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Online Multi-Agent Pickup and Delivery with Task Deadlines

Hiroya Makino, Seigo Ito

TL;DR

This paper defines online MAPD-D, a deadline-aware extension of multi-agent pickup-and-delivery where tasks arrive over time and each carries a delivery deadline. It introduces two algorithms, deadline-aware token passing (D-TP) and D-TP with task swaps (D-TPTS), to compute pickup deadlines and assign tasks by balancing execution cost with deadline proximity, with additional flexibility from task swapping and switching to further reduce tardiness. Numerical experiments in a warehouse-like grid show significant tardiness reductions compared with online MAPD baselines, and highlight the trade-offs controlled by the weighting parameter $\alpha$ and the use of swaps/switches. The work demonstrates practical impact for automated warehouses and factories by enabling deadline-aware, online task management, and outlines future directions for scaling, decentralized solutions, and obstacle-rich environments.

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.

Online Multi-Agent Pickup and Delivery with Task Deadlines

TL;DR

This paper defines online MAPD-D, a deadline-aware extension of multi-agent pickup-and-delivery where tasks arrive over time and each carries a delivery deadline. It introduces two algorithms, deadline-aware token passing (D-TP) and D-TP with task swaps (D-TPTS), to compute pickup deadlines and assign tasks by balancing execution cost with deadline proximity, with additional flexibility from task swapping and switching to further reduce tardiness. Numerical experiments in a warehouse-like grid show significant tardiness reductions compared with online MAPD baselines, and highlight the trade-offs controlled by the weighting parameter and the use of swaps/switches. The work demonstrates practical impact for automated warehouses and factories by enabling deadline-aware, online task management, and outlines future directions for scaling, decentralized solutions, and obstacle-rich environments.

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.
Paper Structure (19 sections, 2 equations, 6 figures, 2 tables, 2 algorithms)

This paper contains 19 sections, 2 equations, 6 figures, 2 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. Problem Definition
  4. MAPD-D problem
  5. Well-formed MAPD-D Instances
  6. Proposed Method
  7. D-TP
  8. Calculation of Pickup Deadlines
  9. Deadline-aware Task Assignment
  10. Algorithm
  11. D-TPTS
  12. Task Swapping Among Agents
  13. Task Switching
  14. Algorithm
  15. Completeness
  16. ...and 4 more sections

Figures (6)

  • Figure 1: Simulation examples in a four-neighbor grid environment ma2017. Unlike MAPD ma2017, a deadline is set for each task in MAPD-D. Each agent searches for a path from the pickup to delivery locations for the assigned task.
  • Figure 2: Comparison of task assignment methods: Our method assigns tasks by considering the execution cost and proximity of deadlines, thereby effectively reducing tardiness.
  • Figure 3: Four-neighbor grid environment ma2017. A pair of pickup and delivery locations with the same color represents a single task. In MAPD-D, each delivery location is assigned a deadline.
  • Figure 4: Comparisons of cumulative tardiness.
  • Figure : Token passing for tasks with deadlines (TP-D)
  • ...and 1 more figures