Fuzzy UTASTAR: A method for discovering utility functions from fuzzy data

Patiniotakis, I; Apostolou, D; Mentzas, G

dc.contributor.author	Patiniotakis, I	en
dc.contributor.author	Apostolou, D	en
dc.contributor.author	Mentzas, G	en
dc.date.accessioned	2014-03-01T01:35:45Z
dc.date.available	2014-03-01T01:35:45Z
dc.date.issued	2011	en
dc.identifier.issn	0957-4174	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/21182
dc.subject	Fuzzy linear programming	en
dc.subject	Fuzzy numbers	en
dc.subject	Multiple criteria decision making	en
dc.subject	UTASTAR	en
dc.subject	Utility functions	en
dc.subject.classification	Computer Science, Artificial Intelligence	en
dc.subject.classification	Engineering, Electrical & Electronic	en
dc.subject.classification	Operations Research & Management Science	en
dc.subject.other	Fuzzy linear programming	en
dc.subject.other	Fuzzy numbers	en
dc.subject.other	Multiple criteria decision making	en
dc.subject.other	UTASTAR	en
dc.subject.other	Utility functions	en
dc.subject.other	Artificial intelligence	en
dc.subject.other	Decision making	en
dc.subject.other	Decision support systems	en
dc.subject.other	Fuzzy sets	en
dc.subject.other	Function evaluation	en
dc.title	Fuzzy UTASTAR: A method for discovering utility functions from fuzzy data	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.eswa.2011.06.014	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.eswa.2011.06.014	en
heal.language	English	en
heal.publicationDate	2011	en
heal.abstract	We propose Fuzzy UTASTAR, a method for inferring fuzzy utility functions from a partial preorder of options evaluated on multiple criteria. It is an extension of the well-known UTASTAR method capable to handle both ordinary (crisp) and fuzzy evaluation data. This property gives much flexibility to decision makers because the majority of real-life decision problems involve a considerable level of uncertainty that hinders them from assigning exact evaluations (scores) to options. In case all evaluation data are crisp the method behaves exactly as the original UTASTAR. The proposed method builds fuzzy additive value functions taking as input a partial preorder on a subset of the options, called reference set, along with their associated scores on the criteria. The resulting fuzzy utility functions can subsequently be used to estimate the (fuzzy) utility of each option, thus allowing their ranking, prioritization, selection or classification. The ranking of the options in partial preorder is as compatible as possible to the original one. The method is implemented into a decision support system and is applied to an example from the transportation domain. Results are found to be in concordance with those of the original method. To the best of our knowledge this is the first attempt to extend UTASTAR method to handle both crisp and fuzzy evaluation data. (C) 2011 Elsevier Ltd. All rights reserved.	en
heal.publisher	PERGAMON-ELSEVIER SCIENCE LTD	en
heal.journalName	Expert Systems with Applications	en
dc.identifier.doi	10.1016/j.eswa.2011.06.014	en
dc.identifier.isi	ISI:000295193400117	en
dc.identifier.volume	38	en
dc.identifier.issue	12	en
dc.identifier.spage	15463	en
dc.identifier.epage	15474	en