Medical Supplier Selection with a Group Decision-Making Method Based on Incomplete Probabilistic Linguistic Preference Relations

In hospital operation management, the medical supplier selection is a significant problem in which experts' domain knowledge plays a critical role in selecting medical suppliers. The probabilistic linguistic preference relation (PLPR) whose elements are probabilistic linguistic term sets (PLTSs) is an effective tool to express experts' preferences on alternatives based on pairwise comparisons. Due to the lack of knowledge of experts and the limit information of alternatives, some preference information may be missing and thus the incomplete PLPR (InPLPR) is constructed. In this study, a group decision-making (GDM) method with InPLPRs is proposed to deal with a practical medical supplier selection problem. To achieve this goal, we first propose a method to check whether an InPLPR is acceptable. Then, a procedure is developed to complete the acceptable InPLPR based on the property of additive consistency and a social strategy is proposed to complete the unacceptable InPLPR. Afterward, a GDM model based on InPLPRs is constructed in which the aggregation phase and exploitation phase are conducted by the probabilistic linguistic weighted averaging (PLWA) operator. Besides, an approach for calculating the weights of experts is proposed considering the characteristics of InPLPRs. Finally, we illustrate the proposed GDM model by a practical case about the medical supplier selection. A comparative analysis is presented to demonstrate the advantages of our method.

Automated summary

This study proposes a method for group decision-making using incomplete probabilistic linguistic preference relations to address the issue of medical supplier selection. Through a series of processes and methods, the handling of incomplete preference relations is completed and a group decision-making model is established, demonstrating the advantages of the method through a practical case.