Predictive Modeling for Sustainable Tire Retreading and Resource Optimization in Public Transport System

Arun Navin Joseph (), Nedunchezhian Natarajan, Murugesan Ramasamy and Pachaivannan Partheeban
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Arun Navin Joseph: Department of Mechanical Engineering, Anna University, Chennai 600025, India
Nedunchezhian Natarajan: Department of Automobile Engineering, Annapoorana Engineering College, Salem 636308, India
Murugesan Ramasamy: Department of Civil Engineering, Nandha Engineering College, Erode 638052, India
Pachaivannan Partheeban: Department of Civil Engineering, Chennai Institute of Technology, Chennai 600069, India

Sustainability, 2025, vol. 17, issue 12, 1-23

Abstract: Retreading is a cornerstone in the remanufacturing process of tires, facilitating the extraction of maximum kilometers (Km) from a tire carcass. Tire remanufacturing plays a crucial role in conserving raw materials, reducing environmental impacts, and lowering the overall operating costs. This study employs predictive modeling techniques to forecast tire performance and optimize resource allocation, departing from traditional approaches, for a bus transport system in India. Machine learning models, including linear regression, ensemble boosted trees, and neural network models, were used. Two scenarios were devised: Scenario I addressed premature failures and optimizing performance to reduce tire procurement and Scenario II used targeted interventions, such as eliminating new tire condemnations and optimizing retread (RT) strategies, and could potentially salvage 169 tires from premature retirement. The results achieved R 2 values of 0.44, 0.51, and 0.45 and improved values for the test datasets of 0.46, 0.52 and 0.44. By leveraging these models, decision-makers can substantially improve tire mileage, reduce premature condemnations, increase tire production, and drive cost savings in fleet operations. Notably, this approach contributes to enhanced operational efficiency and promotes sustainability by cutting costs by 15–25%, improving tire mileage by 20–30%, and reducing environmental impacts by up to 25%. These results demonstrate the broader implications of predictive modelling as a decision-support tool, highlighting its capacity to drive economic and environmental benefits across industrial logistics and sustainable development.

Keywords: predictive maintenance; machine learning; resource optimization; tire lifecycle management; sustainability; retread strategies (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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