Affiliations: [a] School of Management, Hefei University of Technology, Hefei, Anhui, P.R. China | [b] Key Laboratory of Process Optimization and Intelligent Decision-making, Ministry of Education, Hefei, Anhui, P.R. China | [c] School of Economics and Management, Shanghai Maritime University, Shanghai, P.R. China | [d] School of Economics and Management, Zhejiang Normal University, Jinhua, Zhejiang, Province, P.R. China
Correspondence:
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Corresponding authors. Xiaoan Tang, School of Management, Hefei University of Technology, Hefei, Box 270, Hefei 230009, Anhui, P.R. China. Tel.: +86 0551 62904930; Fax: +86 0551 62905263; E-mail: [email protected] and Jian Wu, School of Economics and Management, Zhejiang Normal University, Jinhua 321004, Zhejiang, Province, P.R. China. Tel./Fax: +21 38282400; E-mail: [email protected].
Abstract: This article proposes a new fuzzy MAGDM method based on expert reliability and the evidential reasoning (ER) rule in intuitionistic fuzzy environments. First, we develop an objective method to measure the reliability of each expert in a group, with the use of two sets of intuitionistic fuzzy assessments, i.e., original assessments and updated assessments provided after group discussion. Next, the ER rule is used to combine expert assessments with expert weights and the resulting reliabilities, and further to combine group assessments on each attribute for each alternative. Expert reliabilities and expert weights are taken into account simultaneously. To produce interval-valued aggregated assessments of alternatives, pairs of optimization problems are constructed. Then, an overall priority degree of each alternative with respect to other alternatives is calculated in accordance with the transformed intuitionistic fuzzy assessments of the aggregated assessments of alternatives. An alternative with larger overall priority degree is more preferred to other alternatives. Finally, a new campus site selection problem of one key university in Anhui Province of China is solved by using the proposed method as a practical example to demonstrate its detailed implementation process, validity, and applicability.
