A holistic approach for selecting a third-party reverse logistics provider in the presence of vagueness

Abstract

Growing environmental concerns have motivated businesses to carefully assess the environmental impact of their products and services at all stages of a life-cycle. Reverse logistics plays an important role in achieving “green supply chains” by providing customers with the opportunity to return the warranted and/or defective products to the manufacturer. An efficient reverse logistics structure may lead to a significant return on investment as well as a significantly increased competitiveness in the market. In order to ensure efficiency, many organizations outsource their reverse logistics activities by engaging third-party logistics providers that implement reverse logistics programs designed to gain value from returned products. The selection of third-party providers is a crucial step in initializing reverse logistics related practices. This study aims to efficiently assist the decision makers in determining the “most appropriate” third-party reverse logistics provider using a two-phase model based on artificial neural networks and fuzzy logic in a holistic manner. A numerical example is also included in the study to demonstrate the steps of the proposed model.