Hesitant Trapezoidal Fuzzy QUALIFLEX Method and Its Application in the Evaluation of Green Supply Chain Initiatives

Abstract

This paper explores how to handle multiple criteria decision-making (MCDM) problems in which the criteria values of alternatives take the form of comparative linguistic expressions. Firstly, the new concept of hesitant trapezoidal fuzzy numbers (HTrFNs) is provided to model the semantics of the comparative linguistic expressions. Then, the operational laws and the distance measures of HTrFNs are presented. Afterwards, a useful outranking method, the hesitant trapezoidal fuzzy QUALIFLEX method, is developed to handle the MCDM problems with hierarchical structure in the environment of HTrFN. At length, the proposed method is applied to evaluating green supply chain initiatives in order to achieve sustainable economic and environmental performance, and a case study concerned with a fashion retail chain is presented to demonstrate its feasibility and applicability, also, a comparative analysis with other relevant approaches is conducted to validate the effectiveness of the proposed method.

A Generalization of Trapezoidal Fuzzy Numbers Based on Modal Interval Theory

“…Among these methods, we will pay special attention to the following: the CODAS method (Combinative Distance-based Assessment) [42], QUALIFLEX method (QUALItative FLEXible) [43], ELECTRE method (ELimination Et Choix Traduisant la REalité) [44,45], VIKOR method (VlseKriterijumska Optimizacija I Kompromisno Resenje) [46,47], MULTIMOORA method (Multiple Objective Optimization on the basis of Ratio Analysis) [48], EAMRIT Method (Evaluation by an Area-based Method of Ranking Interval Type-2 Fuzzy sets) [49], TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution [50], EDAS Method (Evaluation based on Distance from Average Solution) [51], AFRAW Method (Assessment-based on Fuzzy Ranking and Aggregated Weights) [52], TEDE Method (Total Effective Dose Equivalent) [53], and WASPAS (Weighted Aggregated Sum Product ASsessment) [54]. The extension of these methods to the field of MITFNs can provide new tools, from the point of view of both the calculations, as well as from the interpretative point of view.…”

Green Supplier Evaluation and Selection Using Cloud Model Theory and the QUALIFLEX Method

“…Chen [35] suggested an interval-valued intuitionistic fuzzy QUALIFLEX approach using likelihood-based comparison method to resolve MCDM issues. In addition, other meaningful extensions of the QUALIFLEX technique in previous studies include the interval-valued intuitionistic fuzzy QUALIFLEX [36], the interval type-2 fuzzy QUALIFLEX [37], and the hesitant trapezoidal fuzzy QUALIFLEX [38].…”

Closeness Degree-Based Hesitant Trapezoidal Fuzzy Multicriteria Decision Making Method for Evaluating Green Suppliers with Qualitative Information

“…Although TrFNs can well capture the vagueness of single linguistic term assessments, they fail to represent the vagueness of FLEs. By integrated hesitant fuzzy elements into TrFNs, Zhang et al [17] recently provided a novel notion of the HTrF value for representing the vagueness of FLEs based on hesitant fuzzy LTSs.…”

“…For instance, the evaluator may utilize FLEs like "between middle and high" to express his/her opinion when assessing the delivery capacity of a green supplier. The use of FLEs to present the uncertain 2 Discrete Dynamics in Nature and Society qualitative information greatly increases the accuracy of the assignment of criteria weights and criteria values in the evaluating process [17]. Although many extension forms of TOPSIS approach, including hesitant fuzzy TOPSIS [18], intuitionistic fuzzy TOPSIS [9], Pythagorean fuzzy TOPSIS [19], interval-valued intuitionistic fuzzy TOPSIS [20,21], hesitant fuzzy linguistic TOPSIS [22], and so on, have recently been developed to handle the MCDM issues with different fuzzy environments, these existing techniques fail to deal with the MCDM issues where the decision data are represented by FLEs.…”

“…Although many extension forms of TOPSIS approach, including hesitant fuzzy TOPSIS [18], intuitionistic fuzzy TOPSIS [9], Pythagorean fuzzy TOPSIS [19], interval-valued intuitionistic fuzzy TOPSIS [20,21], hesitant fuzzy linguistic TOPSIS [22], and so on, have recently been developed to handle the MCDM issues with different fuzzy environments, these existing techniques fail to deal with the MCDM issues where the decision data are represented by FLEs. To this end, this paper attempts to develop a new closeness degree-based hesitant trapezoidal fuzzy (HTrF) MCDM method which absorbs the advantages of HTrF values and inherits the feature of the classical TOPSIS approach simultaneously [17].…”