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

Abstract

To improve the core competitiveness, a growing number of enterprises choose to outsource their logistics business. Medicine, as a special item, has extremely high requirements on logistics, which makes the selection of third-party logistics providers complicated. Since the selection needs to consider many aspects and usually involves multiple experts, it is regard as a multi-criteria group decision making problem. To address this problem, this paper proposes a rough set-based gained and lost dominance score (GLDS) method in which linguistic terms are information. In reality, different experts may have different cognition about the semantics of the same linguistic terms. Thus, we use different linguistic scale functions to reflect this fact. In addition, the rough set theory using upper and lower approximations to express uncertainty is also employed to effectively handle the imprecision and subjective judgments of experts. The original GLDS method is extended to rough set context. Finally, an illustrative example of selecting the optimal third-party cold chain medicine logistics is given to validate the our method.