Genetic based fuzzy goal programming for multiobjective chance constrained programming problems with continuous random variables

Solution procedure consisting of fuzzy goal programming and stochastic simulation-based genetic algorithm is presented, in this article, to solve multiobjective chance constrained programming problems with continuous random variables in the objective functions and in chance constraints. The fuzzy go...

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Veröffentlicht in:International journal of computer mathematics Jg. 83; H. 2; S. 171 - 179
Hauptverfasser: Jana, R. K., Biswal, M. P.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Taylor & Francis 01.02.2006
Schlagworte:
Chance Constrained Programming
Continuous random variable
Fuzzy goal programming
Genetic algorithm
Multiobjective programming
ISSN:0020-7160, 1029-0265
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Abstract Solution procedure consisting of fuzzy goal programming and stochastic simulation-based genetic algorithm is presented, in this article, to solve multiobjective chance constrained programming problems with continuous random variables in the objective functions and in chance constraints. The fuzzy goal programming formulation of the problem is developed first using the stochastic simulation-based genetic algorithm. Without deriving the deterministic equivalent, chance constraints are used within the genetic process and their feasibilities are checked by the stochastic simulation technique. The problem is then reduced to an ordinary chance constrained programming problem. Again using the stochastic simulation-based genetic algorithm, the highest membership value of each of the membership goal is achieved and thereby the most satisfactory solution is obtained. The proposed procedure is illustrated by a numerical example.
AbstractList Solution procedure consisting of fuzzy goal programming and stochastic simulation-based genetic algorithm is presented, in this article, to solve multiobjective chance constrained programming problems with continuous random variables in the objective functions and in chance constraints. The fuzzy goal programming formulation of the problem is developed first using the stochastic simulation-based genetic algorithm. Without deriving the deterministic equivalent, chance constraints are used within the genetic process and their feasibilities are checked by the stochastic simulation technique. The problem is then reduced to an ordinary chance constrained programming problem. Again using the stochastic simulation-based genetic algorithm, the highest membership value of each of the membership goal is achieved and thereby the most satisfactory solution is obtained. The proposed procedure is illustrated by a numerical example.
Author Biswal, M. P.
Jana, R. K.
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  fullname: Biswal, M. P.
  organization: Department of Mathematics , Indian Institute of Technology
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Cites_doi 10.1017/CBO9780511559402
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StartPage 171
SubjectTerms Chance Constrained Programming
Continuous random variable
Fuzzy goal programming
Genetic algorithm
Multiobjective programming
Title Genetic based fuzzy goal programming for multiobjective chance constrained programming problems with continuous random variables
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