A Mathematical Model for Selecting Third-Party Reverse Logistics Providers

Abstract

Generally, many optimisation models of Third-Party Reverse Logistics (3PL) provider selection assume that cardinal data, with less emphasis on ordinal data, exist. However, to select the best 3PL providers, this assumption is not realistic because ordinal data are vital. To deal with this difficulty and select the most efficient 3PL provider with the condition that both ordinal and cardinal data are present, a methodology which is based on Imprecise Data Envelopment Analysis (IDEA) is introduced. A numerical example demonstrates the application of the proposed method.