差异化按需配送下自动化仓库运营与最后一公里运输的集成优化

中文摘要 AI 辅助

在差异化按需货物配送服务的背景下，本研究提出了一种针对基于自动导引车（AGV）的智能仓库运营与最后一公里多式联运的集成优化方法。设计了一种用于多目标联合调度的深度强化学习算法，以在两个系统之间建立动态联系，解决实现高通量连续订单调度、满足相互冲突的需求以及提高系统整体灵敏度和适应性等关键挑战。在该框架内的仓库优化方面，我们提出了一种基于多目标、多奖励机-A*引导深度Q网络（MORM-AGDQN）的改进算法，该算法结合了服务水平、系统成本和外部运输需求。在外部优化方面，我们提出了一种基于多奖励、多头注意力-异构容量车辆路径问题（MRMH-HCVRP）框架的改进算法，该算法整合了优化的调度顺序序列和分组，并结合了客户位置、需求、优先级、车辆容量、速度和服务范围。结果表明，所提出的框架显著优于传统方法，实现了仓储作业100%的准时交付率。经过联合优化，最后一公里的平均交付时间减少了29.3%至53.2%，总运输距离减少了46.4%，高优先级服务率提高到92%以上，并在运营成本和客户满意度之间保持了平衡。

英文摘要

In the context of differentiated on-demand goods delivery services, this study proposes an integrated optimization method for automated guided vehicles (AGVs) based smart warehouse operations and the last-mile multi-modal transport. A deep reinforcement learning algorithm for multi-objective joint scheduling is designed to establish a dynamic connection between two systems, solving key challenges such as achieving high-throughput continuous order scheduling, meeting competing requirements, and improving the overall system sensitivity and adaptability. For warehouse optimization within this framework, we propose an improved algorithm based on multi-objective, Multi-Reward Machines-A* Guided Deep Q-Network (MORM-AGDQN), which combines service level, system cost, and external transportation demand. For external optimization, we propose an improved algorithm based on a Multi-Reward, Multi Head attention-Heterogeneous Capacity Vehicle Routing Problem (MRMH-HCVRP) framework, which incorporates the optimized scheduling order sequence and grouping, combined with customer location, demand, and priority, vehicle capacity, speed, and service range. The results show that the proposed framework significantly outperforms traditional methods, achieving 100% on-time delivery rate for warehousing operations. After joint optimization, the average delivery time for the last mile was reduced by 29.3% to 53.2%, the total transportation distance was reduced by 46.4%, the high-priority service rate was increased to over 92%, and a balance was maintained between operating costs and customer satisfaction.

发表机构

• Toronto Metropolitan University（多伦多城市大学）

机构由 AI 辅助整理，请以论文原文为准。