NDLI: A fuzzy multiple attribute decision making method based on possibility degree

Content Provider	IEEE Xplore Digital Library
Author	Zhi-Ying, L.V. Xi-Nong Liang Xue-Zhang Liang Li-Wei Zheng
Copyright Year	2015
Description	Author affiliation: Manage. Fac., Chengdu Univ. of Inf. Technol., Chengdu, China (Zhi-Ying, L.V.) \|\| Coll. of Appl. Math., Chengdu Univ. of Inf. Technol., Chengdu, China (Li-Wei Zheng) \|\| Jilin Province Road Adm. Bur., Changchun, China (Xi-Nong Liang) \|\| Coll. of Math., Jilin Univ., Changchun, China (Xue-Zhang Liang)
Abstract	A fuzzy multiple attribute decision making method is investigated, in which the weights are given by interval numbers and the attribute values are given by the form of triangular fuzzy numbers or linguistic terms. A possibility degree formula for the comparison between two trapezoidal fuzzy numbers is proposed. According to the ordered weighted average (OWA) operator, an approach is presented to aggregate the possibility matrixes based on attributes and then the most desirable alternative is selected. This fuzzy multiple attribute decision making method is used in the field of financial investment evaluation, the set of attributes of the decision making program is built by analyzing of financial and accounting reports in the same industry. Finally a numerical example is provided to demonstrate the practicality and the feasibility of the proposed method.
Starting Page	450
Ending Page	454
File Size	255092
Page Count	5
File Format	PDF
e-ISBN	9781467376822
DOI	10.1109/FSKD.2015.7381984
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-08-15
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Industries Decision making trapezoidal fuzzy number Open wireless architecture OWA aggregation Operators investment options Indexes possibility degree Information technology multiple attribution decision making Investment Pragmatics
Content Type	Text
Resource Type	Article

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