CLC number: N949
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
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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.
@article{title="A vague-set-based fuzzy multi-objective decision making model for bidding purchase",
author="WANG Zhou-jing, QIAN Edward Y.",
journal="Journal of Zhejiang University Science A",
volume="8",
number="4",
pages="644-650",
year="2007",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2007.A0644"
}
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%T A vague-set-based fuzzy multi-objective decision making model for bidding purchase
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%A QIAN Edward Y.
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%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2007.A0644
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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
Y1 - 2007
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DOI - 10.1631/jzus.2007.A0644
Abstract: 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.
[1] Bozdag, C.E., Kahraman, C., Ruan, D., 2003. Fuzzy group decision making for selection among computer integrated manufacturing systems. Computers in Industry, 51(1):13-29.
[2] Chen, S.M., Tan, J.M., 1994. Handling multi-criteria fuzzy decision-making problems elements. Fuzzy Sets Syst., 67(2):163-172.
[3] Chen, C.T., 2000. Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets Syst., 114(1):1-9.
[4] Chen, Y., Li, K.W., Levy, J., Hipel, K.W., Kilgour, D.M., 2006. Rough-set multiple-criteria ABC analysis. Lect. Notes Artif. Intell., 4259:328-337.
[5] Gau, W.L., Buehrer, D.J., 1993. Vague sets. IEEE Trans. Syst. Man Cybern., 23(2):610-614.
[6] Hong, D.H., Kim, C., 1999. A note on similarity measures between vague sets and between elements. Inf. Sci., 115(1-4):83-96.
[7] Hong, D.H., Choi, C.H., 2000. Multicriteria fuzzy decision-making problems based on vague set theory. Fuzzy Sets Syst., 114(1):103-113.
[8] Leung, L.C., Cao, D., 2000. On consistency and ranking of alternatives in fuzzy AHP. Eur. J. Operational Res., 124(1):102-113.
[9] Li, F., Lu, A., Cai, L.J., 2001. Muti-objective decision-making based on vague set. J. Huazhong Univ. Sci. Technol., 29(7):1-3 (in Chinese).
[10] Li, K.W., Karray, F., Hipel, K.W., Kilgour, D.M., 2001. Fuzzy approaches to the game of chicken. IEEE Trans. Fuzzy Syst., 9(4):608-623.
[11] Li, D.F., 2002. Fuzzy Multi-objective Many-person Decision Makings and Games. National Defense Industry Press, Beijing, China, p.88-93 (in Chinese).
[12] Liu, H.W., 2004. Vague set methods of multicriteria fuzzy decision making. Syst. Eng. Theory Practice, 24(5):103-109.
[13] Liu, Y.L., Lv, X.L., Tian, J.F., 2005. Value engineering assessment method in bidding purchase of equipments. Construction Economy, 12:56-58.
[14] Lo, C.C., Wang, P., Chao, K.M., 2006. Solving consensus measure of ambiguous GDM problems using vague sets—an application of risk assessment. LNCS, 3865:573-585.
[15] Lu, A., Ng, W., 2005. Vague sets or intuitionistic fuzzy sets for handling vague data: which one is better? LNCS, 3716:401-416.
[16] Paek, J.H., Lee, Y.W., Napier, T.R., 1992. Selection of design/build proposed using fuzzy logic system. J. Construction Engineering and Management, 118(2):303-317.
[17] Polkowski, L., 2006. A set theory for rough sets: toward a formal calculus of vague statements. Fundamenta Informaticae, 71(1):49-61.
[18] Skowron, A., 2005. Rough sets and vague concepts. Fundamenta Informaticae, 64(1-4):417-431.
[19] Szmidt, E., Kacprzyk, J., 2002. Evaluation of Agreement in a Group of Experts via Distance between Intuitionistic Fuzzy Preference. International IEEE Symposium “Intelligent Systems”, 1:166-170.
[20] Wu, H.Q., 2004. On the bidding decision model based on evidential reasoning. Systems Engineering, 22(11):90-93 (in Chinese).
[21] Wu, S.R., 2004. Purchasing Management in Project. Publishing Company of Machine Industry, Beijing, p.1-10 (in Chinese).
[22] Yan, X.S., Liu, F.Z., Xi, Y.F., 2002. Application of TOPSIS comprehensive evaluation in computer-aided bidding purchase system. J. Xi’an Univ. Archit. & Technol., 34(2):191-194 (in Chinese).
[23] Zadeh, L.A., 1965. Fuzzy sets. Information and Control, 8(3):338-356.
[24] Zhu, Z., Liu, Z.R., Wang, K.F., 2001. AHP method in choosing project contractor. J. Zhejiang Univ. (Natural Science), 35(5):567-571 (in Chinese).
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