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.
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