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

Authors: A. Udhayakumar, Vincent Charles, V. Rhymend Uthariaraj

Addresses: Department of Computer Applications, Hindustan University, Chennai 603 103, Tamilnadu, India. ' CENTRUM Catolica, Pontificia Universidad Catolica del Peru, Santiago de Surco, Peru. ' Department of Computer Science and Engineering, Ramanujan Computing Center, Anna University, Chennai 600 025, Tamilnadu, India

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.

DOI: 10.1504/IJOR.2010.034359

International Journal of Operational Research, 2010 Vol.9 No.1, pp.23 - 38

Published online: 01 Aug 2010 *

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