Stochastic Versus Fuzzy Approaches to Multiobjective Mathematical Programming under Uncertainty

Stochastic Versus Fuzzy Approaches to Multiobjective Mathematical Programming under Uncertainty

Stochastic Versus Fuzzy Approaches to Multiobjective Mathematical Programming under Uncertainty

Stochastic Versus Fuzzy Approaches to Multiobjective Mathematical Programming under Uncertainty

Paperback(Softcover reprint of the original 1st ed. 1990)

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Overview

Operations Research is a field whose major contribution has been to propose a rigorous fonnulation of often ill-defmed problems pertaining to the organization or the design of large scale systems, such as resource allocation problems, scheduling and the like. While this effort did help a lot in understanding the nature of these problems, the mathematical models have proved only partially satisfactory due to the difficulty in gathering precise data, and in formulating objective functions that reflect the multi-faceted notion of optimal solution according to human experts. In this respect linear programming is a typical example of impressive achievement of Operations Research, that in its detenninistic fonn is not always adapted to real world decision-making : everything must be expressed in tenns of linear constraints ; yet the coefficients that appear in these constraints may not be so well-defined, either because their value depends upon other parameters (not accounted for in the model) or because they cannot be precisely assessed, and only qualitative estimates of these coefficients are available. Similarly the best solution to a linear programming problem may be more a matter of compromise between various criteria rather than just minimizing or maximizing a linear objective function. Lastly the constraints, expressed by equalities or inequalities between linear expressions, are often softer in reality that what their mathematical expression might let us believe, and infeasibility as detected by the linear programming techniques can often been coped with by making trade-offs with the real world.

Product Details

ISBN-13: 9789401074490
Publisher: Springer Netherlands
Publication date: 09/28/2011
Series: Theory and Decision Library D: , #6
Edition description: Softcover reprint of the original 1st ed. 1990
Pages: 427
Product dimensions: 6.10(w) x 9.25(h) x 0.04(d)

Table of Contents

I. The General Framework.- 1. Multiobjective programming under uncertainty : scope and goals of the book.- 2. Multiobjective programming : basic concepts and approaches.- 3. Shastic programming : numerical solution techniques by semi-shastic approximation methods.- 4. Fuzzy programming : a survey of recent developments.- II. The Shastic Approach.- 1. Overview of different approaches for solving shastic programming problems with multiple objective functions.- 2. “STRANGE” : an interactive method for multiobjective shastic linear programming, and “STRANGE-MOMIX” : its extension to integer variables.- 3. Application of STRANGE to energy studies.- 4. Multiobjective shastic linear programming with incomplete information : a general methodology.- 5. Computation of efficient solutions of shastic optimization problems with applications to regression and scenario analysis.- III. The Fuzzy Approach.- 1. Interactive decision-making for multiobjective programming problems with fuzzy parameters.- 2. A possibilistic approach for multiobjective programming problems. Efficiency of solutions.- 3. “FLIP” : an interactive method for multiobjective linear programming with fuzzy coefficients.- 4. Application of “FLIP” method to farm structure optimization under uncertainty.- 5. “FULPAL” : an interactive method for solving (multiobjective) fuzzy linear programming problems.- 6. Multiple objective linear programming problems in the presence of fuzzy coefficients.- 7. Inequality constraints between fuzzy numbers and their use in mathematical programming.- 8. Using fuzzy logic with linguistic quantifiers in multiobjective decision making and optimization: A step towards more human-consistent models.- IV. Shastic Versus Fuzzy Approaches and RelatedIssues.- 1. Shastic versus possibilistic multiobjective programming.- 2. A comparison study of “STRANGE” and “FLIP”.- 3. Multiobjective mathematical programming with inexact data.
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