Transaction selection policy in tier-to-tier SBSRS by using Deep Q-Learning

Bartu Arslan, Banu Yetkin Ekren (Corresponding author)

Research output: Contribution to journalArticleAcademicpeer-review

10 Citations (Scopus)
18 Downloads (Pure)

Abstract

This paper studies a Deep Q-Learning (DQL) method for transaction sequencing problems in an automated warehousing system, Shuttle-based Storage and Retrieval System (SBSRS), in which shuttles can move between tiers flexibly. Here, the system is referred to as tier-to-tier SBSRS (t-SBSRS), developed as an alternative design to tier-captive SBSRS (c-SBSRS). By the flexible travel of shuttles between tiers in t-SBSRS, the number of shuttles in the system may be reduced compared to its simulant c-SBSRS design. The flexible travel of shuttles makes the operation decisions more complex in that system, motivating us to explore whether integration of a machine learning approach would help to improve the system performance. We apply the DQL method for the transaction selection of shuttles in the system to attain process time advantage. The outcomes of the DQN are confronted with the well-applied heuristic approaches: first-come-first-serve (FIFO) and shortest process time (SPT) rules under different racking and numbers of shuttles scenarios. The results show that DQL outperforms the FIFO and SPT rules promising for the future of smart industry applications. Especially, compared to the well-applied SPT rule in industries, DQL improves the average cycle time per transaction by roughly 43% on average.
Original languageEnglish
Pages (from-to)7353-7366
Number of pages14
JournalInternational Journal of Production Research
Volume61
Issue number21
Early online date30 Nov 2022
DOIs
Publication statusPublished - 2023

Keywords

  • SBSRS
  • automated warehousing
  • deep reinforcement learning
  • DQN
  • agent-based simulation
  • logistics
  • Logistics

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  • Smart Transaction Picking in Tier-to-tier SBS/RS by Deep Q-Learning

    Arslan, B. & Yetkin Ekren, B., 2021, Proceedings of the 11th Annual International Conference on Industrial Engineering and Operations Management, 2021. IEOM Society, p. 6415-6425 11 p. 1099

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    Open Access

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