Fuzzy Stochastic Multiobjective Programming Fuzzy Stochastic Multiobjective Programming
International Series in Operations Research & Management Science

Fuzzy Stochastic Multiobjective Programming

Masatoshi Sakawa et autres
    • 159,99 $
    • 159,99 $

Description de l’éditeur

Although studies on multiobjective mathematical programming under uncertainty have been accumulated and several books on multiobjective mathematical programming under uncertainty have been published (e.g., Stancu-Minasian (1984); Slowinski and Teghem (1990); Sakawa (1993); Lai and Hwang (1994); Sakawa (2000)), there seems to be no book which concerns both randomness of events related to environments and fuzziness of human judgments simultaneously in multiobjective decision making problems. In this book, the authors are concerned with introducing the latest advances in the field of multiobjective optimization under both fuzziness and randomness on the basis of the authors’ continuing research works. Special stress is placed on interactive decision making aspects of fuzzy stochastic multiobjective programming for human-centered systems under uncertainty in most realistic situations when dealing with both fuzziness and randomness. Organization of each chapter is briefly summarized as follows:

Chapter 2 is devoted to mathematical preliminaries, which will be used throughout the remainder

of the book. Starting with basic notions and methods of multiobjective programming, interactive

fuzzy multiobjective programming as well as fuzzy multiobjective programming is outlined.

In Chapter 3, by considering the imprecision of decision maker’s (DM’s) judgment for stochastic

objective functions and/or constraints in multiobjective problems, fuzzy multiobjective stochastic

programming is developed.

In Chapter 4, through the consideration of not only the randomness of parameters involved in

objective functions and/or constraints but also the experts’ ambiguous understanding of the realized values of the random parameters, multiobjective programming problems with fuzzy random variables are formulated.

In Chapter 5, for resolving conflict of decision making problems in hierarchical managerial or

public organizations where there exist two DMs who have different priorities in making decisions, two-level programming problems are discussed.

Finally, Chapter 6 outlines some future research directions.

GENRE
Affaires et finances
SORTIE
2011
3 février
LANGUE
EN
Anglais
LONGUEUR
276
Pages
ÉDITEUR
Springer New York
VENDEUR
Springer Nature B.V.
TAILLE
6,9
 Mo
Numerical Nonsmooth Optimization Numerical Nonsmooth Optimization
2020
Introduction to Quantitative Macroeconomics Using Julia Introduction to Quantitative Macroeconomics Using Julia
2018
Techniques of Decision Making, Uncertain Reasoning and Regression Analysis Under the Hesitant Fuzzy Environment and Their Applications Techniques of Decision Making, Uncertain Reasoning and Regression Analysis Under the Hesitant Fuzzy Environment and Their Applications
2021
Advanced Optimization and Operations Research Advanced Optimization and Operations Research
2020
Perspectives on Operations Research Perspectives on Operations Research
2007
Linear Programming Models and Methods of Matrix Games with Payoffs of Triangular Fuzzy Numbers Linear Programming Models and Methods of Matrix Games with Payoffs of Triangular Fuzzy Numbers
2015
Linear and Multiobjective Programming with Fuzzy Stochastic Extensions Linear and Multiobjective Programming with Fuzzy Stochastic Extensions
2013
Cooperative and Noncooperative Multi-Level Programming Cooperative and Noncooperative Multi-Level Programming
2009
Planning Production and Inventories in the Extended Enterprise Planning Production and Inventories in the Extended Enterprise
2011
Perishable Inventory Systems Perishable Inventory Systems
2011
Profiles in Operations Research Profiles in Operations Research
2011
Linear Programming and Generalizations Linear Programming and Generalizations
2011
Supply Chain Engineering Supply Chain Engineering
2011
Portfolio Decision Analysis Portfolio Decision Analysis
2011