Reverse logistics is a critical topic that has captured the attention of government, private entities and researchers in recent years. This increase in the concern was driven by current set of government regulations, increase of public awareness, and the attractive economic opportunities. As a result, many corporations have started to comprehend the importance of the recovery process and are taking serious steps in restructuring their supply chain processes to meet the new regulations such as limitations on waste disposal and recycling requirements [1]. Because of the unique problems associated with reverse supply chain and the complex nature of the reverse logistics activities, numerous studies have been carried out in this field. One of those crucial areas is inventory management of end-of-life (EOL) products. Previously, we have assumed deterministic data for demand, supply, and line yields when modeling the inventory problem along the disassembly line. In this paper, we model uncertainty in the data with probability distribution to be accurate thus covering all possible inputs. The definition of the problem will remain the same as before.


Originally published in the Proceedings of the 2009 Northeast Decision Sciences Institute Conference, Uncasville, CT, April 1-3, 2009 (CD-ROM)


end-of-life processing, disassembly


Industrial Engineering | Mechanical Engineering


Northeast Decision Sciences Institute

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Copyright 2009, Surendra M. Gupta

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Surendra M. Gupta

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