Multiobjective random fuzzy linear programming problems based on the possibility maximization model

Takashi Hasuike, Hideki Katagiri, Hiroaki Ishii

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

Two multiobjective random fuzzy programming problems considered based on the possibility maximization model using possibilistic and stochastic programming are not initially well defined due to the random variables and fuzzy numbers included. To solve them analytically, probability criteria are set for objective functions and chance constraints are introduced. Taking into account the decision maker's subjectivity and the original plan's flexibility, a fuzzy goal is introduced for each objective function. The original problems are then changed into deterministic equivalent problems to make the possibility fractile optimization problem equivalent to a linear programming problem. The possibility maximization problem for probability is changed into a nonlinear programming problem, and an analytical solution is constructed extending previous solution approaches.

Original languageEnglish
Pages (from-to)373-379
Number of pages7
JournalJournal of Advanced Computational Intelligence and Intelligent Informatics
Volume13
Issue number4
Publication statusPublished - 2009
Externally publishedYes

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Linear programming
Stochastic programming
Nonlinear programming
Random variables

Keywords

  • Multiobjective model
  • Random fuzzy programming
  • Stochastic and fuzzy programming

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Human-Computer Interaction

Cite this

Multiobjective random fuzzy linear programming problems based on the possibility maximization model. / Hasuike, Takashi; Katagiri, Hideki; Ishii, Hiroaki.

In: Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol. 13, No. 4, 2009, p. 373-379.

Research output: Contribution to journalArticle

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