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On probability distributions of the time deviation law of container liner ships under interference uncertainty

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  • Yunting Song
  • Nuo Wang

Abstract

Container liner shipping is a kind of transportation mode that is operated according to a schedule. Although the goal is to operate container liner ships on time, the actual arrival time and handling time often deviate from the schedule due to uncertain factors. The identification of a proper probability distribution to describe time deviation law will have a significant impact on accurately recognizing the uncertainty of the operation of container liner ships. In view of this problem, this paper discusses the basic characteristics of container liner ships’ operation time, analyses the properties of relevant probability distributions, and selects representative container ports around the world to collect data on the container liner ships’ operation time for statistical verification. The results show that under schedule constraints and interference uncertainty, the time deviation presents a specific state between a fixed length and random distribution that conforms to the properties of an Erlang distribution. Given that container liner shipping follows the same operation rules worldwide, it is reasonable to deduce that the time deviation law could be generalized to other container ports. Finally, the practical value of this study is demonstrated through quantitative evaluation of port congestion degree under various probabilistic models.

Suggested Citation

  • Yunting Song & Nuo Wang, 2021. "On probability distributions of the time deviation law of container liner ships under interference uncertainty," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(1), pages 354-367, January.
  • Handle: RePEc:bla:jorssa:v:184:y:2021:i:1:p:354-367
    DOI: 10.1111/rssa.12627
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    References listed on IDEAS

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    1. Dulebenets, Maxim A., 2018. "A comprehensive multi-objective optimization model for the vessel scheduling problem in liner shipping," International Journal of Production Economics, Elsevier, vol. 196(C), pages 293-318.
    2. Shabayek, A. A. & Yeung, W. W., 2002. "A simulation model for the Kwai Chung container terminals in Hong Kong," European Journal of Operational Research, Elsevier, vol. 140(1), pages 1-11, July.
    3. Qi, Xiangtong & Song, Dong-Ping, 2012. "Minimizing fuel emissions by optimizing vessel schedules in liner shipping with uncertain port times," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(4), pages 863-880.
    4. Amir Hossein Gharehgozli & Debjit Roy & René de Koster, 2016. "Sea container terminals: New technologies and OR models," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 18(2), pages 103-140, June.
    5. Wang, Shuaian & Meng, Qiang, 2012. "Liner ship route schedule design with sea contingency time and port time uncertainty," Transportation Research Part B: Methodological, Elsevier, vol. 46(5), pages 615-633.
    6. Wang, Yixuan & Wang, Nuo, 2019. "The role of the port industry in China's national economy: An input–output analysis," Transport Policy, Elsevier, vol. 78(C), pages 1-7.
    7. Fadlalla G. Elfadaly & Paul H. Garthwaite, 2020. "On quantifying expert opinion about multinomial models that contain covariates," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 959-981, June.
    8. Li, Qianfei & (Will) Chen, Peng & (Marco) Nie, Yu, 2015. "Finding optimal hyperpaths in large transit networks with realistic headway distributions," European Journal of Operational Research, Elsevier, vol. 240(1), pages 98-108.
    9. Kang, Seungmo & Medina, Juan C. & Ouyang, Yanfeng, 2008. "Optimal operations of transportation fleet for unloading activities at container ports," Transportation Research Part B: Methodological, Elsevier, vol. 42(10), pages 970-984, December.
    10. Song, Yunting & Wang, Nuo & Yu, Anqi, 2019. "Temporal and spatial evolution of global iron ore supply-demand and trade structure," Resources Policy, Elsevier, vol. 64(C).
    11. Di Wu & Nuo Wang & Anqi Yu & Nuan Wu, 2019. "Vulnerability analysis of global container shipping liner network based on main channel disruption," Maritime Policy & Management, Taylor & Francis Journals, vol. 46(4), pages 394-409, May.
    12. Yunting Song & Nuo Wang, 2019. "On probability distributions of the operational law of container liner ships," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(3), pages 943-961, June.
    13. Zhen, Lu, 2015. "Tactical berth allocation under uncertainty," European Journal of Operational Research, Elsevier, vol. 247(3), pages 928-944.
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