Supplier Selection of Medical Consumption Products with a Probabilistic Linguistic MABAC Method.

Guiwu Wei,Jiang Wu,Hongjun Wang,Cun Wei

doi:10.3390/ijerph16245082

Abstract

In order to obtain an optimal medical consumption product supplier, the integration of combined weights and multi-attributive border approximation area comparison (MABAC) under probabilistic linguistic sets (PLTSs) has offered a novel integrated model in which the CRiteria Importance Through Intercriteria Correlation (CRITIC) method is employed for calculating the objective weights of various attributes and the MABAC method with PLTSs is used to acquire the final ranking result of a medical consumption product supplier. Additionally, so as to indicate the applicability of the devised method, this model is confirmed by a numerical case for the supplier selection of medical consumption products. Some comparative studies are made with some existing methods. The proposed method can also successfully select suitable alternatives in other selection problems.

Highlights

Along with ever growing complication and ambiguity of decision making issues and the fuzziness of human subjective cognition, it is increasingly arduous for DMs to offer exact judgments
Song and Li [49] proposed an LGDM method in which incomplete information is more relevant for multi-stakeholders to stand for their evaluation; three normalizing algorithms were presented to obtain the entire probabilistic linguistic sets (PLTSs) based on three kinds of risk attitudes: optimistic, pessimistic and neutral
The major research contribution can be described as follows: (1) The modified multi-attributive border approximation area comparison (MABAC) is extended by PLTSs. (2) The probabilistic linguistic MABAC (PL-MABAC) method is developed to tackle multiple attribute group decision making (MAGDM) problems with

Summary

Introduction

Along with ever growing complication and ambiguity of decision making issues and the fuzziness of human subjective cognition, it is increasingly arduous for DMs (decision makers) to offer exact judgments. Song and Li [48] proposed the the consensus process, in which MGPFLPRs (multi-granular probabilistic fuzzy linguistic preference relations) are utilized to express the preference information of sub-groups To settle these issues, Song and Li [49] proposed an LGDM method in which incomplete information is more relevant for multi-stakeholders to stand for their evaluation; three normalizing algorithms were presented to obtain the entire PLTSs based on three kinds of risk attitudes: optimistic, pessimistic and neutral. While noting taking different weights into account influences the sorting results, a novel method is proposed to decide the weights through integrating subjective elements with objective ones To obtain such goals, the major research contribution can be described as follows: (1) The modified MABAC is extended by PLTSs.

Preliminaries

MABAC Method for Probabilistic Linguistic MAGDM Problems

A Case Analysis

Comparative Analysis

Methods

Conclusions