Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
9-2025
Abstract
This study investigates the optimization of storage location in automated storage and retrieval systems (AS/RS). We introduce an optimization approach based on the Deep Q-Network (DQN) algorithm to enhance warehouse task efficiency and minimize stacker travel during storage and retrieval. To accelerate the algorithm training process, we integrate a prioritized experience replay mechanism. Furthermore, we decouple action selection from value estimation within the DQN framework to address the issue of value overestimation. The proposed model is evaluated against three heuristic methods. The experimental results demonstrate that our approach significantly outperforms these baselines.
Keywords
AS/RS, DQN algorithm, Prioritized experience replay, Double DQN
Discipline
Databases and Information Systems | Data Storage Systems
Research Areas
Intelligent Systems and Optimization
Areas of Excellence
Sustainability
Publication
Joint conference 16th International Conference on Computational Logistics (ICCL) | 1st EURO Mini Conference on Maritime Optimization and Logistics (EUROMar), Delft, Netherland, 2025 September 8-10
First Page
1
Last Page
15
Publisher
Springer
City or Country
Cham
Citation
WANG, Lingjun; GUNAWAN, Aldy; and VANSTEENWEGEN, Pieter.
Storage location optimization in automated storage and retrieval systems: A deep reinforcement learning approach. (2025). Joint conference 16th International Conference on Computational Logistics (ICCL) | 1st EURO Mini Conference on Maritime Optimization and Logistics (EUROMar), Delft, Netherland, 2025 September 8-10. 1-15.
Available at: https://ink.library.smu.edu.sg/sis_research/10435
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.