Reinforcement learning for multi-item retrieval in the puzzle-based storage system

TL;DR

This paper introduces a deep reinforcement learning approach to optimize multi-item retrieval in puzzle-based storage systems, significantly improving solution quality and scalability over existing heuristics.

Contribution

The paper develops a novel Double&Dueling Deep Q Network algorithm and a general integer programming model for efficient multi-item retrieval in complex storage systems.

Findings

Reinforcement learning outperforms heuristic algorithms in solution quality.

The approach handles large-scale and simultaneous movement scenarios effectively.

Proposed methods enhance the practicality of puzzle-based storage systems.

Abstract

Nowadays, fast delivery services have created the need for high-density warehouses. The puzzle-based storage system is a practical way to enhance the storage density, however, facing difficulties in the retrieval process. In this work, a deep reinforcement learning algorithm, specifically the Double&Dueling Deep Q Network, is developed to solve the multi-item retrieval problem in the system with general settings, where multiple desired items, escorts, and I/O points are placed randomly. Additionally, we propose a general compact integer programming model to evaluate the solution quality. Extensive numerical experiments demonstrate that the reinforcement learning approach can yield high-quality solutions and outperforms three related state-of-the-art heuristic algorithms. Furthermore, a conversion algorithm and a decomposition framework are proposed to handle simultaneous movement and large-scale instances respectively, thus improving the applicability of the PBS system.