Nonlinear fuzzy chance constrained approach for multi-objective mixed fuzzy-stochastic optimization problem

Fuzzy set theory currently has a wide range of applications to model real-world issues with ambiguous or incomplete information, which to some extent captures reality. On the other hand stochastic environment also deals with uncertainties with different approach (probability distribution). In order...

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Veröffentlicht in:Opsearch Jg. 61; H. 1; S. 121 - 136
Hauptverfasser: kumar, Ajeet, Mishra, Babita
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New Delhi Springer India 01.03.2024
Schlagworte:
Application Article
Business and Management
Management
Mathematics
Operations Research/Decision Theory
C44
C69
Fuzzy chance constrained programming problem
Fuzzy random variable
Multi-objective mixed fuzzy stochastic programming problem
Fuzzy mean and Fuzzy variance
Nonlinear membership function
C73
C61
ISSN:0030-3887, 0975-0320
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Beschreibung
Zusammenfassung:Fuzzy set theory currently has a wide range of applications to model real-world issues with ambiguous or incomplete information, which to some extent captures reality. On the other hand stochastic environment also deals with uncertainties with different approach (probability distribution). In order to deal with decision problems involving more than one objective, where the parameters and the objectives both are uncertain, the mixed fuzzy stochastic programming approach have been introduced. In this paper, a new solution named as fuzzy stochastic pareto optimal solution is defined. Here we have developed an iterative method for the decision making of a multi-objective optimization problem in the fuzzy stochastic environment. Further a numerical illustration of the developed methodology has been given and the superiority of the proposed method has been established by comparing the obtained results with some well known existing methods.
ISSN:0030-3887
0975-0320
DOI:10.1007/s12597-023-00699-0