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

Takashi Hasuike, Hiroaki Ishii

研究成果: Conference contribution

7 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトルApplications of Soft Computing
ホスト出版物のサブタイトルFrom Theory to Praxis
出版社Springer Verlag
ページ285-294
ページ数10
ISBN(印刷版)9783540896180
DOI
出版ステータスPublished - 2009
外部発表はい

出版物シリーズ

名前Advances in Intelligent and Soft Computing
58
ISSN(印刷版)1867-5662

ASJC Scopus subject areas

  • Computer Science(all)

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