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Exploring Freight Loading Management by Deep Learning: a Case Study in Home Furnishing Industry

Author

Listed:
  • Wei Deng

    (Guizhou University of Finance and Economics
    Nebraska Furniture Mart)

  • Rajvardhan Patil

    (Arkansas Tech University)

  • Fangyao Liu

    (University of Nebraska at Omaha)

  • Ergu Daji

    (Southwest Minzu University)

  • Yong Shi

    (University of Nebraska at Omaha
    The Chinese Academy of Sciences
    Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

Abstract

Long short-term memory (LSTM) networks as state-of-the-art Deep Learning models, have achieved remarkable results in time series forecasting. However, they are less commonly applied to the industry of logistics. This paper presents two novel LSTM networks to predict the freight loading of routing areas, and the design of a smart loading management system is introduced. While most existing works on LSTM utilize the power of its prediction very well, our study shows less accurate and instable results if only LSTM network is applied, that arise from the big variation in the dataset. Instead, two constraints are inspired in this paper. The first constraint adds a time factor node to strengthen the correlation between predicted final loading units and booked loading units as close to delivery date. And the second proposal extends to parallel training by groupwise constraint. Experiments with across 3-year records in a national wide home furnishing retail company show that constraint LSTM networks significantly improve both the accuracy and stability of the prediction. Besides, the design of a smart loading management system will be shown, in which LSTM model plays the core rule to predict loading capacity, meanwhile the rule-based system triggers different events based on loading prediction and truck size.

Suggested Citation

  • Wei Deng & Rajvardhan Patil & Fangyao Liu & Ergu Daji & Yong Shi, 2022. "Exploring Freight Loading Management by Deep Learning: a Case Study in Home Furnishing Industry," Annals of Data Science, Springer, vol. 9(2), pages 213-228, April.
    • Handle: RePEc:spr:aodasc:v:9:y:2022:i:2:d:10.1007_s40745-021-00357-6
    DOI: 10.1007/s40745-021-00357-6
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    References listed on IDEAS

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    2. Wei Bao & Jun Yue & Yulei Rao, 2017. "A deep learning framework for financial time series using stacked autoencoders and long-short term memory," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-24, July.
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