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A dynamic programming-based matheuristic for the dynamic berth allocation problem

Author

Listed:
  • Tatsushi Nishi

    (Osaka University)

  • Tatsuya Okura

    (Osaka University)

  • Eduardo Lalla-Ruiz

    (University of Hamburg)

  • Stefan Voß

    (University of Hamburg)

Abstract

The increasing maritime traffic forces terminal operators to efficiently reduce the container ships’ service time in order to maintain or increase their market share. This situation gives rise to the well-known berth allocation problem. Its goal is to determine the allocation and the berthing time of container ships arriving to the port with the aim of minimizing the total service time. For tackling this problem, we propose a dynamic programming-based matheuristic that allows to derive lower and upper bounds, and therefore, evaluate the optimality of the provided solutions. Its behavior is assessed on realistic problem instances from the related literature as well as on a new set of larger instances with 150 ships and 15 berths. The results indicate that our proposed approach shows a competitive performance.

Suggested Citation

  • Tatsushi Nishi & Tatsuya Okura & Eduardo Lalla-Ruiz & Stefan Voß, 2020. "A dynamic programming-based matheuristic for the dynamic berth allocation problem," Annals of Operations Research, Springer, vol. 286(1), pages 391-410, March.
    • Handle: RePEc:spr:annopr:v:286:y:2020:i:1:d:10.1007_s10479-017-2715-9
    DOI: 10.1007/s10479-017-2715-9
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    References listed on IDEAS

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    1. Richard K. Congram & Chris N. Potts & Steef L. van de Velde, 2002. "An Iterated Dynasearch Algorithm for the Single-Machine Total Weighted Tardiness Scheduling Problem," INFORMS Journal on Computing, INFORMS, vol. 14(1), pages 52-67, February.
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    4. Jean-François Cordeau & Gilbert Laporte & Pasquale Legato & Luigi Moccia, 2005. "Models and Tabu Search Heuristics for the Berth-Allocation Problem," Transportation Science, INFORMS, vol. 39(4), pages 526-538, November.
    5. J-F Cordeau & G Laporte & A Mercier, 2001. "A unified tabu search heuristic for vehicle routing problems with time windows," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 52(8), pages 928-936, August.
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    Cited by:

    1. Liu, Baoli & Li, Zhi-Chun & Wang, Yadong, 2022. "A two-stage stochastic programming model for seaport berth and channel planning with uncertainties in ship arrival and handling times," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 167(C).
    2. Liu, Baoli & Li, Zhi-Chun & Sheng, Dian & Wang, Yadong, 2021. "Integrated planning of berth allocation and vessel sequencing in a seaport with one-way navigation channel," Transportation Research Part B: Methodological, Elsevier, vol. 143(C), pages 23-47.
    3. Danilo Abril & Carlos D. Paternina-Arboleda & Jesus Velasquez-Bermudez, 2024. "An Integrated Event-Driven Real-Time Tactical–Operational Optimization Framework for Smart Port Operations Planning," Logistics, MDPI, vol. 8(3), pages 1-37, June.
    4. Jaap-Jan Steeg & Menno Oudshoorn & Neil Yorke-Smith, 2023. "Berth planning and real-time disruption recovery: a simulation study for a tidal port," Flexible Services and Manufacturing Journal, Springer, vol. 35(1), pages 70-110, March.
    5. Elham Ziar & Mehdi Seifbarghy & Mahdi Bashiri & Benny Tjahjono, 2023. "An efficient environmentally friendly transportation network design via dry ports: a bi-level programming approach," Annals of Operations Research, Springer, vol. 322(2), pages 1143-1166, March.
    6. Petris, Matteo & Pellegrini, Paola & Pesenti, Raffaele, 2022. "Models and algorithms for an integrated vessel scheduling and tug assignment problem within a canal harbor," European Journal of Operational Research, Elsevier, vol. 300(3), pages 1120-1135.
    7. Marco Antonio Boschetti & Vittorio Maniezzo, 2022. "Matheuristics: using mathematics for heuristic design," 4OR, Springer, vol. 20(2), pages 173-208, June.

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