Dynamic Multi-Agent Pickup and Delivery in Robotic Cellular Warehousing Systems
For warehouse operators, this work addresses the practical challenge of dynamic order updates during execution, offering improved efficiency over static approaches.
This paper introduces the Dynamic Multi-Agent Pickup and Delivery problem with internal order evolution, and proposes two event-triggered replanning algorithms (Dynamic Token Passing and Cooperative Token Passing) that reduce order flowtime compared to static and non-cooperative baselines in simulated robotic cellular warehousing systems.
Robotic Cellular Warehousing Systems (RCWS) give rise to multi-agent pickup and delivery (MAPD) processes in which robots sequentially collect multiple stock-keeping units (SKUs) for each order. Unlike classical MAPD formulations that assume static tasks, real warehouse operations often involve dynamic order evolution, where new SKUs may be appended to an order while it is being executed. Motivated by this practical requirement, this letter formulates the Dynamic Multi-Agent Pickup and Delivery problem considering internal order evolution for the first time. Building on the token passing paradigm, we propose two event-triggered online replanning algorithms. The first, Dynamic Token Passing, performs localized replanning upon order updates through add-order decomposition and priority-based token scheduling while preserving collision-free execution. The second, Cooperative Token Passing, further enables idle robots to opportunistically assist newly added pickups, improving system-level efficiency. Simulation results in RCWS environments demonstrate that the proposed methods significantly reduce order flowtime compared with static and non-cooperative baselines.