A Multi-Attribute Group Decision-Making Method Based on Linguistic Intuitionistic Fuzzy Numbers and Dempster–Shafer Evidence Theory

Peide Liu (), Xiaoxiao Liu, Guiying Ma, Zhaolong Liang, Changhai Wang and Fawaz E. Alsaadi
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Peide Liu: School of Management Science and Engineering, Shandong University of Finance and Economics, Jinan, Shandong, China
Xiaoxiao Liu: School of Management Science and Engineering, Shandong University of Finance and Economics, Jinan, Shandong, China
Guiying Ma: #x2020;Price Monitoring Center in Shandong Province, Jinan, Shandong, China
Zhaolong Liang: #x2020;Price Monitoring Center in Shandong Province, Jinan, Shandong, China
Changhai Wang: #x2020;Price Monitoring Center in Shandong Province, Jinan, Shandong, China
Fawaz E. Alsaadi: #x2021;Department of Information Technology, Faculty of Computing and IT, King Abdulaziz University, Jeddah, Saudi Arabia

International Journal of Information Technology & Decision Making (IJITDM), 2020, vol. 19, issue 02, 499-524

Abstract: In this paper, we propose a multi-attribute group decision-making (MAGDM) method based on Dempster–Shafer Evidence Theory (DST) and linguistic intuitionistic fuzzy numbers (LIFNs), in which both the expert weights and attribute weights are unknown. Firstly, we represent LIFNs as basic probability assignments (BPAs) by DST based on linguistic scale function (LSF), and a linear programming model is proposed to combine the objective weights and subjective weights of attributes to obtain the combined weights. At the same time, the experts’ weights are obtained through Jousselme distance. Secondly, we use the weights to correct the evidence, and the comprehensive evaluation value of each alternative is calculated by the combination rule of evidence. Further, a new MAGDM approach with DST and LIFNs is presented. Finally, we give an example to explain the proposed method and compare it with other methods to show the feasibility and superiority.

Keywords: Linguistic intuitionistic fuzzy numbers; evidence theory; combination weight; MAGDM (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1142/S0219622020500042

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