A two-phase mathematical programming approach to efficiently design a reverse supply chain is proposed, as follows: in phase I, a fuzzy cost-benefit function is formulated to perform a multi-criteria economic analysis for selecting the most economical product to re-process, from a set of candidate used products; in phase II, an integer goal programming model that not only identifies potential recovery facilities but also leads to transportation of the right mix and quantities of products (used as well as re-processed) across the supply chain, is formulated. Since the success of a reverse supply chain depends on its marketing strategy as well as on its design, it is important that the planned marketing strategy be evaluated with respect to drivers of public participation in the supply chain (more participation of the public implies more effectiveness of the marketing strategy), before actually implementing the strategy. To this end, we identify all the important drivers of public participation, and propose a fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) approach to evaluate the marketing strategy of a reverse supply chain with respect to those drivers.


Originally published in the Proceedings of the 2004 IEEE International Symposium on Electronics and the Environment, Phoenix, AZ, May 10-13, 2004 (CD-ROM)


Reverse Supply Chain, Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)


Industrial Engineering | Mechanical Engineering



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