Handling multicriteria fuzzy decision-making problems based on vague set theory

Chen, Shyi‐Ming; Tan, Jinglu

doi:10.1016/0165-0114(94)90084-1

Cited by 1,032 publications

(447 citation statements)

References 14 publications

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“…The score function was first introduced and used by Chen and Tan [6] to measure a vague value for multi-criteria fuzzy decision making. In this case, given a Vague Set (VS) [13], the score function was defined as the degree of membership minus the degree of non-membership for such type of set.…”

Section: A Ranking Approaches For Intuitionistic Fuzzy Setsmentioning

confidence: 99%

Choice degrees in decision-making: A comparison between intuitionistic and fuzzy preference relations approaches

Chiclana

Ureña

Herrera‐Viedma

2016

2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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Abstract-Preference modelling based on Atanassov's intuitionistic fuzzy sets are gaining increasing relevance in the field of group decision making as they provide experts with a flexible and simple tool to express their preferences on a set of alternative options, while allowing, at the same time, to accommodate experts' preference uncertainty, which is inherent to all decision making processes. A key issue within this framework is the provision of efficient methods to rank alternatives, from best to worse, taking into account the peculiarities that this type of preference representation format presents. In this contribution we analyse the relationships between the main method proposed and used by researchers to rank alternatives using intuitionistic fuzzy sets, the score degree function, and the well known choice degree based on Orlovsky's non-dominance concept for the case when the preferences are expressed by means of fuzzy preference relations. This relationship study will provide the necessary theoretical results to support the implementation of Orlovsky's non-dominance concept to define the fuzzy quantifier guided non-dominance choice degree for intuitionistic fuzzy preference relations.

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Section: A Ranking Approaches For Intuitionistic Fuzzy Setsmentioning

confidence: 99%

Choice degrees in decision-making: A comparison between intuitionistic and fuzzy preference relations approaches

Chiclana

Ureña

Herrera‐Viedma

2016

2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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“…The ex post evaluation uses fuzzy logic (Barrett and Pattanaik 1989;Carlsson and Fuller 1996;Andriantiatsaholiniaina and others 2004;Prato 2005Prato , 2009 This approach helps decision-makers solve complex decision-making problems in a systematic, consistent and productive way [A18] and has been widely applied to tackle DM problems with multiple criteria and alternatives [27]. In short, fuzzy set theory offers a mathematically precise way of modeling vague preferences for example when it comes to setting weights of performance scores on criteria.…”

Section: Referencesmentioning

confidence: 99%

Fuzzy multiple criteria decision making: Recent developments

Carlsson

Fuller

1996

Fuzzy Sets and Systems

413

129

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Multiple Criteria Decision Making (MCDM) shows signs of becoming a maturing field. There are four quite distinct families of methods: (i) the outranking, (ii) the value and utility theory based, (iii) the multiple objective programming, and (iv) group decision and negotiation theory based methods. Fuzzy MCDM has basically been developed along the same lines, although with the help of fuzzy set theory a number of innovations have been made possible; the most important methods are reviewed and a novel approach -interdependence in MCDM -is introduced.

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“…Last years, the use of Atanassov's intuitionistic fuzzy sets in decision making has been widely studied [1,2,3,4,5,6,7]. In these works, the evaluations provided by experts are given in terms of intuitionistic values.…”

Section: Introductionmentioning

confidence: 99%

Weighted Voting Method Using Atanassov's Intuitionistic Fuzzy Preference Relations and Ignorance Functions

Paternain¹,

Jurío²,

Barrenechea³

et al. 2011

Proceedings of the 7th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-2011)

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In this work we present a method for constructing an Atanassov's intuitionistic fuzzy preference relation from a fuzzy preference relation that takes into account the ignorance of the expert when evaluating the preferences in the latter. Moreover, we present two algorithms that make use of this method for extending the weighted voting method to the Atanassov's intuitionistic fuzzy setting in decision making problems.

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Handling multicriteria fuzzy decision-making problems based on vague set theory

Cited by 1,032 publications

References 14 publications

Choice degrees in decision-making: A comparison between intuitionistic and fuzzy preference relations approaches

Choice degrees in decision-making: A comparison between intuitionistic and fuzzy preference relations approaches

Fuzzy multiple criteria decision making: Recent developments

Weighted Voting Method Using Atanassov's Intuitionistic Fuzzy Preference Relations and Ignorance Functions

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