Reverse logistics network design for additive remanufacturing: a case study in Ontario

This article studies the design of reverse logistics networks by simultaneously addressing economic and environmental objectives, drawing on a real-world case study from the automotive industry. Two bi-objective mixed-integer linear programming models are developed: one deterministic and one stochastic. Both models determine the optimal locations of remanufacturing facilities and the optimal shipment routes, while minimizing total costs and greenhouse gas emissions. The stochastic model explicitly accounts for uncertainty in the supply of parts for remanufacturing and the demand for remanufactured parts. The models are applied using data from a case study in Ontario, Canada, demonstrating how reverse logistics network design can balance cost efficiency with environmental sustainability. The results consistently identify the Greater Toronto Area (specifically Mississauga and Brampton) and Ottawa as optimal locations for remanufacturing facilities, providing valuable insights for planners. Furthermore, the results quantify the trade-off between economic and environmental objectives. For example, a strategic shift in facility location decisions reduces emissions by 3.3% (over 330 tons of CO2 equivalent emissions annually) while increasing total network costs by less than 0.7%. These findings demonstrate that substantial environmental gains can be achieved with minimal economic impact, highlighting the importance of explicitly incorporating emissions into network design models.