Abstract
Introducing clean energy into urban cold chain distribution to achieve a green transition is crucial for reducing transportation and logistics emissions and promoting sustainable urban development. This study focuses on the Cold Chain Green Multi-Depot Vehicle Routing Problem with Time Windows and Mixed Fleets (CC-GMD-VRPTW-MF) for city logistics distribution, utilizing both electric vehicles (EVs) and gasoline and diesel vehicles (GDVs). To accurately assess energy consumption, a realistic energy consumption model is employed. The CC-GMD-VRPTW-MF aims to minimize total costs (including fixed, energy consumption, damage, and environmental costs). To address the problem, we propose an improved Variable Neighborhood Search (VNS) algorithm that introduces a new balanced mechanism for perturbation and a new memory-based mechanism for local search to enhance computational performance. Numerical studies on newly designed CC-GMD-VRPTW-MF instances are conducted to investigate the effect of incorporating EVs for joint delivery, considering different carbon prices, and adjusting time windows. Furthermore, we evaluate the effectiveness of the proposed improvement strategies in VNS and demonstrate the algorithm’s performance on benchmarks of related problems.
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