Robust expectation optimization model using the possibility measure for the fuzzy random programming problem

Takashi Hasuike, Hiroaki Ishii

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

This paper considers an expectation optimization model using a possibility measure to the objective function in the fuzzy random programming problem, based on possibilistic programming and stochastic programming. The main fuzzy random programming problem is not a well-defined problem due to including random variables and fuzzy numbers. Therefore, in order to solve it analytically, a criterion for goal of objective function is set and the chance constraint are introduced. Then, considering decision maker's subjectivity and flexibility of the original plan, a fuzzy goal for each objective function is introduced. Furthermore, this paper considers that the occurrence probability of each scenario has ambiguity, and is represented as an interval value. Considering this interval of probability, a robust expectation optimization problem is proposed. Main problem is transformed into the deterministic equivalent linear programming problem, and so the analytical solution method extending previous solution approaches is constructed.

Original languageEnglish
Title of host publicationAdvances in Intelligent and Soft Computing
PublisherSpringer Verlag
Pages285-294
Number of pages10
Volume58
ISBN (Print)9783540896180
Publication statusPublished - 2009
Externally publishedYes

Publication series

NameAdvances in Intelligent and Soft Computing
Volume58
ISSN (Print)18675662

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ASJC Scopus subject areas

  • Computer Science(all)

Cite this

Hasuike, T., & Ishii, H. (2009). Robust expectation optimization model using the possibility measure for the fuzzy random programming problem. In Advances in Intelligent and Soft Computing (Vol. 58, pp. 285-294). (Advances in Intelligent and Soft Computing; Vol. 58). Springer Verlag.