Agent based simulation optimization of waste electrical and electronics equipment recovery

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. Sociodemographic properties of the consumers, attributes of the takeback 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 simulationbased optimization are provided to illustrate the application of the model.