Stochastic simulation-based genetic algorithm for chance constrained fractional programming problem
type: Item
instance of: scholarly article
title: Stochastic simulation-based genetic algorithm for chance constrained fractional programming problem
zbMATH Open document ID: 1200.90159
DOI: 10.1504/IJOR.2010.034359
author: A. Udhayakumar
author: Vincent Charles
author: V. Rhymend Uthariaraj
published in: International Journal of Operational Research
publication date: 2010-11-12T00:00:00Z
review text: Summary: 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.
Mathematics Subject Classification ID: 90C32
Mathematics Subject Classification ID: 90C15
Mathematics Subject Classification ID: 90C59
zbMATH DE Number: 5815555
zbMATH Keywords: chance constraint programming
zbMATH Keywords: chance constrained fractional programming
zbMATH Keywords: genetic algorithms
zbMATH Keywords: gas
zbMATH Keywords: stochastic programming
zbMATH Keywords: stochastic simulation
zbMATH Keywords: random variables
zbMATH Keywords: continuous distribution
MaRDI profile type: Publication
description: scientific article; zbMATH DE number 5815555