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

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.