Online Multi-Agent Pickup and Delivery with Task Deadlines

TL;DR

This paper addresses online multi-agent pickup and delivery with delivery deadlines, proposing new algorithms that improve task completion times and reduce tardiness in simulated warehouse environments.

Contribution

It introduces the MAPD-D problem, a deadline-aware extension of MAPD, and proposes novel algorithms D-TP and D-TPTS to improve deadline adherence and efficiency.

Findings

D-TP and D-TPTS significantly reduce task tardiness.

Algorithms effectively balance cost and deadline proximity.

Methods outperform existing approaches in simulations.

Abstract

Managing delivery deadlines in automated warehouses and factories is crucial for maintaining customer satisfaction and ensuring seamless production. This study introduces the problem of online multi-agent pickup and delivery with task deadlines (MAPD-D), an advanced variant of the online MAPD problem incorporating delivery deadlines. In the MAPD problem, agents must manage a continuous stream of delivery tasks online. Tasks are added at any time. Agents must complete their tasks while avoiding collisions with each other. MAPD-D introduces a dynamic, deadline-driven approach that incorporates task deadlines, challenging the conventional MAPD frameworks. To tackle MAPD-D, we propose a novel algorithm named deadline-aware token passing (D-TP). The D-TP algorithm calculates pickup deadlines and assigns tasks while balancing execution cost and deadline proximity. Additionally, we introduce the D-TP with task swaps (D-TPTS) method to further reduce task tardiness, enhancing flexibility and efficiency through task-swapping strategies. Numerical experiments were conducted in simulated warehouse environments to showcase the effectiveness of the proposed methods. Both D-TP and D-TPTS demonstrated significant reductions in task tardiness compared to existing methods. Our methods contribute to efficient operations in automated warehouses and factories with delivery deadlines.