Optimisation of the circular supply chains for the mining equipment industry

With increasing sustainability concerns and stringent environmental regulations, industries are adopting circular economy (CE) strategies to reduce their environmental footprint while maintaining economic viability. However, implementing CE strategies adds complexity by introducing reverse logistics, supply chain tires and additional activities for product lifecycle extension, requiring advanced optimization techniques to balance economic and environmental objectives. This study presents a multi-objective mixed-integer linear programming (MILP) model for optimizing circular supply chains, focusing on cost minimization and CO₂ equivalent emissions reduction. The model is based on a case study of a mining equipment manufacturer integrating modular product refurbishment into the supply chain. Three model solution approaches are explored: a single-objective cost minimization model, a single-objective emissions minimization model, and a multi-objective model solved using the ε-constraint method to generate Pareto??optimal trade-offs. Results highlight how multi-objective optimization helps to support decision??making by balancing economic performance and sustainability goals, particularly in global distribution networks. The study contributes to circular supply chain optimization literature by extending its application to heavy industries like mining equipment, providing insights into optimal trade-offs between cost efficiency and environmental impact.