基于全尺度学习的料箱搬运机器人系统订单履行序贯决策框架
Omni-scale Learning-based Sequential Decision Framework for Order Fulfillment of Tote-handling Robotic Systems
AI总结：
针对料箱搬运机器人系统的订单履行决策，提出一种结合结构化组合优化与多智能体强化学习的通用可扩展序贯决策框架OLSF-TRS，在小规模系统上平均最优性差距低于3.5%，在大规模场景中相比启发式基线减少8-12%的料箱移动，并保持实时响应。
摘要：
受电子商务和小批量生产的快速扩张推动，成品、半成品和原材料的内部物流负载单元规模正在稳步缩小。料箱正逐渐取代托盘成为主要的搬运和存储容器。这一转变将料箱搬运机器人系统推向了自动化订单履行中心的前沿。料箱搬运机器人系统的订单履行决策具有共同的订单-料箱-机器人序贯决策性质。现有研究主要针对特定系统的决策机制，难以泛化或迁移到其他场景。我们提出了一种基于全尺度学习的料箱搬运机器人系统订单履行序贯决策框架（OLSF-TRS），这是一个通用且可扩展的序贯决策框架，结合了结构化组合优化与多智能体强化学习，以协调订单、料箱和机器人决策。在小规模料箱搬运机器人系统上，OLSF-TRS在两种不同的系统配置下实现了接近最优的性能，平均最优性差距低于3.5%。在大规模场景中，OLSF-TRS在两种不同类型的系统上始终优于启发式基线，与基于规则的最先进方法相比，总料箱移动量减少了8-12%和超过30%，同时保持实时响应。这些改进转化为切实的运营效益，包括成本降低、能耗降低和吞吐量稳定性增强。所提出的框架为广泛部署的料箱搬运机器人系统提供了一种高效且统一的订单履行决策框架，支持电子商务和工业物流领域的高质量订单履行。
英文摘要：
Driven by the rapid expansion of e-commerce and small-batch production, the size of the intralogistics load unit of finished goods, semi-finished goods and raw materials is steadily shrinking. Totes are gradually replacing pallets as the primary handling and storage container. This shift has propelled tote-handling robotic systems to the forefront of automation order fulfillment centers. The order-fulfillment decisions of tote-handling robotic systems share a common order-tote-robot sequential decision-making nature. Existing studies primarily focus on decision mechanisms tailored to particular systems, making it difficult to generalize or transfer them to other contexts. We propose an Omni-scale Learning-based Sequential Decision Framework for Order Fulfillment of Tote-handling Robotic Systems (OLSF-TRS), a generalized and scalable sequential decision framework that combines structured combinatorial optimization with multi-agent reinforcement learning to coordinate order,tote, and robot decisions. On small-scale tote-handling robotic systems, OLSF-TRS achieves near-optimal performance with average optimality gaps below 3.5% across two distinct system configurations. In large-scale scenarios, OLSF-TRS consistently outperforms heuristic baselines across two different system types, reducing total tote movements by 8-12% and over 30% compared to SOTA rule-based approaches, while maintaining real-time responsiveness. These improvements translate into tangible operational benefits, including cost reduction, lower energy consumption, and enhanced throughput stability. The proposed framework delivers an efficient and unified order fulfillment decision-making framework for widely deployed tote-handling robotic systems,supporting high-quality order fulfillment in both e-commerce and industrial logistics sectors.
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