Research on Electric Vehicle Routing Optimization for Bulky Waste Collection Considering Disturbance Recovery

作者：Xu Wang, Huimin Ma · 发表于：2025 6th International Conference on Computer Engineering and Intelligent Control (ICCEIC) · 年份：2025 · DOI：10.1109/ICCEIC67916.2025.11308784

During the collection and transportation of bulky waste, inaccurate reporting of waste volume and weight, as well as fluctuations in the battery capacity of electric vehicles, may cause the original vehicle scheduling plan to become invalid or significantly increase operational costs. To address this issue, this study investigates a multi-type electric vehicle routing optimization problem for bulky waste collection, with a focus on disturbance recovery. In terms of model formulation, disturbances in waste generation are explicitly modeled. To measure their impact on the stability of existing routes, a disturbance intensity function is introduced, enabling quantitative assessment and dynamic adjustment of initial routes according to disturbance information. On the algorithmic side, a solution framework based on Adaptive Large Neighborhood Search (ALNS) is developed, where a Q-learning reinforcement learning mechanism is integrated to optimize operator selection strategies, thereby achieving self-learning and intelligent decision-making in route adjustments. Simulation experiments demonstrate the effectiveness and robustness of the proposed method under datasets of different scales and varying levels of disturbance, providing feasible optimization approaches and methodological support for building electrified and intelligent bulky waste collection systems.