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International Conference on Web Information Systems Engineering

WISE 2021: Web Information Systems Engineering – WISE 2021 pp 50–66Cite as

AMBD: Attention Based Multi-Block Deep Learning Model for Warehouse Dwell Time Prediction

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 13081))

Abstract

Warehouse dwell time consists of working time and waiting time of trucks that have loading tasks in a warehouse. Warehouse dwell time prediction plays a crucial role for improving the truck scheduling strategies as well as the truck drivers’ experiences, and further proliferating the efficiency of warehouse logistics. Queuing theory based time prediction methods mainly focus on some queuing events with regularity to conform. However, the warehouse queuing system has a more complex structure. Specifically, the dwell time of any truck depends on the dwell time of its preceding trucks and the loading ability of warehouse. While warehouse loading ability keeps dynamically changing due to several factors like the capacity of the production line and loading device failures. This greatly increases the difficulty of warehouse dwell time predicting. In this paper, we first put forward the definition of block to represent the loading task statuses of different trucks. On the basis of that, we propose a deep learning based multi-block dwell time prediction model, called AMBD. It incorporates the loading ability of warehouse and the execution process of loading tasks of preceding trucks in the queue. Moreover, to proliferate the precision of warehouse dwell time prediction, we introduce attention mechanism to extract strong correlations among the trucks’ dwell time. Experimental results on real-world steel logistics data demonstrate the efficacy of our proposed models.

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Acknowledgments

This research was supported by NSFC (Nos. 62072180, U1911203 and U1811264).

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Authors and Affiliations

  1. School of Data Science and Engineering, East China Normal University, Shanghai, China

    Xingyi Lv, Wei Zhao, Jiali Mao & Aoying Zhou

  2. Jing Chuang Zhi Hui (Shanghai) Logistics Technology, Shanghai, China

    Ye Guo

Authors
  1. Xingyi Lv
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  2. Wei Zhao
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  3. Jiali Mao
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  4. Ye Guo
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  5. Aoying Zhou
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Corresponding author

Correspondence to Jiali Mao .

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Editors and Affiliations

  1. School of Computer Science and Engineering, The University of New South Wales, Sydney, NSW, Australia

    Wenjie Zhang

  2. Peking University, Beijing, China

    Lei Zou

  3. Zayed University, Dubai, United Arab Emirates

    Zakaria Maamar

  4. Swinburne University of Technology, Melbourne, VIC, Australia

    Lu Chen

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Lv, X., Zhao, W., Mao, J., Guo, Y., Zhou, A. (2021). AMBD: Attention Based Multi-Block Deep Learning Model for Warehouse Dwell Time Prediction. In: Zhang, W., Zou, L., Maamar, Z., Chen, L. (eds) Web Information Systems Engineering – WISE 2021. WISE 2021. Lecture Notes in Computer Science(), vol 13081. Springer, Cham. https://doi.org/10.1007/978-3-030-91560-5_4

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  • DOI: https://doi.org/10.1007/978-3-030-91560-5_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-91559-9

  • Online ISBN: 978-3-030-91560-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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