Journal
COMPUTERS & INDUSTRIAL ENGINEERING
Volume 54, Issue 2, Pages 269-287Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.cie.2007.07.009
Keywords
reverse logistics; artificial neural networks; fuzzy analytical hierarchy process; 3PLs selection; multiple criteria decision-making
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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.
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