Omni-scale Learning-based Sequential Decision Framework for Order Fulfillment of Tote-handling Robotic Systems
Researchers have proposed a new Omni-scale Learning-based Sequential Decision Framework (OLSF-TRS) to optimize order fulfillment in tote-handling robotic systems, which are increasingly vital in e-commerce and industrial logistics. As totes replace pallets due to shrinking load units, existing decision mechanisms often lack generalizability. The OLSF-TRS framework addresses this by combining structured combinatorial optimization with multi-agent reinforcement learning to coordinate orders, totes, and robots. In small-scale scenarios, the framework achieves near-optimal performance with optimality gaps under 3.5%. In large-scale operations, it significantly outperforms heuristic baselines, reducing total tote movements by 8-12% and over 30% compared to state-of-the-art rule-based approaches, while maintaining real-time responsiveness. These enhancements lead to reduced operational costs, lower energy consumption, and improved throughput stability. The study offers a scalable, unified solution for automated fulfillment centers, supporting high-quality logistics operations across diverse system configurations and sectors.
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Omni-scale Learning-based Sequential Decision Framework for Order Fulfillment of Tote-handling Robotic Systems
Researchers have proposed a new Omni-scale Learning-based Sequential Decision Framework (OLSF-TRS) to optimize order fulfillment in tote-handling robotic systems, which are increasingly vital in e-commerce and industrial logistics. As totes replace pallets due to shrinking load units, existing decision mechanisms often lack generalizability. The OLSF-TRS framework addresses this by combining structured combinatorial optimization with multi-agent reinforcement learning to coordinate orders, totes, and robots. In small-scale scenarios, the framework achieves near-optimal performance with optimality gaps under 3.5%. In large-scale operations, it significantly outperforms heuristic baselines, reducing total tote movements by 8-12% and over 30% compared to state-of-the-art rule-based approaches, while maintaining real-time responsiveness. These enhancements lead to reduced operational costs, lower energy consumption, and improved throughput stability. The study offers a scalable, unified solution for automated fulfillment centers, supporting high-quality logistics operations across diverse system configurations and sectors.
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