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The berth allocation problem: Optimizing vessel arrival time

Author

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  • Mihalis M Golias

    (University of Memphis, 3815 Central Avenue, Memphis, Tennessee 38152, USA)

  • Georgios K Saharidis

    (Rutgers, The State University of New Jersey, 623 Bowser Road, Piscataway, New Jersey 08854, USA)

  • Maria Boile

    (Rutgers, The State University of New Jersey, 623 Bowser Road, Piscataway, New Jersey 08854, USA)

  • Sotirios Theofanis

    (Rutgers, The State University of New Jersey, 623 Bowser Road, Piscataway, New Jersey 08854, USA)

  • Marianthi G Ierapetritou

    (Rutgers, The State University of New Jersey, 623 Bowser Road, Piscataway, New Jersey 08854, USA)

Abstract

The berth scheduling problem deals with the assignment of vessels to berths in a marine terminal, with the objective to maximize the ocean carriers’ satisfaction (minimize delays) and/or minimize the terminal operator's costs. In the existing literature, two main assumptions are made regarding the status of a vessel: (a) either all vessels to be served are already in the port before the planning period starts, or (b) they are scheduled to arrive after the planning period starts. The latter case assumes an expected time of arrival for each vessel, which is a function of the departure time of the vessel from the previous port, the average operating speed and the distance between the two ports. Recent increases in fuel prices have forced ocean carriers to reduce current operating speeds, while stressing to terminal operators the need to maintain the integrity of their schedule. In addition, several collaborative efforts between industry and government agencies have been proposed, aiming to reduce emissions from marine vessels and port operations. In light of these issues, this article presents a berth-scheduling policy to minimize vessel delayed departures and indirectly reduce fuel consumption and emissions produced by the vessels while in idle mode. Vessel arrival times are considered as a variable and are optimized to accommodate the objectives of the proposed policy while providing ocean carriers with an optimized vessel speed. Example problems using real data show that the proposed policy reduces the amount of emissions produced by vessels at the port in idle mode, optimizes fuel consumption and waiting time at the port by reducing vessel operating speeds to optimal levels and minimizes the effects of late arrivals to the ocean carriers’ schedule.

Suggested Citation

  • Mihalis M Golias & Georgios K Saharidis & Maria Boile & Sotirios Theofanis & Marianthi G Ierapetritou, 2009. "The berth allocation problem: Optimizing vessel arrival time," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 11(4), pages 358-377, December.
  • Handle: RePEc:pal:marecl:v:11:y:2009:i:4:p:358-377
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    Cited by:

