Sustainable management of EOL tyres: A remanufacturing supply chain network design

• Published in: Journal of cleaner production Vol. 434; p. 140115
• Main Authors: Manupati, Vijaya Kumar, Kulatunga, A.K., Ray, Pritee, Rofin, T.M.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.01.2024
• Subjects: End-of-Life products; Facility location; Optimization; Remanufacturing supply chain; Vehicle allocation; Facility location; Vehicle allocation; End-of-Life products; Optimization; Remanufacturing supply chain
• ISSN: 0959-6526; 1879-1786
• DOI: 10.1016/j.jclepro.2023.140115

End-of-life (EOL) objects such as tyres have been categorised as hazardous to the environment due to heavy metals and chemicals. To manage the recovery and remanufacturing operations of these EOL items, an effective network design is necessary. In this paper, we developed a mixed integer linear programming (MILP) model to minimize the total cost of the proposed EOL tyre remanufacturing supply chain network. Since, the problem is Non-deterministic Polynomial time hardness (NP-hard) in nature, we propose a prediction model that evaluates the operational feasibility of vehicle allocation and an evolutionary algorithm-based technique is employed to identify optimal facility locations, material flows among facilities, and demand distribution to individual automobiles for near-optimal solutions. Numerical experiments were conducted on a leading tyre remanufacturing company as a case study, and the results were compared with several evolutionary algorithms found that the Modified Genetic Algorithm (MGA) outperforms other algorithms. We demonstrate how, in each case, an optimal cost-effective location for a factory is found based on the cost and demand of the products. The study findings can assist governments and industry stakeholders in developing successful EOL tyre management strategies. •End-of-life (EOL) tyre remanufacturing supply chain network design was developed.•A Mixed Integer Linear Programming model for EOL tyre remanufacturing unit.•A novel prediction and optimization approach has been proposed.•Evolutionary approaches are studied where Modified Genetic Algorithm outperformed.•Based on a real case, experiments conducted to obtain near-optimal results.