Approach to Multicriteria Group Decision Making with Z‐Numbers Based on TOPSIS and Power Aggregation Operators

Abstract

Decisions strongly rely on information, so the information must be reliable, yet most of the real-world information is imprecise and uncertain. The reliability of the information about decision analysis should be measured. Z-number, which incorporates a restraint of evaluation on investigated objects and the corresponding degree of confidence, is considered as a powerful tool to characterize this information. In this paper, we develop a novel approach based on TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method and the power aggregation operators for solving the multiple criteria group decision making (MCGDM) problem where the weight information for decision makers (DMs) and criteria is incomplete. In the MCGDM, the evaluation information made by DMs is represented in the form of linguistic terms and the following calculation is performed using Z-numbers. First, we establish an optimization model based on similarity measure to determine the weights of DMs and a linear programming model with partial weight information provided by DMs based on distance measure to determine the weights of criteria. Subsequently, decision matrices from all the DMs are aggregated into a comprehensive evaluation matrix utilizing the proposed ZWAPA operator or ZWGPA operator. Then, those considered alternatives are ranked in accordance with TOPSIS idea and the feature of Z-evaluation. Finally, a practical example about supplier selection is given to demonstrate the detailed implementation process of the proposed approach, and the feasibility and validity of the approach are verified by comparisons with some existing approaches.

An Integrated Approach for Renewable Energy Resource and Plant Location Selection

“…This method has been successfully used in various areas, requiring multi-criteria decision making. The fuzzy version of the TOPSIS, introduced in (Chen, 2000) and elaborated in the follow-up research and publications (Chu, (2002); Mahdavi et al, 2008;Wang and Lee, 2009;Kaya and Kahraman (2011); Madi et al, 2015;Nădăban et al, 2016;Palczewski and Sałabun, 2019), laid down a methodological foundation for applications of the Z-number based versions of the TOPSIS (Yaakob and Gegov, 2015;Krohling et al, 2016;Wang and Mao, 2019).…”

Defuzzification of Intuitionistic Z-Numbers for Fuzzy Multi Criteria Decision Making

“…Adopting the evaluations made by three decision makers from [14], the authors have the set of evaluations in form of TrIZNs. Next, the TrIZNs are transformed into TrIFNs.…”

A rough‐Z‐number‐based DEMATEL to evaluate the co‐creative sustainable value propositions for smart product‐service systems

“…Recently, the Z‐number has gained popularity in MCDM due to its strength in the uncertainty and objectivity characterization during the evaluation process 22,23 24 …”

“…DEMATEL, rough-Z-number, smart product-service systems (smart PSS), sustainable value propositions, value co-creation Recently, the Z-number has gained popularity in MCDM due to its strength in the uncertainty and objectivity characterization during the evaluation process. 22,23 For example, a Z-number extended DEMATEL is developed to identify key performance indicators in large group decision-making scenarios. 24 However, the aggregation of Z-numbers remains an open problem under the group decision-making environment.…”