A distance-based aggregation approach for group decision making with interval preference orderings |
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| Affiliation: | 1. State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, PR China;2. Business School, Hohai University, Jiangning, Nanjing, Jiangsu 211100, PR China;3. School of Management Science and Engineering, Qingdao University, Qingdao, 266071, PR China;4. School of Information, Zhejiang University of Finance & Economics, 18 Xueyuan Street, Hangzhou 210018, PR China;1. State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing, Jiangsu 210098, PR China;2. Business School, Hohai University, Nanjing, Jiangsu 211100, PR China;3. School of Information, Zhejiang University of Finance & Economics, 18 Xueyuan Street, Hangzhou 310018, PR China;1. School of Economics and Management, Zhejiang Normal University, Jinhua, Zhejiang, China;2. Centre for Computational Intelligence, Faculty of Technology, De Montfort University, Leicester, UK;3. DMU Interdisciplinary Group in Intelligent Transport Systems, Faculty of Technology, De Montfort University, Leicester, UK;1. School of Economics and Management, Southeast University, Nanjing, Jiangsu 211189, China;2. Business School, Sichuan University, Chengdu, Sichuan 610064, China;1. School of Computer and Information Science, Southwest University, Chongqing 400715, China;2. School of Hanhong, Southwest University, Chongqing 400715, China;3. Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, China;4. School of Engineering, University of British Columbia, 1137 Alumni Ave, Kelowna, BC V1V 1V7, Canada;5. School of Engineering, Vanderbilt University, Nashville, TN 37235, USA;6. School of Automation, Northwestern Polytechnical University, Xi’an, Shaanxi 710072, China |
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| Abstract: | Xu (2013) proposed a nonlinear programming model to derive an exact formula to determine the experts’ relative importance weights for the group decision making (GDM) with interval preference orderings. However, in this study, we show that the exact formula to determine the weight vector which always equals to w = (1/m, 1/m, … , 1/m)T (m is the number of experts). In this paper, we propose a distance-based aggregation approach to assess the relative importance weights for GDM with interval preference orderings. Relevant theorems are offered to support the proposed approach. After that, by using the weighted arithmetic averaging operator, we obtain the aggregated virtual interval preference orderings. We propose a possibility degree formula to compare two virtual interval preference orderings, then rank and select the alternatives. The proposed method is tested by two numerical examples. Comparative analysis are provided to show the advantages and effectiveness of the proposed method. |
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| Keywords: | GDM Interval preference orderings Weights Possibility degree Distance |
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