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Integrated method for forecasting container slot booking in intercontinental liner shipping service

Author

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  • Yadong Wang

    (National University of Singapore)

  • Qiang Meng

    (National University of Singapore)

Abstract

Intercontinental liner shipping services transport containers between two continents. This paper forecasts the number of containers to be transported through long-haul legs for the incoming trip of an intercontinental shipping service. Considering the clear and unique container slot booking patterns in the historical data, three different forecasting models are developed, including piecewise linear regression model, autoregressive model, and artificial neural network model. These three models are further combined into an integrated model to simultaneously incorporate their merits in formulating the container slot booking patterns. Test results show satisfactory forecasting precisions of the integrated forecasting model.

Suggested Citation

  • Yadong Wang & Qiang Meng, 2019. "Integrated method for forecasting container slot booking in intercontinental liner shipping service," Flexible Services and Manufacturing Journal, Springer, vol. 31(3), pages 653-674, September.
    • Handle: RePEc:spr:flsman:v:31:y:2019:i:3:d:10.1007_s10696-018-9324-z
    DOI: 10.1007/s10696-018-9324-z
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    References listed on IDEAS

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    Cited by:

    1. Hanghang Zhu & Zhi Pei, 2023. "Two-Stage Robust Liner Container Booking with Uncertain Customer Demand," Mathematics, MDPI, vol. 11(8), pages 1-24, April.
    2. Michael F. Gorman & John-Paul Clarke & René Koster & Michael Hewitt & Debjit Roy & Mei Zhang, 2023. "Emerging practices and research issues for big data analytics in freight transportation," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 25(1), pages 28-60, March.
    3. Wang, Tingsong & Meng, Qiang & Wang, Shuaian & Qu, Xiaobo, 2021. "A two-stage stochastic nonlinear integer-programming model for slot allocation of a liner container shipping service," Transportation Research Part B: Methodological, Elsevier, vol. 150(C), pages 143-160.
    4. Kjetil Fagerholt & Kap Hwan Kim & Qiang Meng & Julio César Góez & Frank Meisel & Magnus Stålhane, 2019. "Analytics and models for maritime logistics and systems," Flexible Services and Manufacturing Journal, Springer, vol. 31(3), pages 563-566, September.
    5. Marco Ferretti & Ugo Fiore & Francesca Perla & Marcello Risitano & Salvatore Scognamiglio, 2022. "Deep Learning Forecasting for Supporting Terminal Operators in Port Business Development," Future Internet, MDPI, vol. 14(8), pages 1-19, July.
    6. S. Van Cranenburgh & S. Wang & A. Vij & F. Pereira & J. Walker, 2021. "Choice modelling in the age of machine learning -- discussion paper," Papers 2101.11948, arXiv.org, revised Nov 2021.
    7. Elnaz Irannezhad, 2020. "The Architectural Design Requirements of a Blockchain-Based Port Community System," Logistics, MDPI, vol. 4(4), pages 1-31, November.
    8. Wang, Yadong & Meng, Qiang & Jia, Peng, 2019. "Optimal port call adjustment for liner container shipping routes," Transportation Research Part B: Methodological, Elsevier, vol. 128(C), pages 107-128.
    9. Yu Guo & Ran Yan & Hans Wang, 2021. "Maximization of container slot booking profits for carriers in the liner shipping industry," Journal of Shipping and Trade, Springer, vol. 6(1), pages 1-10, December.
    10. Liang, Jinpeng & Li, Liming & Zheng, Jianfeng & Tan, Zhijia, 2023. "Service-oriented container slot allocation policy under stochastic demand," Transportation Research Part B: Methodological, Elsevier, vol. 176(C).
    11. Michael Boviatsis & George Vlachos, 2022. "Sustainable Operation of Unmanned Ships under Current International Maritime Law," Sustainability, MDPI, vol. 14(12), pages 1-17, June.

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