Extended VIKOR-QUALIFLEX Method Based on Trapezoidal Fuzzy Two-Dimensional Linguistic Information for Multiple Attribute Decision-Making with Unknown Attribute Weight

An integrated group decision-making method under q-rung orthopair fuzzy 2-tuple linguistic context with partial weight information

Considering the advantages of q-rung orthopair fuzzy 2-tuple linguistic set (q-RFLS), which includes both linguistic and numeric data to describe evaluations, this article aims to design a new decision-making methodology by integrating Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) and qualitative flexible (QUALIFLEX) methods based on the revised aggregation operators to solve multiple criteria group decision making (MCGDM). To accomplish this, we first revise the extant operational laws of q-RFLSs to make up for their shortcomings. Based on novel operational laws, we develop q-rung orthopair fuzzy 2-tuple linguistic (q-RFL) weighted averaging and geometric operators and provide the corresponding results. Next, we develop a maximization deviation model to determine the criterion weights in the decision-making procedure, which accounts for partial weight unknown information. Then, the VIKOR and QUALIFLEX methodologies are combined, which can assess the concordance index of each ranking combination using group utility and individual maximum regret value of alternative and acquire the ranking result based on each permutation's general concordance index values. Consequently, a case study is conducted to select the best bike-sharing recycling supplier utilizing the suggested VIKOR-QUALIFLEX MCGDM method, demonstrating the method's applicability and availability. Finally, through sensitivity and comparative analysis, the validity and superiority of the proposed method are demonstrated.