMiAaPQMiAaPQBOOK9911049225403321Uncertain Multi-objective Decision Making: Methods and Industrial Models4533912UNINA