Third-Party Cold Chain Medicine Logistics Provider Selection by a Rough Set-Based Gained and Lost Dominance Score Method

Abstract

Choosing third-party cold chain logistics suppliers can be regarded as a multiple criteria group decision-making problem since multiple aspects of suppliers are required to be evaluated by multiple experts. This paper aims to address the problem of selecting the optimal third-party cold chain medicine logistics provider considering the uncertainties caused by the qualitative criteria that are difficult to accurately evaluate and the limitation of experts’ knowledge and cognition. We propose a rough set-based gained and lost dominance score method in which experts are supposed to use linguistic terms to express their information. First, we combine rough numbers with linguistic scale functions to depict the semantics of linguistic terms and convert the formal expression information of linguistic terms into numerical information, which has an advantage of expressing imprecision and subjective judgments of experts. In addition, given that different experts often have different emphases on different attributes, we investigate a rough set-based gained and lost dominance score method in which experts have different weights for different criteria. Finally, an illustrative example of selecting the optimal third-party cold chain medicine logistics is given with comparative analysis, showing the efficiency of the proposed method. This method provides a new way to solve the selection problem of third-party logistics suppliers.