Abstract
The sustainable management of medical waste is essential for environmental protection and public health safety, contributing to urban development and societal well-being. This study focuses on the medical waste Location-Routing Problem with Electric Vehicles (EVs), aiming to mitigate infection risk while providing cost-effective and energy-efficient logistics solutions. It encompasses diverse elements such as disposal location selection, vehicle scheduling, recharge strategies, time windows, working duration, and load-dependent energy consumption. To tackle this complex problem, we introduce an Evolutionary Decomposition-based Adapted Large Neighborhood Search algorithm (E-ALNS/D), which surpasses existing methods such as MOEA/D-HGS, MOEA/D-LNS, MPGA, and NSGAII-LNS across various urban settings. Empirical analysis and a case study reveal opportunities for enhancing medical waste management with EVs. Key findings show that optimizing service times, adopting fast-charging infrastructure, and strategically placing temporary disposal facilities in response to unforeseen demand spikes significantly reduce energy consumption, lower infection risk, and cut charging cost. Furthermore, selecting peripheral over central disposal locations better minimizes infection risks. This research offers valuable insights for decision-makers in EV-based medical waste management, emphasizing the need to balance public health, environmental sustainability, and economic efficiency. Our methodology paves the way for future research in sustainable medical waste management with EVs.
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