Stochastic simulation based genetic algorithm for chance constraint programming problems with some discrete random variables

A stochastic simulation based genetic algorithm (GA) is presented, in this paper, for solving chance constraint programming problems in which the random variables follow some discrete distributions. The feasibility of the chance constraints is checked by stochastic simulation. In general, the feasib...

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Veröffentlicht in:International journal of computer mathematics Jg. 81; H. 12; S. 1455 - 1463
Hauptverfasser: Jana, R. K., Biswal, M. P.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Taylor & Francis 01.12.2004
Schlagworte:
Chance constraint
Discrete random variables
Genetic algorithm
Stochastic programming
Stochastic simulation
ISSN:0020-7160, 1029-0265
Online-Zugang:Volltext
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Zusammenfassung:A stochastic simulation based genetic algorithm (GA) is presented, in this paper, for solving chance constraint programming problems in which the random variables follow some discrete distributions. The feasibility of the chance constraints is checked by stochastic simulation. In general, the feasible region associate with such problems is non-convex. Therefore, GA is used to obtain the optimal solution. In the proposed method, the stochastic model is directly used without finding the deterministic equivalent of it. A numerical example is presented to prove the efficiency of the proposed method. E-mail: rabin@maths.iitkgp.ernet.in
Bibliographie:ObjectType-Article-2
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ISSN:0020-7160
1029-0265
DOI:10.1080/0020716042000272584