As electronic waste (e-waste) becomes one of the fastest growing environmental concerns, remanufacturing may be a promising solution. However, the profitability of take-back systems is hampered by several factors, including the lack of information on the quantity and timing of to-be-returned used products to a remanufacturing facility. Factors that contribute to this unpredictability in the waste stream include product design features, consumers' awareness of recycling opportunities, sociodemographic characteristics, peer pressure, and the tendency of consumers to keep used items in storage. A system that helps predict when the consumer will stop using a product and store, resell, recycle, or discard it could help manufacturers better estimate return trends. The objective of this paper is to develop an agent-based simulation (ABS) framework that integrates a discrete choice analysis (DCA) technique to predict consumer end-of-use (EOU) decisions. The proposed simulation tools examine the impact of design features, interaction among individual consumers, and sociodemographic criteria related to the number of e-product returns. A numerical example of a cellphone take-back system has been provided to show the application of the model.

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