The profitability of Electronic waste (e-waste) recovery operations is quite challenging due to various sources of uncertainties in quantity, quality and timing of returns originating from consumers’ behavior. The cloud-based remanufacturing concept, data collection and information tracking technologies seems a promising solution toward proper collection and recovery of product life cycle data under uncertainty. A comprehensive model that takes every aspect of recovery systems into account will help policy makers perform better decisions over a planning horizon. The objective of this study is to develop an Agent Based Simulation (ABS) framework to model the overall product take-back and recovery system based on the product identity data available through cloud-based remanufacturing infrastructure. Socio-demographic properties of the consumers, attributes of the take-back programs, specific characteristics of the recovery process and product life cycle information have been considered to capture the optimum buyback price proposed for a product with the aim of controlling the timing and quality of incoming used products to collection sites for recovery. A numerical example of an electronic product take-back system and a simulation-based optimization are provided to illustrate the application of the model.

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Agent-Based Simulation Optimization of Waste Electrical and Electronic Equipment Recovery