Mobile parcel lockers (MPLs) have been recently introduced by urban logistics operators as a means to reduce traffic congestion and operational cost. Their capability to relocate their position during the day has the potential to improve customer accessibility and convenience (if deployed and planned accordingly), allowing customers to collect parcels at their preferred time among one of the multiple locations. This paper proposes an integer programming model to solve the Location Routing Problem for MPLs to determine the optimal configuration and locker routes. In solving this model, a Hybrid Q-Learning algorithm-based Method (HQM) integrated with global and local search mechanisms is developed, the performance of which is examined for different problem sizes and benchmarked with genetic algorithms. Furthermore, we introduced two route adjustment strategies to resolve stochastic events that may cause delays. The results show that HQM achieves 443.41% improvement on average in solution improvement, compared with the 94.91% improvement of heuristic counterparts, suggesting HQM enables a more efficient search for better solutions. Finally, we identify critical factors that contribute to service delays and investigate their effects.
翻译:城市物流运营商最近引入了移动包裹储物柜(MPL),作为减少交通拥堵和运营成本的一种手段,城市物流运营商最近引入了移动包裹储物柜(MPLs)作为减少交通拥堵和运营成本的手段,他们在白天迁移其位置的能力有可能改善客户的无障碍性和方便性(如果部署和规划相应的话),使客户能够在多个地点之一的首选时间收集包裹。本文件建议采用一个整数编程模式,解决移动包裹储物柜的定位路程问题,以便确定最佳配置和储物柜路线。在解决这一模式时,开发了一种混合Q-学习算法(HQM)与全球和地方搜索机制相结合的方法(HQM),该方法的性能根据不同的问题大小加以审查,并以遗传算法作为基准。此外,我们引入了两种路线调整战略,以解决可能造成延误的随机事件。结果显示,HQM在解决方案改进方面平均实现了44.41%的改善率,而超度对应方则建议HQM能够更有效地寻找更好的解决方案。最后,我们确定了导致服务延误和调查其影响的关键因素。