Research on Task Allocation in Four-Way Shuttle Storage and Retrieval Systems Based on Deep Reinforcement Learning

Zhongwei Zhang, Jingrui Wang, Jie Jin, Zhaoyun Wu (), Lihui Wu, Tao Peng () and Peng Li
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Zhongwei Zhang: School of Mechanical & Electrical Engineering, Henan University of Technology, Zhengzhou 450001, China
Jingrui Wang: School of Mechanical & Electrical Engineering, Henan University of Technology, Zhengzhou 450001, China
Jie Jin: School of Mechanical & Electrical Engineering, Henan University of Technology, Zhengzhou 450001, China
Zhaoyun Wu: School of Mechanical & Electrical Engineering, Henan University of Technology, Zhengzhou 450001, China
Lihui Wu: School of Mechanical Engineering, Shanghai Institute of Technology, Shanghai 201418, China
Tao Peng: State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310058, China
Peng Li: Zhengzhou Deli Automation Logistics Equipment Manufacturing Co., Ltd., Zhengzhou 452470, China

Sustainability, 2025, vol. 17, issue 15, 1-33

Abstract: The four-way shuttle storage and retrieval system (FWSS/RS) is an advanced automated warehousing solution for achieving green and intelligent logistics, and task allocation is crucial to its logistics efficiency. However, current research on task allocation in three-dimensional storage environments is mostly conducted in the single-operation mode that handles inbound or outbound tasks individually, with limited attention paid to the more prevalent composite operation mode where inbound and outbound tasks coexist. To bridge this gap, this study investigates the task allocation problem in an FWSS/RS under the composite operation mode, and deep reinforcement learning (DRL) is introduced to solve it. Initially, the FWSS/RS operational workflows and equipment motion characteristics are analyzed, and a task allocation model with the total task completion time as the optimization objective is established. Furthermore, the task allocation problem is transformed into a partially observable Markov decision process corresponding to reinforcement learning. Each shuttle is regarded as an independent agent that receives localized observations, including shuttle position information and task completion status, as inputs, and a deep neural network is employed to fit value functions to output action selections. Correspondingly, all agents are trained within an independent deep Q-network (IDQN) framework that facilitates collaborative learning through experience sharing while maintaining decentralized decision-making based on individual observations. Moreover, to validate the efficiency and effectiveness of the proposed model and method, experiments were conducted across various problem scales and transport resource configurations. The experimental results demonstrate that the DRL-based approach outperforms conventional task allocation methods, including the auction algorithm and the genetic algorithm. Specifically, the proposed IDQN-based method reduces the task completion time by up to 12.88% compared to the auction algorithm, and up to 8.64% compared to the genetic algorithm across multiple scenarios. Moreover, task-related factors are found to have a more significant impact on the optimization objectives of task allocation than transport resource-related factors.

Keywords: four-way shuttle storage and retrieval system; composite operation mode; task allocation; deep reinforcement learning; independent deep Q-network (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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