Many-to-Many Multi-Agent Pickup and Delivery
Ethan Schneider, Jingkai Chen, Tianyi Gu, Kunlei Lian, Seth Hutchinson + 1 more
TLDR
This paper introduces M2M, a novel algorithm for many-to-many multi-agent pickup and delivery in warehouses, outperforming prior methods.
Key contributions
- Addresses the NP-hard many-to-many MAPD problem, common in real-world automated warehouses.
- Proposes M2M algorithm with two variants: M2M (task duration) and M2M-wSKU (SKU distribution).
- M2M consistently outperforms state-of-the-art, completing up to 22,000 more tasks in simulations.
Why it matters
Automated warehouses face complex many-to-many pickup and delivery challenges. This paper's M2M algorithm offers a significant leap in efficiency, enabling robots to manage continuous task streams more effectively. Its superior performance could lead to substantial operational improvements in logistics.
Original Abstract
Multi-robot systems in automated warehouses must manage continuous streams of pickup-and-delivery tasks while ensuring efficiency and safety. Prior work on Multi-Agent Pickup-and-Delivery (MAPD) has largely focused on the one-to-one variant, where each task has a fixed pickup and delivery location. In contrast, real warehouses often present many-to-many MAPD scenarios, where items, tracked by stock keeping unit (SKU) identifiers, can be retrieved from or stored at multiple locations, resulting in an NP-hard four-dimensional assignment problem. To solve the many-to-many MAPD problem, we contribute our algorithm: Many-to-Many Multi-Agent Pickup and Delivery (M2M). We experiment with two variants of our algorithm: one that minimizes estimated task durations (M2M), and one which incorporates SKU distribution into the objective function (M2M-wSKU). Simulation results over 8-hour warehouse operations show that our method consistently matches or outperforms prior state of the art, with M2M completing up to 22,000 more tasks on average across different environments and warehouse inventory densities.
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