使用移动包裹柜的最后一英里配送路径规划：一种混合 Q-Learning 网络方法
Route Planning for Last-Mile Deliveries Using Mobile Parcel Lockers: A Hybrid Q-Learning Network Approach
AI总结：
针对最后一英里配送，提出移动包裹柜问题并开发混合 Q-Learning 网络方法（HQM），结合全局与局部搜索优化路径规划，实验表明其性能优于精确方法和遗传算法，并分析了影响配送效率的关键因素。
摘要：
移动包裹柜最近由物流运营商提出，作为一种可以帮助减少城市货运配送中交通拥堵和运营成本的技术。鉴于其在部署区域内可重新定位的能力，它们具有改善客户可及性和便利性的潜力。在本研究中，我们提出了移动包裹柜问题（MPLP），这是位置-路径问题（LRP）的一个特例，用于确定 MPL 在全天中的最优停靠位置并规划相应的配送路线。我们开发了一种基于混合 Q-Learning 网络的方法（HQM），以解决由此产生的大型问题实例的计算复杂性，同时摆脱局部最优。此外，HQM 集成了全局和局部搜索机制，以解决经典强化学习方法面临的探索与利用困境。我们在不同问题规模（最多 200 个节点）下检验了 HQM 的性能，并将其与精确方法和遗传算法（GA）进行了基准对比。结果表明，在大型问题实例中，与 Gurobi 求解器求解的精确方法相比，HQM 取得了更好的优化性能且计算时间更短。此外，HQM 获得的平均奖励是 GA 的 1.96 倍，这表明 HQM 具有更好的优化能力。进一步，我们确定了影响车队规模需求、行驶距离和服务延迟的关键因素。我们的研究结果表明，MPL 的效率主要取决于时间窗的长度和 MPL 停靠点的部署。最后，我们基于参数分析强调了管理启示，为物流运营商在高效最后一英里配送运营的背景下提供指导。
英文摘要：
Mobile parcel lockers have been recently proposed by logistics operators as a technology that could help reduce traffic congestion and operational costs in urban freight distribution. Given their ability to relocate throughout their area of deployment, they hold the potential to improve customer accessibility and convenience. In this study, we formulate the Mobile Parcel Locker Problem (MPLP) , a special case of the Location-Routing Problem (LRP) which determines the optimal stopover location for MPLs throughout the day and plans corresponding delivery routes. A Hybrid Q Learning Network based Method (HQM) is developed to resolve the computational complexity of the resulting large problem instances while escaping local optima. In addition, the HQM is integrated with global and local search mechanisms to resolve the dilemma of exploration and exploitation faced by classic reinforcement learning methods. We examine the performance of HQM under different problem sizes (up to 200 nodes) and benchmarked it against the exact approach and Genetic Algorithm (GA). Our results indicate that HQM achieves better optimisation performance with shorter computation time than the exact approach solved by the Gurobi solver in large problem instances. Additionally, the average reward obtained by HQM is 1.96 times greater than GA, which demonstrates that HQM has a better optimisation ability. Further, we identify critical factors that contribute to fleet size requirements, travel distances, and service delays. Our findings outline that the efficiency of MPLs is mainly contingent on the length of time windows and the deployment of MPL stopovers. Finally, we highlight managerial implications based on parametric analysis to provide guidance for logistics operators in the context of efficient last-mile distribution operations.
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