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このアイテムの引用には次の識別子を使用してください: http://hdl.handle.net/10119/4660

タイトル: A Satisfactory-Oriented Approach to Multiexpert Decision-Making With Linguistic Assessments
著者: Huynh, Van-Nam
Nakamori, Yoshiteru
キーワード: Decision making
linguistic hierarchies
linguistic variables
multigranular linguistic contexts
randomly linguistic preferences
satisfactory principle
発行日: 2005-04
出版者: Institute of Electrical and Electronics Engineers (IEEE)
誌名: IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
巻: 35
号: 2
開始ページ: 184
終了ページ: 196
DOI: 10.1109/TSMCB.2004.842248
抄録: This paper proposes a multiexpert decision-making (MEDM) method with linguistic assessments, making use of the notion of random preferences and a so-called satisfactory principle. It is well known that decision-making problems that manage preferences from different experts follow a common resolution scheme composed of two phases: an aggregation phase that combines the individual preferences to obtain a collective preference value for each alternative; and an exploitation phase that orders the collective preferences according to a given criterion, to select the best alternative/s. For our method, instead of using an aggregation operator to obtain a collective preference value, a random preference is defined for each alternative in the aggregation phase. Then, based on a satisfactory principle defined in this paper, that says that it is perfectly satisfactory to select an alternative as the best if its performance is as at least "good" as all the others under the same evaluation scheme, we propose a linguistic choice function to establish a rank ordering among the alternatives. Moreover, we also discuss how this linguistic decision rule can be applied to the MEDM problem in multigranular linguistic contexts. Two application examples taken from the literature are used to illuminate the proposed techniques.
Rights: Copyright (c)2005 IEEE. Reprinted from IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 35(2), 2005, 184-196. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of JAIST's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.
URI: http://hdl.handle.net/10119/4660
資料タイプ: publisher
出現コレクション:a10-1. 雑誌掲載論文 (Journal Articles)

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