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

EFENDİGİL T. , ÖNÜT S. , Kongar E.

COMPUTERS & INDUSTRIAL ENGINEERING, vol.54, no.2, pp.269-287, 2008 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 54 Issue: 2
  • Publication Date: 2008
  • Doi Number: 10.1016/j.cie.2007.07.009
  • Page Numbers: pp.269-287
  • Keywords: reverse logistics, artificial neural networks, fuzzy analytical hierarchy process, 3PLs selection, multiple criteria decision-making, DECISION-MAKING, DESIGN, MANAGEMENT, NETWORKS, MODELS, SYSTEM


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. (c) 2007 Elsevier Ltd. All rights reserved.