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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 2/2014

01.04.2014 | Original Article

Random fuzzy bilevel linear programming through possibility-based value at risk model

verfasst von: Hideki Katagiri, Takeshi Uno, Kosuke Kato, Hiroshi Tsuda, Hiroe Tsubaki

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 2/2014

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Abstract

This article considers bilevel linear programming problems where random fuzzy variables are contained in objective functions and constraints. In order to construct a new optimization criterion under fuzziness and randomness, the concept of value at risk and possibility theory are incorporated. The purpose of the proposed decision making model is to optimize possibility-based values at risk. It is shown that the original bilevel programming problems involving random fuzzy variables are transformed into deterministic problems. The characteristic of the proposed model is that the corresponding Stackelberg problem is exactly solved by using nonlinear bilevel programming techniques under some convexity properties. A simple numerical example is provided to show the applicability of the proposed methodology to real-world hierarchical problems.
Vorheriger Artikel Sparse group LASSO based uncertain feature selection
Nächster Artikel Reconstruction of surgical instruments in virtual surgery system

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Metadaten
Titel
Random fuzzy bilevel linear programming through possibility-based value at risk model
verfasst von
Hideki Katagiri
Takeshi Uno
Kosuke Kato
Hiroshi Tsuda
Hiroe Tsubaki
Publikationsdatum
01.04.2014
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 2/2014
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-012-0126-4

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