A Novel Approach for Multiplicative Linguistic Group Decision Making Based on Symmetrical Linguistic Chi-Square Deviation and VIKOR Method

Abstract

Most linguistic-based approaches to multi-attribute group decision making (MAGDM) use symmetric, uniformly distributed sets of additive linguistic terms to express the opinions of decision makers. However, in reality, there are also some problems that require the use of asymmetric, uneven, i.e., non-equilibrium, multiplicative linguistic term sets to express the evaluation. The purpose of this paper is to propose a new approach to MAGDM under multiplicative linguistic information. The aggregation of linguistic data is an important component in MAGDM. To solve this problem, we define a chi-square for measuring the difference between multiplicative linguistic term sets. Furthermore, the linguistic generalized weighted logarithm multiple averaging (LGWLMA) operator and linguistic generalized ordered weighted logarithm multiple averaging (LGOWLMA) operator are proposed based on chi-square deviation. On the basis of the proposed two operators, we develop a novel approach to GDM with multiplicative linguistic term sets. Finally, the evaluation of transport logistics enterprises is developed to illustrate the validity and practicality of the proposed approach.

2-Tuple Linguistic q-Rung Orthopair Fuzzy Power MSM Approach for Choosing Sustainable Waste Disposal Technology

“…In the proposed method, we rank the waste disposal technologies using different values of parameter q. First, we establish the ranking of the alternatives when the values of q vary (q = 1, 2, 3,5,7,9,11,13,15,17,19,21) (suppose κ = 3). Table 6 shows the ranking results for different values of q based on 2TLq-ROFPWMSM operator.…”

“…Kumar and Chen [20] developed an MAGDM approach based on the advanced linguistic IF weighted aggregation operator (AO) of linguistic IF numbers. Gong et al [21] presented a novel method for MAGDM with multiplicative linguistic data. Concretely, owing to its symbolic translation, the 2TL representation model presented by Herrera and Martinez [22], stands out for its versatility, accuracy, and willingness to perform precise linguistic computations without any assumptions.…”