Iterative Solution Process for Multiple Objective Stochastic Linear Programming Problems Under Fuzzy Environment

This article presents one interactive algorithm, and thereby determines the Pareto optimal solution to multi-objective stochastic linear programming (MOSLP) problems in real-life oriented fuzzy environment. Among the various objective functions, there always exists one objective function, referred t...

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Veröffentlicht in:Fuzzy information and engineering Jg. 10; H. 4; S. 435 - 451
Hauptverfasser: Garai, Arindam, Mandal, Palash, Roy, Tapan Kumar
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Abingdon Taylor & Francis 02.10.2018
Taylor & Francis Group
Schlagworte:
Algorithms
chance-constrained programming
Decision making
expectation model
Fuzzy logic
fuzzy multi-objective optimisation
Fuzzy sets
Iterative methods
Iterative solution
Linear programming
Main objective function
Optimization
Pareto optimum
stochastic optimisation
trade-off ratio
ISSN:1616-8658, 1616-8666
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Zusammenfassung:This article presents one interactive algorithm, and thereby determines the Pareto optimal solution to multi-objective stochastic linear programming (MOSLP) problems in real-life oriented fuzzy environment. Among the various objective functions, there always exists one objective function, referred to as the main objective function in this article, to multi-objective models, whose optimal value is most vital to decision-makers. When the optimal value to main objective function meets the pre-determined aspiration level, and the corresponding values to other objective functions are satisfactory in nature, that Pareto optimal solution is acceptable to decision-makers. Again, in several existing interactive fuzzy optimisation methods to MOSLP models, all reference membership levels of expectations to objective functions are considered as a unity. However, this seems to be less rational that the expectation of each conflicting objective function simultaneously attains the individual goal. So, the present article proposes to employ the trade-off ratios of membership functions to analytically determine reference membership levels in a fuzzy environment. Numerical applications further illustrate this algorithm. Finally, conclusions are drawn.
Bibliographie:ObjectType-Article-1
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ISSN:1616-8658
1616-8666
DOI:10.1080/16168658.2020.1750871