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Journal of Zhejiang University SCIENCE A 2007 Vol.8 No.4 P.644~650


A vague-set-based fuzzy multi-objective decision making model for bidding purchase

Author(s):  WANG Zhou-jing, QIAN Edward Y.

Affiliation(s):  Department of Automation, Xiamen University, Xiamen 361005, China; more

Corresponding email(s):   wangzj@xmu.edu.cn

Key Words:  Fuzzy multi-objective decision making model, Vague set, Score function, Lower bound of satisfaction, Upper bound of dissatisfaction

WANG Zhou-jing, QIAN Edward Y.. A vague-set-based fuzzy multi-objective decision making model for bidding purchase[J]. Journal of Zhejiang University Science A, 2007, 8(4): 644~650.

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publisher="Zhejiang University Press & Springer",

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%A WANG Zhou-jing
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%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2007.A0644

T1 - A vague-set-based fuzzy multi-objective decision making model for bidding purchase
A1 - WANG Zhou-jing
A1 - QIAN Edward Y.
J0 - Journal of Zhejiang University Science A
VL - 8
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SP - 644
EP - 650
%@ 1673-565X
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.2007.A0644

A vague-set-based fuzzy multi-objective decision making model is developed for evaluating bidding plans in a bidding purchase process. A group of decision-makers (DMs) first independently assess bidding plans according to their experience and preferences, and these assessments may be expressed as linguistic terms, which are then converted to fuzzy numbers. The resulting decision matrices are then transformed to objective membership grade matrices. The lower bound of satisfaction and upper bound of dissatisfaction are used to determine each bidding plan’s supporting, opposing, and neutral objective sets, which together determine the vague value of a bidding plan. Finally, a score function is employed to rank all bidding plans. A new score function based on vague sets is introduced in the model and a novel method is presented for calculating the lower bound of satisfaction and upper bound of dissatisfaction. In a vague-set-based fuzzy multi-objective decision making model, different valuations for upper and lower bounds of satisfaction usually lead to distinct ranking results. Therefore, it is crucial to effectively contain DMs’ arbitrariness and subjectivity when these values are determined.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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