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Genetic algorithm based fuzzy programming approach for multi-objective linear fractional stochastic transportation problem involving four-parameter Burr distribution

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Abstract

In real life situations, it is difficult to handle multi-objective linear fractional stochastic transportation problem. It can’t be solved directly using mathematical programming approaches. In this paper, a solution procedure is proposed for the above problem using a genetic algorithm based fuzzy programming method. The supply and demand parameters of the said problem follow four-parameters Burr distribution. The proposed approach omits the derivation of deterministic equivalent form as required in case of classical approach. In the given methodology, initially the probabilistic constraints present in the problem are tackled using stochastic programming combining the strategy adopted in genetic algorithm. Throughout the problem, feasibility criteria is maintained. Then after, the non-dominated solution are obtained using genetic algorithm based fuzzy programming approach. In the proposed approach, the concept of fuzzy programming approach is inserted in the genetic algorithm cycle. The proposed algorithm has been compared with fuzzy programming approach and implemented on two examples. The result shows the efficacy of the proposed algorithm over fuzzy programming approach.

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Authors and Affiliations

  1. Department of Mathematics, KIIT, Deemed to be University, Bhubaneswar, Odisha, 751024, India

    Adane Abebaw Gessesse, Rajashree Mishra & Mitali Madhumita Acharya

  2. Department of Mathematics, NIT Silchar, Silchar, Assam, 788010, India

    Kedar Nath Das

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  1. Adane Abebaw Gessesse
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  2. Rajashree Mishra
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  3. Mitali Madhumita Acharya
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  4. Kedar Nath Das
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Correspondence to Adane Abebaw Gessesse.

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Abebaw Gessesse, A., Mishra, R., Acharya, M.M. et al. Genetic algorithm based fuzzy programming approach for multi-objective linear fractional stochastic transportation problem involving four-parameter Burr distribution. Int J Syst Assur Eng Manag 11, 93–109 (2020). https://doi.org/10.1007/s13198-019-00928-0

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