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Abstract：
Robotic Mobile Fulfillment Systems (RMFSs) face challenges in handling large-scale orders and navigating complex environments, frequently encountering a series of intricate decision-making problems, such as order allocation, shelf selection, and robot scheduling. To address these challenges, this paper integrates Deep Reinforcement Learning (DRL) technology into an RMFS, to meet the needs of efficient order processing and system stability. This study focuses on three key stages of RMFSs: order allocation and sorting, shelf selection, and coordinated robot scheduling. For each stage, mathematical models are established and the corresponding solutions are proposed. Unlike traditional methods, DRL technology is introduced to solve these problems, utilizing a Genetic Algorithm and Ant Colony Optimization to handle decision making related to large-scale orders. Through simulation experiments, performance indicators-such as shelf access frequency and the total processing time of the RMFS-are evaluated. The experimental results demonstrate that, compared to traditional methods, our algorithms excel in handling large-scale orders, showcasing exceptional superiority, capable of completing approximately 110 tasks within an hour. Future research should focus on integrated decision-making modeling for each stage of RMFSs and designing efficient heuristic algorithms for large-scale problems, to further enhance system performance and efficiency.
摘要：
机器人移动履行系统（RMFS）在处理大规模订单和导航复杂环境方面面临挑战，经常遇到一系列复杂的决策问题，例如订单分配，货架选择,机器人调度为了应对这些挑战，本文将深度强化学习(DRL)技术集成到RMFS中，满足高效订单处理和系统稳定性的需要。本研究集中在RMFS的三个关键阶段：订单分配和排序，货架选择,和协调的机器人调度。对于每个阶段,建立了数学模型,并提出了相应的解决方案。与传统方法不同，DRL技术的引入解决了这些问题,利用遗传算法和蚁群优化来处理与大规模订单相关的决策。通过仿真实验,评估性能指标，例如货架访问频率和RMFS的总处理时间。实验结果表明,与传统方法相比，我们的算法擅长处理大规模订单，展示非凡的优越性，能够在一小时内完成大约110个任务。未来的研究应该集中在对RMFSs的每个阶段进行综合决策建模，并为大规模问题设计有效的启发式算法，进一步提高系统性能和效率。
