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A smart predict-then-optimize method for targeted and cost-effective maritime transportation

Author

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  • Tian, Xuecheng
  • Yan, Ran
  • Liu, Yannick
  • Wang, Shuaian

Abstract

In maritime transportation, port state control (PSC) is the last line of defense against substandard ships. During a PSC inspection, PSC officers (PSCOs) identify ship deficiencies that lead to a ship's detention, which can cause severe economic and reputational losses to the ship operator. Therefore, this study innovatively uses PSC inspection data to design ship maintenance plans for ship operators to minimize overall operational costs. We identify three types of operational costs associated with each deficiency code: inspection cost, repair cost, and risk cost in the ship operators’ decision-making process. The risk cost of a deficiency code is strongly related to the detention contribution of the deficiency items under a deficiency code, as indicated by the feature importance of that code in the random forest (RF) model used to predict detention outcomes. To design ship maintenance plans, the sequential predict-then-optimize (PO) method typically solves the optimization problem using input parameters, including the predicted probabilities of having deficiency items under each code and the three types of operational costs. However, the loss function in this two-stage framework does not consider the effect of predictions on the downstream decisions. Hence, we use a smart predict-then-optimize (SPO) method using an ensemble of SPO trees (SPOTs). Each SPOT uses an SPO loss function that measures the sub-optimality of the decisions derived from the predicted parameters. By exploiting the structural properties of the optimization problem analyzed in this study, we demonstrate that training an SPOT for this problem can be simplified tremendously by using the relative class frequency of true labels within a leaf node to yield a minimum SPO loss. Computational experiments show that the SPO-based ship maintenance scheme is superior to other schemes and can reduce a ship's total operating expenses by approximately 1% over the PO-based scheme and by at least 3% over schemes that do not use machine learning methods. In the long run, SPO-based ship maintenance plans also improve the efficiency of port logistics by reducing the resources needed for formal PSC inspections and alleviating port congestion.

Suggested Citation

  • Tian, Xuecheng & Yan, Ran & Liu, Yannick & Wang, Shuaian, 2023. "A smart predict-then-optimize method for targeted and cost-effective maritime transportation," Transportation Research Part B: Methodological, Elsevier, vol. 172(C), pages 32-52.
    • Handle: RePEc:eee:transb:v:172:y:2023:i:c:p:32-52
    DOI: 10.1016/j.trb.2023.03.009
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    References listed on IDEAS

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    1. Wang, Shuaian & Yan, Ran & Qu, Xiaobo, 2019. "Development of a non-parametric classifier: Effective identification, algorithm, and applications in port state control for maritime transportation," Transportation Research Part B: Methodological, Elsevier, vol. 128(C), pages 129-157.
    2. Wu-Hsun Chung & Sheng-Long Kao & Chun-Min Chang & Chien-Chung Yuan, 2020. "Association rule learning to improve deficiency inspection in port state control," Maritime Policy & Management, Taylor & Francis Journals, vol. 47(3), pages 332-351, April.
    3. Selin Merdan & Christine L. Barnett & Brian T. Denton & James E. Montie & David C. Miller, 2021. "OR Practice–Data Analytics for Optimal Detection of Metastatic Prostate Cancer," Operations Research, INFORMS, vol. 69(3), pages 774-794, May.
    4. Wang, Shuaian & Yan, Ran, 2023. "Fundamental challenge and solution methods in prescriptive analytics for freight transportation," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 169(C).
    5. Dimitris Bertsimas & Nathan Kallus, 2020. "From Predictive to Prescriptive Analytics," Management Science, INFORMS, vol. 66(3), pages 1025-1044, March.
    6. Shubo Wu & Xinqiang Chen & Chaojian Shi & Junjie Fu & Ying Yan & Shengzheng Wang, 2022. "Ship detention prediction via feature selection scheme and support vector machine (SVM)," Maritime Policy & Management, Taylor & Francis Journals, vol. 49(1), pages 140-153, January.
    7. Yang, Zhisen & Yang, Zaili & Yin, Jingbo & Qu, Zhuohua, 2018. "A risk-based game model for rational inspections in port state control," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 118(C), pages 477-495.
    8. Wang, Tingsong & Wang, Xinchang & Meng, Qiang, 2018. "Joint berth allocation and quay crane assignment under different carbon taxation policies," Transportation Research Part B: Methodological, Elsevier, vol. 117(PA), pages 18-36.
    9. Yang, Zhisen & Yang, Zaili & Yin, Jingbo, 2018. "Realising advanced risk-based port state control inspection using data-driven Bayesian networks," Transportation Research Part A: Policy and Practice, Elsevier, vol. 110(C), pages 38-56.
    10. Ng, ManWo, 2015. "Container vessel fleet deployment for liner shipping with stochastic dependencies in shipping demand," Transportation Research Part B: Methodological, Elsevier, vol. 74(C), pages 79-87.
    11. Yan, Ran & Wang, Shuaian & Cao, Jiannong & Sun, Defeng, 2021. "Shipping Domain Knowledge Informed Prediction and Optimization in Port State Control," Transportation Research Part B: Methodological, Elsevier, vol. 149(C), pages 52-78.
    12. Sun, Zhuo & Zheng, Jianfeng, 2016. "Finding potential hub locations for liner shipping," Transportation Research Part B: Methodological, Elsevier, vol. 93(PB), pages 750-761.
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    Cited by:

    1. Tian, Xuecheng & Wang, Shuaian & Laporte, Gilbert & Yang, Ying, 2024. "Determinism versus uncertainty: Examining the worst-case expected performance of data-driven policies," European Journal of Operational Research, Elsevier, vol. 318(1), pages 242-252.
    2. Shuaian Wang & Xuecheng Tian, 2023. "A Deficiency of the Predict-Then-Optimize Framework: Decreased Decision Quality with Increased Data Size," Mathematics, MDPI, vol. 11(15), pages 1-9, July.
    3. Yan, Ran & Wang, Shuaian & Zhen, Lu, 2023. "An extended smart “predict, and optimize” (SPO) framework based on similar sets for ship inspection planning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 173(C).
    4. Yan, Ran & Liu, Yan & Wang, Shuaian, 2024. "A data-driven optimization approach to improving maritime transport efficiency," Transportation Research Part B: Methodological, Elsevier, vol. 180(C).
    5. Xuecheng Tian & Yanxia Guan & Shuaian Wang, 2023. "A Decision-Focused Learning Framework for Vessel Selection Problem," Mathematics, MDPI, vol. 11(16), pages 1-13, August.
    6. Qiaoyun Guo & Abdol Aziz Shahraki, 2023. "Locating Transportation Logistics Centers and Their Dynamic Synergy for Equilibrium Economic Behavior," Sustainability, MDPI, vol. 15(16), pages 1-16, August.
    7. Huang, Di & Zhang, Jinyu & Liu, Zhiyuan & He, Yiliu & Liu, Pan, 2024. "A novel ranking method based on semi-SPO for battery swapping allocation optimization in a hybrid electric transit system," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 188(C).

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