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

Author

Listed:
  • Ajeet kumar

    (Mahatma Gandhi Central University)

  • Babita Mishra

    (Mahatma Gandhi Central University)

Abstract

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.

Suggested Citation

  • Ajeet kumar & Babita Mishra, 2024. "Nonlinear fuzzy chance constrained approach for multi-objective mixed fuzzy-stochastic optimization problem," OPSEARCH, Springer;Operational Research Society of India, vol. 61(1), pages 121-136, March.
    • Handle: RePEc:spr:opsear:v:61:y:2024:i:1:d:10.1007_s12597-023-00699-0
    DOI: 10.1007/s12597-023-00699-0
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    More about this item

    Keywords

    Multi-objective mixed fuzzy stochastic programming problem; Fuzzy chance constrained programming problem; Fuzzy random variable; Fuzzy mean and Fuzzy variance; Nonlinear membership function;
    All these keywords.

    JEL classification:

    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C69 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Other
    • C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games

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