Premium
Multicriteria group decision making under incomplete preference judgments: Using fuzzy logic with a linguistic quantifier
Author(s) -
Choi Duke Hyun,
Ahn Byeong Seok,
Kim Soung Hie
Publication year - 2007
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.20218
Subject(s) - vagueness , group decision making , preference , quantifier (linguistics) , computer science , fuzzy logic , selection (genetic algorithm) , artificial intelligence , aggregate (composite) , group (periodic table) , mathematics , machine learning , management science , statistics , psychology , social psychology , economics , materials science , chemistry , organic chemistry , composite material
Abstract In the face of increasing global competition and complexity of the socioeconomic environment, many organizations employ groups in decision making. Inexact or vague preferences have been discussed in the decision‐making literature with a view to relaxing the burden of preference specifications imposed on the decision makers and thus taking into account the vagueness of human judgment. In this article, we present a multiperson decision‐making method using fuzzy logic with a linguistic quantifier when each group member specifies incomplete judgment possibly both in terms of the evaluation of the performance of different alternatives with respect to multiple criteria and on the criteria themselves. Allowing for incomplete judgment in the model, however, makes a clear selection of the best alternative by the group more difficult. So, further interactions with the decision makers may proceed to the extent to compensate for the initial comfort of preference specifications. These interactions, however, may not guarantee the selection of the best alternative to implement. To circumvent this deadlock situation, we present a procedure for obtaining a satisfactory solution by the use of a linguistic‐quantifier‐guided aggregation that implies the fuzzy majority. This is an approach that combines a prescriptive decision method via mathematical programming and a well‐established approximate solution method to aggregate multiple objects. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 641–660, 2007.