    1. Song, Shuang & Govindan, Kannan & Xu, Lei & Du, Peng & Qiao, Xiaojiao, 2017. "Capacity and production planning with carbon emission constraints," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 97(C), pages 132-150.
    2. Elio Canestrelli & Marco Corazza & Giuseppe Nadai & Raffaele Pesenti, 2017. "Managing the Ship Movements in the Port of Venice," Networks and Spatial Economics, Springer, vol. 17(3), pages 861-887, September.
    3. Albert W. Veenstra & Rogier L. A. Harmelink, 2022. "Process mining ship arrivals in port: the case of the Port of Antwerp," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 24(3), pages 584-601, September.
    4. Wang, Yadong & Meng, Qiang & Du, Yuquan, 2015. "Liner container seasonal shipping revenue management," Transportation Research Part B: Methodological, Elsevier, vol. 82(C), pages 141-161.
    5. Yu, Jingjing & Tang, Guolei & Song, Xiangqun, 2022. "Collaboration of vessel speed optimization with berth allocation and quay crane assignment considering vessel service differentiation," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 160(C).
    6. Jia, Shuai & Li, Chung-Lun & Xu, Zhou, 2020. "A simulation optimization method for deep-sea vessel berth planning and feeder arrival scheduling at a container port," Transportation Research Part B: Methodological, Elsevier, vol. 142(C), pages 174-196.
    7. Guo, Liming & Zheng, Jianfeng & Du, Haoming & Du, Jian & Zhu, Zhihong, 2022. "The berth assignment and allocation problem considering cooperative liner carriers," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).
    8. Hamed Hasheminia & Changmin Jiang, 2017. "Strategic trade-off between vessel delay and schedule recovery: an empirical analysis of container liner shipping," Maritime Policy & Management, Taylor & Francis Journals, vol. 44(4), pages 458-473, May.
    9. Arijit De & Saurabh Pratap & Akhilesh Kumar & M. K. Tiwari, 2020. "A hybrid dynamic berth allocation planning problem with fuel costs considerations for container terminal port using chemical reaction optimization approach," Annals of Operations Research, Springer, vol. 290(1), pages 783-811, July.
    10. Kai Li & Yongqiang Zhuo & Xiaoqing Luo, 2022. "Optimization method of fuel saving and cost reduction of tugboat main engine based on genetic algorithm," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(1), pages 605-614, March.
    11. Peter Andersson & Pernilla Ivehammar, 2017. "Dynamic route planning in the Baltic Sea Region – A cost-benefit analysis based on AIS data," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 19(4), pages 631-649, December.
    12. Rodrigues, Filipe & Agra, Agostinho, 2022. "Berth allocation and quay crane assignment/scheduling problem under uncertainty: A survey," European Journal of Operational Research, Elsevier, vol. 303(2), pages 501-524.
    13. Du, Yuquan & Chen, Qiushuang & Quan, Xiongwen & Long, Lei & Fung, Richard Y.K., 2011. "Berth allocation considering fuel consumption and vessel emissions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 47(6), pages 1021-1037.
    14. Yuquan Du & Qiushuang Chen & Jasmine Siu Lee Lam & Ya Xu & Jin Xin Cao, 2015. "Modeling the Impacts of Tides and the Virtual Arrival Policy in Berth Allocation," Transportation Science, INFORMS, vol. 49(4), pages 939-956, November.
    15. Bierwirth, Christian & Meisel, Frank, 2015. "A follow-up survey of berth allocation and quay crane scheduling problems in container terminals," European Journal of Operational Research, Elsevier, vol. 244(3), pages 675-689.
    16. Ronald A. Halim & Lucie Kirstein & Olaf Merk & Luis M. Martinez, 2018. "Decarbonization Pathways for International Maritime Transport: A Model-Based Policy Impact Assessment," Sustainability, MDPI, vol. 10(7), pages 1-30, June.
    17. Li, Chen & Qi, Xiangtong & Song, Dongping, 2016. "Real-time schedule recovery in liner shipping service with regular uncertainties and disruption events," Transportation Research Part B: Methodological, Elsevier, vol. 93(PB), pages 762-788.
    18. 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.
    19. Wang, Shuaian & Meng, Qiang, 2012. "Sailing speed optimization for container ships in a liner shipping network," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(3), pages 701-714.
    20. Meng, Qiang & Du, Yuquan & Wang, Yadong, 2016. "Shipping log data based container ship fuel efficiency modeling," Transportation Research Part B: Methodological, Elsevier, vol. 83(C), pages 207-229.
    21. Shucheng Yu & Shuaian Wang & Lu Zhen, 2017. "Quay crane scheduling problem with considering tidal impact and fuel consumption," Flexible Services and Manufacturing Journal, Springer, vol. 29(3), pages 345-368, December.
    22. Ahmed Karam & Amr Eltawil & Kristian Hegner Reinau, 2020. "Energy-Efficient and Integrated Allocation of Berths, Quay Cranes, and Internal Trucks in Container Terminals," Sustainability, MDPI, vol. 12(8), pages 1-24, April.
    23. Raeesi, Ramin & Sahebjamnia, Navid & Mansouri, S. Afshin, 2023. "The synergistic effect of operational research and big data analytics in greening container terminal operations: A review and future directions," European Journal of Operational Research, Elsevier, vol. 310(3), pages 943-973.
    24. Facchini, F. & Digiesi, S. & Mossa, G., 2020. "Optimal dry port configuration for container terminals: A non-linear model for sustainable decision making," International Journal of Production Economics, Elsevier, vol. 219(C), pages 164-178.

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