Reference : Knowledge-based Consistency Index for Fuzzy Pairwise Comparison Matrices

	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Computer science
	Focus Areas :	Computational Sciences
	To cite this reference:	http://hdl.handle.net/10993/31973

Title :	Knowledge-based Consistency Index for Fuzzy Pairwise Comparison Matrices
Language :	English
Author, co-author :	Kubler, Sylvain [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Derigent, William [Université de Lorraine > Centre de Recherche en Automatique de Nancy]
	Voisin, Alexandre [Université de Lorraine > Centre de Recherche en Automatique de Nancy]
	Robert, Jérémy [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Le Traon, Yves [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Publication date :	10-Jul-2017
Main document title :	Knowledge-based Consistency Index for Fuzzy Pairwise Comparison Matrices
Peer reviewed :	Yes
On invitation :	No
Audience :	International
Event name :	IEEE International Conference on Fuzzy Systems
Event date :	from 09-07-2017 to 12-07-2017
Event place (city) :	Naples
Event country :	Italy
Keywords :	[en] Analytic hierarchy process ; Fuzzy logic ; Multiple criteria decision-making ; Consistency ; Decision analysis
Abstract :	[en] Abstract—Fuzzy AHP is today one of the most used Multiple Criteria Decision-Making (MCDM) techniques. The main argument to introduce fuzzy set theory within AHP lies in its ability to handle uncertainty and vagueness arising from decision makers (when performing pairwise comparisons between a set of criteria/alternatives). As humans usually reason with granular information rather than precise one, such pairwise comparisons may contain some degree of inconsistency that needs to be properly tackled to guarantee the relevance of the result/ranking. Over the last decades, several consistency indexes designed for fuzzy pairwise comparison matrices (FPCMs) were proposed, as will be discussed in this article. However, for some decision theory specialists, it appears that most of these indexes fail to be properly “axiomatically” founded, thus leading to misleading results. To overcome this, a new index, referred to as KCI (Knowledge-based Consistency Index) is introduced in this paper, and later compared with an existing index that is axiomatically well founded. The comparison results show that (i) both indexes perform similarly from a consistency measurement perspective, but (ii) KCI contributes to significantly reduce the computation time, which can save expert’s time in some MCDM problems.
Target :	Researchers
Permalink :	http://hdl.handle.net/10993/31973
Other URL :	https://www.fuzzieee2017.org/sessionM10-7.html#
Framework Programme / European Project :	H2020 ; 688203 - bIoTope - Building an IoT OPen innovation Ecosystem for connected smart objects
FnR project :	FnR ; FNR9095399 > Sylvain Kubler > IoT4CaBEHM > Internet Of Things For Context-Aware Building Energy & Health Management > 01/07/2015 > 30/06/2017 > 2014

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