Fuzzy Multi-Criteria Optimization: Possibilistic and Fuzzy/Stochastic Approaches

Masahiro Inuiguchi (), Kosuke Kato () and Hideki Katagiri ()
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Masahiro Inuiguchi: Osaka University
Kosuke Kato: Hiroshima Institute of Technology
Hideki Katagiri: Hiroshima University

Chapter Chapter 20 in Multiple Criteria Decision Analysis, 2016, pp 851-902 from Springer

Abstract: Abstract In this chapter, we review fuzzy multi-criteria optimization focusing on possibilistic treatments of objective functions with fuzzy coefficients and on interactive fuzzy stochastic multiple objective programming approaches. In the first part, treatments of objective functions with fuzzy coefficients dividing into single objective function case and multiple objective function case. In single objective function case, multi-criteria treatments, possibly and necessarily optimal solutions, and minimax regret solutions are described showing the relations to multi-criteria optimization. In multiple objective function case, possibly and necessarily efficient solutions are investigated. Their properties and possible and necessary efficiency tests are shown. As one of interactive fuzzy stochastic programming approaches, multiple objective programming problems with fuzzy random parameters are discussed. Possibilistic expectation and variance models are proposed through incorporation of possibilistic and stochastic programming approaches. Interactive algorithms for deriving a satisficing solution of a decision maker are shown.

Keywords: Fuzzy programming; Possibility measure; Necessity measure; Minimax regret; Possible efficiency; Necessary efficiency; Fuzzy random variable; Random fuzzy variable; Interactive algorithm (search for similar items in EconPapers)
Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:spr:isochp:978-1-4939-3094-4_20

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DOI: 10.1007/978-1-4939-3094-4_20

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