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Novel formulations and modeling enhancements for the dynamic berth allocation problem

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  • Kramer, Arthur
  • Lalla-Ruiz, Eduardo
  • Iori, Manuel
  • Voß, Stefan

Abstract

This paper addresses the well-known dynamic berth allocation problem (DBAP), which finds numerous applications at container terminals aiming to allocate and schedule incoming container vessels into berthing positions along the quay. Due to its impact on ports’ performance, having efficient DBAP formulations is of great importance, especially for determining optimal schedules in quick time as well as aiding managers and developers in the assessment of solution strategies and approximate approaches. In this work, we propose two novel formulations, a time-indexed formulation and an arc-flow one, to efficiently tackle the DBAP. Additionally, to improve computational performance, we propose problem-based modeling enhancements and a variable-fixing procedure that allows to discard some variables by considering their reduced costs. By means of these contributions, we improve the models’ performance for those instances where the optimal solutions were already known, and we solve to optimality for the first time other instances from the literature.

Suggested Citation

  • Kramer, Arthur & Lalla-Ruiz, Eduardo & Iori, Manuel & Voß, Stefan, 2019. "Novel formulations and modeling enhancements for the dynamic berth allocation problem," European Journal of Operational Research, Elsevier, vol. 278(1), pages 170-185.
    • Handle: RePEc:eee:ejores:v:278:y:2019:i:1:p:170-185
    DOI: 10.1016/j.ejor.2019.03.036
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    Cited by:

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    2. de Lima, Vinícius L. & Alves, Cláudio & Clautiaux, François & Iori, Manuel & Valério de Carvalho, José M., 2022. "Arc flow formulations based on dynamic programming: Theoretical foundations and applications," European Journal of Operational Research, Elsevier, vol. 296(1), pages 3-21.
    3. Liu, Baoli & Li, Zhi-Chun & Wang, Yadong & Sheng, Dian, 2021. "Short-term berth planning and ship scheduling for a busy seaport with channel restrictions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 154(C).
    4. 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.
    5. Zhen, Lu & Zhuge, Dan & Wang, Shuaian & Wang, Kai, 2022. "Integrated berth and yard space allocation under uncertainty," Transportation Research Part B: Methodological, Elsevier, vol. 162(C), pages 1-27.
    6. Wu, Lingxiao & Jia, Shuai & Wang, Shuaian, 2020. "Pilotage planning in seaports," European Journal of Operational Research, Elsevier, vol. 287(1), pages 90-105.
    7. Guo, Liming & Zheng, Jianfeng & Liang, Jinpeng & Wang, Shuaian, 2023. "Column generation for the multi-port berth allocation problem with port cooperation stability," Transportation Research Part B: Methodological, Elsevier, vol. 171(C), pages 3-28.
    8. 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.

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