A random fuzzy programming models based on possibilistic programming

Hideki Katagiri, Takashi Hasuike, Hiroaki Ishii

Research output: Contribution to journalConference article

13 Citations (Scopus)

Abstract

This paper considers linear programming problems where each coefficient of the objective function is expressed by a random fuzzy variable. New decision making models are proposed based on stochastic and possibilistic programming in order to maximize both of possibility and probability with respect to the objective function value. It is shown that each of the proposed models is transformed into a deterministic equivalent one. Solution algorithms using convex programming techniques and/or the bisection method are provided for obtaining an optimal solution of each model.

Original languageEnglish
Article number4811548
Pages (from-to)1788-1793
Number of pages6
JournalConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
DOIs
Publication statusPublished - 2008 Dec 1
Externally publishedYes
Event2008 IEEE International Conference on Systems, Man and Cybernetics, SMC 2008 - Singapore, Singapore
Duration: 2008 Oct 122008 Oct 15

Keywords

  • Linear program
  • Random fuzzy programming models
  • Random fuzzy variable

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Human-Computer Interaction

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