Reverse Logistics Network Design for Additive Remanufacturing

Abstract

The current push for businesses to adopt sustainable supply chain practices contributes to a circular economy, a systems-focused approach designed to allow resources to be used and reused for as long as possible. The Canadian Net-Zero Emissions Accountability Act and the 2030 Emissions Reduction Plan developed by the Canadian federal government aim to regulate the environmental footprint by reducing greenhouse gas (GHG) emissions. Some of these goals can be achieved by designing reverse logistics networks. Reverse logistics is the design of a network for the purpose of collecting end-of-life/end-of-use products and reusing, repairing, refurbishing, remanufacturing, and/or recycling.

This thesis proposes four models to design a reverse logistics network: deterministic, multi-period, stochastic, and a game theory model. The models are formulated as bi-objective mixed-integer linear programs. The design of the network is optimized with respect to economic and environmental objectives. The balancing of costs and environmental objectives during the design of the reverse logistics network provides the decision-maker with additional information on how environmental goals can be met. The models aim to determine the optimal locations for remanufacturing facilities and the optimal flow of parts to and from these facilities. The bi-objective models are solved using the weighted sum method, which allows for Pareto-optimal solutions to be analyzed. The models are solved using Gurobi in Python.

Deterministic, stochastic, and game theory models are applied to a case study for the remanufacturing of front lower control arms in the automotive industry in Ontario, Canada. The stochastic model is motivated by the uncertainty in the supply of end-of-life vehicles. The stochastic model is solved using the deterministic equivalent. The game theory model complements the aforementioned approach. It facilitates the triangulation of the results.

The models select the optimal locations for the remanufacturing facilities. Significantly, all three models applied to the case study produce similar results. The quantitative results demonstrate that the optimal solution based on the case study data is when multiple facilities are located, one facility should be opened in Ottawa and at least one other in the GTA. When only one facility is located, it should be placed in the GTA (either Mississauga or Brampton). Overall, the results reveal that small investments can lead to significant reductions in greenhouse emissions released during transportation.

The results can be scaled to design a reverse logistics network for Canada and inform environmental policies. The results and findings of this study may be used to inform policies on the reduction of transportation emissions. The contributions that the thesis makes to the field are: (i) it incorporates greenhouse gas emissions into the models; (ii) it allows decision-makers to compare the results of the three models (deterministic, stochastic, and game theory) applied to the case study; and (iii) it applies the models to real-world data.