A scenario-based robust possibilistic model for a multi-objective electronic reverse logistics network

Electronic reverse logistics topic has received growing attention because of its environmental and economic impact. In Canada, the province of Ontario has enacted regulations regarding the Waste Electrical and Electronic Equipment (WEEE) Recycling program. The objective of this study is to develop a novel scenario-based robust possibilistic approach to optimize and configure an electronic reverse logistics network by considering the uncertainty associated with fixed and variable costs, the quantity of demand and return, and the quality of returned products. A Monte Carlo simulation is utilized to analyze the performance of our proposed model. Then, ANOVA test is conducted to statistically verify our model using the simulation results. The mathematical model is extended to the multi-objective optimization by maximising the environmental compliance of the third parties. The efficient solutions of the multi-objective model are computed using the two-phase fuzzy compromise approach. To provide a comprehensive assessment of the problem under investigation, we provide sensitivity analyses on the impact of different factors (e.g., recovery rates, capacity of facilities) on the total expected profit. Several interesting results were obtained, including the fact that increasing the capacity of facilities does not automatically translate into higher profits. Furthermore, by comparing the efficient solutions of deterministic and robust modes, we illustrate the impact of robustness price on the multi-objective model. The application of the proposed model is illustrated using a network in the Greater Toronto Area (GTA) in Canada.