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Balanced Smart Predict‐Then‐Optimize Framework for Container Yard Intelligent Retrofit Decision‐Making.

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Title: Balanced Smart Predict‐Then‐Optimize Framework for Container Yard Intelligent Retrofit Decision‐Making.
Authors: Li, Xianhui1 (AUTHOR), Yu, Jing2 (AUTHOR), Wang, Xijun3 (AUTHOR), Lu, Houjun2 (AUTHOR) hjlu@shmtu.edu.cn, Iqbal, Umar (AUTHOR) uiqbal1@ilstu.edu
Source: Journal of Advanced Transportation. 6/9/2026, Vol. 2026, p1-20. 20p.
Subjects: Retrofitting, Container terminals, Mixed integer linear programming, Demand forecasting, Long short-term memory, Cost control, Machine learning
Abstract: Intelligent transformation of container yards is essential for increasing terminal capacity. Demand uncertainty may lead to the risk of delays in port container operations. Traditional "predict‐then‐optimize" (PTO) frameworks often yield suboptimal results because forecasting goals are isolated from the actual decision objectives. To bridge this gap, we use a balanced smart predict‐then‐optimize (Bal‐SPO) framework for multiperiod retrofit scheduling. This approach integrates an long short‐term memory (LSTM) network with a mixed‐integer programming (MIP) model through decision‐focused learning. A key innovation is the inclusion of a "balance" term in the loss function. This feature stops too many zones from being closed at once for retrofitting. Experimental results based on historical data demonstrate that this framework reduces operational costs by 17.1% compared to traditional PTO frameworks. It also reduces delay penalty costs by 16.9%. Ultimately, this research provides port managers with a robust and practical decision‐support tool for implementing intelligent yard retrofits in uncertain environments. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Advanced Transportation is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Balanced Smart Predict‐Then‐Optimize Framework for Container Yard Intelligent Retrofit Decision‐Making.
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  Data: <searchLink fieldCode="DE" term="%22Retrofitting%22">Retrofitting</searchLink><br /><searchLink fieldCode="DE" term="%22Container+terminals%22">Container terminals</searchLink><br /><searchLink fieldCode="DE" term="%22Mixed+integer+linear+programming%22">Mixed integer linear programming</searchLink><br /><searchLink fieldCode="DE" term="%22Demand+forecasting%22">Demand forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Long+short-term+memory%22">Long short-term memory</searchLink><br /><searchLink fieldCode="DE" term="%22Cost+control%22">Cost control</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink>
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  Data: Intelligent transformation of container yards is essential for increasing terminal capacity. Demand uncertainty may lead to the risk of delays in port container operations. Traditional "predict‐then‐optimize" (PTO) frameworks often yield suboptimal results because forecasting goals are isolated from the actual decision objectives. To bridge this gap, we use a balanced smart predict‐then‐optimize (Bal‐SPO) framework for multiperiod retrofit scheduling. This approach integrates an long short‐term memory (LSTM) network with a mixed‐integer programming (MIP) model through decision‐focused learning. A key innovation is the inclusion of a "balance" term in the loss function. This feature stops too many zones from being closed at once for retrofitting. Experimental results based on historical data demonstrate that this framework reduces operational costs by 17.1% compared to traditional PTO frameworks. It also reduces delay penalty costs by 16.9%. Ultimately, this research provides port managers with a robust and practical decision‐support tool for implementing intelligent yard retrofits in uncertain environments. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of Journal of Advanced Transportation is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1155/atr/8856441
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      – Code: eng
        Text: English
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        PageCount: 20
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      – SubjectFull: Retrofitting
        Type: general
      – SubjectFull: Container terminals
        Type: general
      – SubjectFull: Mixed integer linear programming
        Type: general
      – SubjectFull: Demand forecasting
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      – SubjectFull: Long short-term memory
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      – SubjectFull: Cost control
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      – SubjectFull: Machine learning
        Type: general
    Titles:
      – TitleFull: Balanced Smart Predict‐Then‐Optimize Framework for Container Yard Intelligent Retrofit Decision‐Making.
        Type: main
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          Name:
            NameFull: Li, Xianhui
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            NameFull: Yu, Jing
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            NameFull: Wang, Xijun
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            NameFull: Lu, Houjun
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            – D: 09
              M: 06
              Text: 6/9/2026
              Type: published
              Y: 2026
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              Value: 2026
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