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Sortation Control Using Multi-Agent Deep Reinforcement Learning in N-Grid Sortation System.

Ju-Bong Kim1, Ho-Bin Choi1, Gyu-Young Hwang1

  • 1Department of Computer Science and Engineering, Korea University of Technology and Education, Cheonan 31253, Korea.

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Summary
This summary is machine-generated.

This study introduces a novel n-grid intralogistics sortation system controlled by collaborative multi-agent reinforcement learning (RL). The system effectively optimizes parcel routing and flow management within warehouses.

Keywords:
multi-agent reinforcement learningn-grid sortation systemreinforcement learningsortation system

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Area of Science:

  • Logistics and Supply Chain Management
  • Artificial Intelligence
  • Robotics and Automation

Background:

  • Increasing parcel volumes necessitate advanced intralogistics solutions.
  • Current sortation systems aim for rapid and accurate parcel routing.
  • Flexible and automated systems are crucial for modern warehouses.

Purpose of the Study:

  • To design a flexible n-grid sortation system for intralogistics.
  • To develop a collaborative multi-agent reinforcement learning (RL) algorithm for system control.
  • To optimize parcel sorting speed and accuracy through intelligent agent behavior.

Main Methods:

  • Design of an n-grid sortation system architecture.
  • Development of a collaborative multi-agent RL algorithm with emission and routing agents.
  • Implementation and verification within a cyber-physical system simulator.

Main Results:

  • Trained RL agents demonstrated effective performance optimization.
  • Routing agents learned to identify and utilize optimal parcel paths.
  • Emission agents successfully balanced parcel inflow and outflow.

Conclusions:

  • The proposed n-grid system and collaborative RL algorithm are effective for intralogistics.
  • Multi-agent RL enables intelligent control for optimized sortation.
  • The system offers a flexible and efficient solution for warehouse automation.