Stochastic simulation-based genetic algorithm for chance constrained fractional programming problem

A. Udhayakumar, Vincent Charles and V. Rhymend Uthariaraj

International Journal of Operational Research, 2010, vol. 9, issue 1, 23-38

Abstract: The field of chance constrained fractional programming (CCFP) has grown into a huge area over the last few years because of its applications in real life problems. Therefore, finding a solution technique to it is of paramount importance. The solution technique so far has been deriving deterministic equivalence of CCFP with random coefficients in the objective function and/or constraints and is possible only if random variable follows some specified distribution with known parameters. This paper presents a stochastic simulation-based genetic algorithm (GA) for solving CCFP problems, where random variables used can follow any continuous distribution. The solution procedure is tested on a few numerical examples. The results demonstrate that the suggested approach could provide researchers a promising way for solving various types of chance constrained programming (CCP) problems.

Keywords: chance constraint programming; chance constrained fractional programming; genetic algorithms; GAs; stochastic programming; stochastic simulation; random variables; continuous distribution. (search for similar items in EconPapers)
Date: 2010
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Persistent link: https://EconPapers.repec.org/RePEc:ids:ijores:v:9:y:2010:i:1:p:23-38

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