标题： 联合匹配与定价用于店内顾客的众包配送 显示英文标题

标题： Joint Matching and Pricing for Crowd-shipping with In-store Customers

摘要： 本文研究了在集中式众包配送系统中，将店内顾客作为配送快递员的使用情况，旨在满足城市地区日益增长的高效最后一公里配送需求。 我们考虑了一个实体零售环境，在该环境中，购物者会获得报酬来配送时效性强的在线订单。 为了管理这一过程，我们提出了一种马尔可夫决策过程（MDP）模型，该模型捕捉了关键的不确定性，包括订单和众包配送员的随机到达，以及配送报价的概率性接受。 我们的解决方案方法结合了神经近似动态规划（NeurADP）用于自适应的订单到购物者分配，以及深度双重Q网络（DDQN）用于动态定价。 这种联合优化策略实现了多点配送路线，并考虑了报价接受的不确定性，更贴近现实操作。 实验结果表明，集成的NeurADP + DDQN策略在配送成本效率方面取得了显著改进，相比固定定价的NeurADP提高了多达6.7%，相比短视基线提高了约18%。 我们还表明，允许灵活的配送延迟和实现多目的地路由分别进一步降低了运营成本8%和17%。 这些发现突显了在众包配送系统中动态、前瞻性策略的优势，并为城市物流运营商提供了实际指导。 显示英文摘要

摘要： This paper examines the use of in-store customers as delivery couriers in a centralized crowd-shipping system, targeting the growing need for efficient last-mile delivery in urban areas. We consider a brick-and-mortar retail setting where shoppers are offered compensation to deliver time-sensitive online orders. To manage this process, we propose a Markov Decision Process (MDP) model that captures key uncertainties, including the stochastic arrival of orders and crowd-shippers, and the probabilistic acceptance of delivery offers. Our solution approach integrates Neural Approximate Dynamic Programming (NeurADP) for adaptive order-to-shopper assignment with a Deep Double Q-Network (DDQN) for dynamic pricing. This joint optimization strategy enables multi-drop routing and accounts for offer acceptance uncertainty, aligning more closely with real-world operations. Experimental results demonstrate that the integrated NeurADP + DDQN policy achieves notable improvements in delivery cost efficiency, with up to 6.7\% savings over NeurADP with fixed pricing and approximately 18\% over myopic baselines. We also show that allowing flexible delivery delays and enabling multi-destination routing further reduces operational costs by 8\% and 17\%, respectively. These findings underscore the advantages of dynamic, forward-looking policies in crowd-shipping systems and offer practical guidance for urban logistics operators.