Robust Optimization by Fuzzy Linear Programming

Masahiro Inuiguchi ()
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Masahiro Inuiguchi: Osaka University

A chapter in Managing Safety of Heterogeneous Systems, 2012, pp 219-239 from Springer

Abstract: Abstract In this paper, the possibilistic linear programming approaches, fuzzy linear programming approaches under uncertain coefficients, are reviewed in connection with robust optimization. The ideas of optimizing approaches and satisficing approaches are described rather than their technical and methodological aspects. Because the former has not been introduced in many papers, we describe the former more than the latter. As optimizing approaches, the concepts of possibly and necessarily optimal solutions are given. Among those, necessarily optimal solutions are robustly optimal solutions. However, it is frequent that such a solution does not exists. Then necessarily soft-optimal solutions are introduced. As satisficing approaches, the treatments based on necessity measures are introduced. Necessity measure optimization model, necessity fractile optimization model and symmetric model are described. Finally, the paper is concluded with giving some promising future topics.

Keywords: Fuzzy Number; Linear Programming Problem; Robust Optimization; Possibility Distribution; Possibility Theory (search for similar items in EconPapers)
Date: 2012
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnechp:978-3-642-22884-1_11

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DOI: 10.1007/978-3-642-22884-1_11

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