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

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 p...

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Bibliographic Details
Published in:International journal of system assurance engineering and management Vol. 11; no. 1; pp. 93 - 109
Main Authors: Abebaw Gessesse, Adane, Mishra, Rajashree, Acharya, Mitali Madhumita, Das, Kedar Nath
Format: Journal Article
Language:English
Published: New Delhi Springer India 01.02.2020
Springer Nature B.V
Subjects:
Engineering
Engineering Economics
Genetic algorithms
Logistics
Marketing
Mathematical programming
Multiple objective analysis
Organization
Original Article
Parameters
Probabilistic methods
Production scheduling
Quality Control
Reliability
Safety and Risk
Transportation problem
90C32
Genetic algorithm
90C29
Fuzzy programming
90C15
Multi-objective programming
Fractional transportation problem
90C08
Stochastic programming
90C70
ISSN:0975-6809, 0976-4348
Online Access:Get full text
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Summary: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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ISSN:0975-6809
0976-4348
DOI:10.1007/s13198-019-00928-0