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Fuzzy-Like Multiple Objective Multistage Decision Making (Studies in Computational Intelligence, 533, Band 533) - Hardcover

Xu, Jiuping; Zeng, Ziqiang

 
9783319033976: Fuzzy-Like Multiple Objective Multistage Decision Making (Studies in Computational Intelligence, 533, Band 533)
Hardcover
ISBN 10: 3319033972 ISBN 13: 9783319033976
Verlag: Springer, 2014

Inhaltsangabe

Decision has inspired reflection of many thinkers since the ancient times. With the rapid development of science and society, appropriate dynamic decision making has been playing an increasingly important role in many areas of human activity including engineering, management, economy and others. In most real-world problems, decision makers usually have to make decisions sequentially at different points in time and space, at different levels for a component or a system, while facing multiple and conflicting objectives and a hybrid uncertain environment where fuzziness and randomness co-exist in a decision making process. This leads to the development of fuzzy-like multiple objective multistage decision making. This book provides a thorough understanding of the concepts of dynamic optimization from a modern perspective and presents the state-of-the-art methodology for modeling, analyzing and solving the most typical multiple objective multistage decision making practical application problems under fuzzy-like uncertainty, including the dynamic machine allocation, closed multiclass queueing networks optimization, inventory management, facilities planning and transportation assignment. A number of real-world engineering case studies are used to illustrate in detail the methodology. With its emphasis on problem-solving and applications, this book is ideal for researchers, practitioners, engineers, graduate students and upper-level undergraduates in applied mathematics, management science, operations research, information system, civil engineering, building construction and transportation optimization

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Über die Autorin bzw. den Autor

This book is the result of a longstanding collaboration between two leading scholars. Professor Asaf Hajiyev, a full member of the Azerbaijan National Academy of Sciences, is renowned for his work in probability theory, mathematical statistics, and stochastic modeling. He has authored over 150 scientific papers and several books, and currently heads the Department of Statistical Modeling at the Institute of Control Systems. Professor Jiuping Xu, Distinguished Professor at Sichuan University and academician of several international academies, specializes in foundational modeling for computer science and management science. He has led over 80 national research projects and published more than 900 papers and 40 books. Together, their teams have developed innovative methodologies to evaluate the stability of AI interpretability using regression-based confidence regions. Their interdisciplinary partnership, active since 2010, combines theoretical rigor with practical insight to support robust and explainable decision-making in artificial intelligence.

Von der hinteren Coverseite

Decision has inspired reflection of many thinkers since the ancient times. With the rapid development of science and society, appropriate dynamic decision making has been playing an increasingly important role in many areas of human activity including engineering, management, economy and others. In most real-world problems, decision makers usually have to make decisions sequentially at different points in time and space, at different levels for a component or a system, while facing multiple and conflicting objectives and a hybrid uncertain environment where fuzziness and randomness co-exist in a decision making process. This leads to the development of fuzzy-like multiple objective multistage decision making. This book provides a thorough understanding of the concepts of dynamic optimization from a modern perspective and presents the state-of-the-art methodology for modeling, analyzing and solving the most typical multiple objective multistage decision making practical application problems under fuzzy-like uncertainty, including the dynamic machine allocation, closed multiclass queueing networks optimization, inventory management, facilities planning and transportation assignment. A number of real-world engineering case studies are used to illustrate in detail the methodology. With its emphasis on problem-solving and applications, this book is ideal for researchers, practitioners, engineers, graduate students and upper-level undergraduates in applied mathematics, management science, operations research, information system, civil engineering, building construction and transportation optimization

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Verlag
Springer
Erscheinungsdatum
2014
Sprache
Englisch
ISBN 10 
3319033972
ISBN 13 
9783319033976
Einband
Gebundene Ausgabe
Anzahl der Seiten
402
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