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Fuzzy Multi-Objective Programming With Joint Probability Distribution

Fuzzy Multi-Objective Programming With Joint Probability Distribution

Copyright: © 2019 |Pages: 33
ISBN13: 9781522583011|ISBN10: 1522583017|ISBN13 Softcover: 9781522592969|EISBN13: 9781522583028
DOI: 10.4018/978-1-5225-8301-1.ch007
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MLA

Animesh Biswas and Arnab Kumar De. "Fuzzy Multi-Objective Programming With Joint Probability Distribution." Multi-Objective Stochastic Programming in Fuzzy Environments, IGI Global, 2019, pp.263-295. https://doi.org/10.4018/978-1-5225-8301-1.ch007

APA

A. Biswas & A. De (2019). Fuzzy Multi-Objective Programming With Joint Probability Distribution. IGI Global. https://doi.org/10.4018/978-1-5225-8301-1.ch007

Chicago

Animesh Biswas and Arnab Kumar De. "Fuzzy Multi-Objective Programming With Joint Probability Distribution." In Multi-Objective Stochastic Programming in Fuzzy Environments. Hershey, PA: IGI Global, 2019. https://doi.org/10.4018/978-1-5225-8301-1.ch007

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Abstract

In this chapter, a fuzzy goal programming (FGP) model is employed for solving multi-objective linear programming (MOLP) problem under fuzzy stochastic uncertain environment in which the probabilistic constraints involves fuzzy random variables (FRVs) following joint probability distribution. In the preceding chapters, the authors explain about linear, fractional, quadratic programming models with multiple conflicting objectives under fuzzy stochastic environment. But the chance constraints in these chapters are considered independently. However, in practical situations, the decision makers (DMs) face various uncertainties where the chance constraints occur jointly. By considering the above fact, the authors presented a solution methodology for fuzzy stochastic MOLP (FSMOLP) with joint probabilistic constraint following some continuous probability distributions. Like the other chapters, chance constrained programming (CCP) methodology is adopted for handling probabilistic constraints. But the difference is that in the earlier chapters chance constraints are considered independently, whereas in this chapter all the chance constraints are taken jointly. Then the transformed problem involving possibilistic uncertainty is converted into a comparable deterministic problem by using the method of defuzzification of the fuzzy numbers (FNs). Objectives are now solved independently under the set of modified system constraints to obtain the best solution of each objective. Then the membership function for each objective is constructed, and finally, a fuzzy goal programming (FGP) model is developed for the achievement of the highest membership goals to the extent possible by minimizing group regrets in the decision-making context.

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