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Multi-attribute group decision making model under the condition of uncertain information

Owing to the increasing complexity in the construction management, integrating experts' knowledge and experiences to make appropriate decisions is a commonly used method. TOPSIS (technique for order performance by similarity to ideal solution) is a practical and useful technique in dealing with...

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Published in:Automation in construction 2008-08, Vol.17 (6), p.792-797
Main Authors: Lin, Yong-Huang, Lee, Pin-Chan, Chang, Ta-Peng, Ting, Hsin-I
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Language:English
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cited_by cdi_FETCH-LOGICAL-c367t-4f64c8bd3ccb463a46890b6dd435bd1f1a10a28199e205997a76cd07d2db515e3
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creator Lin, Yong-Huang
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description Owing to the increasing complexity in the construction management, integrating experts' knowledge and experiences to make appropriate decisions is a commonly used method. TOPSIS (technique for order performance by similarity to ideal solution) is a practical and useful technique in dealing with multi-attribute decision making problems, and has been widely employed in the construction management and other fields. The modification and extension of TOPSIS to a group decision environment is investigated in this study. In the proposed group decision making model, we both adopt the Minkowski distance function to solve the over-weighted problem in the original TOPSIS technique, the grey number operations to deal with the problem of uncertain information, and the aggregation approach to integrate experts' evaluations. Finally, an illustrative example of subcontractor selection is used to demonstrate the feasibility and practicability of the proposed model.
doi_str_mv 10.1016/j.autcon.2008.02.011
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subjects Applied sciences
Buildings. Public works
Computation methods. Tables. Charts
Exact sciences and technology
Grey number
Group decision support
Minkowski distance function
Multi-attribute decision making
Project management. Process of design
Structural analysis. Stresses
TOPSIS
title Multi-attribute group decision making model under the condition of uncertain information
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