Municipal solid waste management using electric vehicle by variable neighbourhood search algorithm

Efficient solid waste management is essential for addressing the multifaceted challenges associated with waste disposal, including environmental, economic, technical, and social aspects. Municipal solid waste management (MSWM) incurs significant costs and contributes to environmental pollution primarily due to the collection process. This research presents a comprehensive case study that focuses on optimising the routing of electric vehicles for solid waste collection. Specifically, it addresses the Half-Open Time-Dependent Multi-Depot Electric Vehicle Routing Problem considering Battery Recharging and Swapping (HOTDMDEVRPBRS) in the context of waste collection. This problem aims to optimise the routes taken by electric vehicles across multiple depots, considering constraints such as limited battery capacity, multiple technologies for recharging battery capacity, the impact of truckload on energy consumption, and time-dependent travel times. To tackle this problem, we propose a highly efficient Variable Neighborhood Search (VNS) meta-heuristic algorithm, which outperforms the Simulated Annealing (SA) approach. The proposed algorithm achieves an average improvement of 11.88% in solution quality and reduces execution time by 73% for last-mile delivery. We evaluate the effectiveness of the proposed approach using real-world data from New York's recycling management system. The findings of this study emphasise the potential for enhancing solution quality and operational efficiency in solid waste management.