Solving multi-objective probabilistic factional programming problem

This paper presents the solution methodology of a multi-objective probabilistic fractional programming problem. In the proposed model the parameters in the constraints coefficient and the right-hand sides of the constraints follow continuous random variables having known distribution. Since the prog...

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Bibliographic Details
Published in:	International journal of engineering and advanced technology Vol. 8; no. 6s3; pp. 897 - 903
Main Authors:	Belay, Berhanu, Acharya, Srikumar, Mishra, Rajshree
Format:	Journal Article
Language:	English
Published:	22.11.2019
ISSN:	2249-8958, 2249-8958
Online Access:	Get full text
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Summary:	This paper presents the solution methodology of a multi-objective probabilistic fractional programming problem. In the proposed model the parameters in the constraints coefficient and the right-hand sides of the constraints follow continuous random variables having known distribution. Since the programming problem consists of random variables, multi-objective function and fractional objective function, it is lengthy, time-consuming and clumsy to solve the proposed programming problem using analytical methods. Stochastic simulation-based genetic algorithm approach is directly applied to solve multi-objective probabilistic non-linear fractional programming problem involving beta distribution and chi-square distribution. In the proposed method, it is not necessary to find the deterministic equivalent of a probabilistic programming problem and applying any traditional methods of fractional programming problem. The stochastic simulation-based genetic algorithm is coded by Code block C++ 16.01 compiler. A set of Pareto optimal solutions are generated for a multi objective probabilistic non-linear fractional programming problem. A numerical example and case study on inventory problem are presented to validate the proposed method.
ISSN:	2249-8958 2249-8958
DOI:	10.35940/ijeat.F1162.0986S319