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.

翻译：移动包裹储物柜是物流运营商近期提出的一项技术，有助于减少城市货运配送中的交通拥堵和运营成本。鉴于其在部署区域内可重新定位的能力，移动包裹储物柜具有提升客户可达性和便利性的潜力。本研究将移动包裹储物柜问题（MPLP）表述为位置-路径问题（LRP）的一个特例，该问题确定移动包裹储物柜在全天的最佳停靠位置，并规划相应的配送路线。为解决由此产生的大规模问题实例的计算复杂性并避免陷入局部最优，我们提出了一种基于混合Q学习网络的方法（HQM）。此外，HQM集成了全局与局部搜索机制，以解决经典强化学习方法在探索与利用之间的两难困境。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进行对比。结果表明，在处理大规模问题时，HQM相比由Gurobi求解器求解的精确方法，能以更短的计算时间实现更优的优化性能。此外，HQM获得的平均奖励是遗传算法的1.96倍，这表明HQM具有更强的优化能力。进一步，我们识别了影响车队规模需求、行驶距离和服务延迟的关键因素。研究结果表明，移动包裹储物柜的效率主要取决于时间窗长度及部署停靠点的数量。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末端配送运营方面提供指导。