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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| Vydáno v: | International journal of computer mathematics Ročník 81; číslo 12; s. 1455 - 1463 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Taylor & Francis
01.12.2004
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| ISSN: | 0020-7160, 1029-0265 |
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| Abstract | 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 |
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| AbstractList | 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. 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 |
| Author | Biswal, M. P. Jana, R. K. |
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| Cites_doi | 10.1016/S0377-2217(97)90319-2 10.1287/opre.11.1.18 10.1287/mnsc.9.3.405 10.1287/mnsc.6.1.73 10.1287/opre.13.6.930 10.1287/opre.24.6.1078 10.1002/9780470316511 10.1007/BF01584661 10.1080/0020716042000272584 10.1080/02522667.1996.10699291 10.2307/1910956 10.1016/0096-3003(87)90009-9 |
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| SubjectTerms | Chance constraint Discrete random variables Genetic algorithm Stochastic programming Stochastic simulation |
| Title | Stochastic simulation based genetic algorithm for chance constraint programming problems with some discrete random variables |
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