Deakin University
Browse

File(s) under embargo

Deep Reinforcement Learning for Task Assignment and Shelf Reallocation in Smart Warehouses

journal contribution
posted on 2024-05-09, 02:20 authored by SC Wu, Wei-Yu ChiuWei-Yu Chiu, CF Wu
With the rapid development of online shopping and the prosperity of the e-commerce industry in recent years, traditional warehouses are struggling to cope with increasing order volumes. Accordingly, smart warehouses have gained considerable attention for their relatively high efficiency and productivity. In such warehouses, robots transport shelves to picking stations on the basis of tasks assigned to them and then return to the inventory area. An accurate task assignment method must be developed to achieve high efficiency in smart warehouses; however, existing task assignment methods use limited information, resulting in a lack of insight regarding future tasks in warehouses. This paper proposes a method based on the deep Q-network (DQN) that considers inventory for task assignments. The developed DQN-based model determines shelf return locations on the basis of current states to improve warehouse performance. The proposed method was compared with a traditional task assignment method, namely regret and marginal-cost based task assignment algorithm (RMCA); the results indicated that compared with the RMCA method, the proposed approach is more efficient and faster and can accommodate more robots.

History

Journal

IEEE Access

Volume

12

Pagination

58915-58926

Location

Piscataway, N.J.

ISSN

2169-3536

eISSN

2169-3536

Language

eng

Publication classification

C1 Refereed article in a scholarly journal

Issue

99

Publisher

IEEE

Usage metrics

    Research Publications

    Keywords

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC