Improved TOPSIS Method Considering Fuzziness and Randomness in Multi-Attribute Group Decision Making

Mei Cai and Yuanyuan Hong ()
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Mei Cai: School of Management Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China
Yuanyuan Hong: Research Center of Risk Management and Emergency Decision Making, School of Management Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China

Mathematics, 2022, vol. 10, issue 22, 1-16

Abstract: Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is a commonly used decision model in multi-attribute group decision making (MAGDM), and a probabilistic linguistic term set (PLTS) is the linguistic variable that can effectively express the fuzziness of decision makers’ (DMs’) preference. However, in actual decision use, PLTS type decision preference needs to be processed before use, which can distort the decision results. The randomness of DM’s preference which also affects the final decision making is often ignored. Therefore, in order to better serve the MAGDM problem, this paper proposes an asymmetric probabilistic linguistic cloud TOPSIS (ASPLC-TOPSIS) method. First, the basic theories of linguistic variables and cloud model (CM) are introduced. Second, the conversation model between linguistic variables and CM is defined along with the operation formula of ASPLC. Third, considering the importance of the DMs’ subjective weights, a DM trust network is established to calculate the DMs’ weights. Finally, the decision process of ASPLC-TOPSIS is proposed and the superiority of this method is proved through experimental studies.

Keywords: TOPSIS; multi-attribute group decision-making; cloud model (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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