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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.
List of contents
Fuzzy Multi-Objective Optimization by -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.
About the author
Hamed Fazlollahtabar earned a BSc and an MSc in industrial engineering from Mazandaran University of Science and Technology, Babol, Iran, in 2008 and 2010, respectively. He received his Ph.D. in Industrial and Systems Engineering from the Iran University of Science and Technology, Tehran, Iran, in 2015 and has completed a postdoctoral research fellowship at Sharif University of Technology, Tehran, Iran, in the area of reliability engineering for complex systems from October 2016 to March 2017. He joined the Department of Industrial Engineering at Damghan University, Damghan, Iran, in June 2017, and currently is working as an Associate Professor. He is a member of Iran Elites foundation. He has been chosen as Exemplary Researcher of Iran in all Engineering Diciplines, 2022 and Distinguished Young Industrial Engineering Researcher, Iran Academy of Science. 2023. He has been listed as world top 2% scientists in 2021, 2022, 2023 and 2024. His research interests are in uncertain decision making and analytics, industry 4.0+ production systems, reliability engineering, and sustainable supply chain planning.
Summary
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.
