Many-to-Many Multi-Agent Pickup and Delivery

TL;DR

This paper introduces M2M, a novel algorithm for many-to-many multi-agent pickup and delivery in warehouses, outperforming previous methods in efficiency and task completion.

Contribution

The paper presents the first algorithm specifically designed for many-to-many MAPD scenarios, addressing a complex NP-hard assignment problem in warehouse logistics.

Findings

M2M completes up to 22,000 more tasks on average in simulations.

M2M outperforms prior state-of-the-art methods in various warehouse settings.

Incorporating SKU distribution improves task efficiency.

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.