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Matheuristics for slot planning of container vessel bays

Author

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  • Korach, Aleksandra
  • Brouer, Berit Dangaard
  • Jensen, Rune Møller

Abstract

Stowage planning is an NP-hard combinatorial problem concerned with loading a container vessel in a given port, such that a number of constraints regarding the physical layout of the vessel and its seaworthiness are satisfied, and a number of objectives with regard to the quality of the placement are optimized. State-of-the-art methods decompose the problem into phases, the latter of which, known as slot planning, involves loading the containers into slots of a bay. This article presents an efficient matheuristic for the slot planning problem. Matheuristics are algorithms using mathematical programming techniques within a heuristic framework. The method finds solutions for 96% of 236 instances based on real stowage plans, 90% of them optimally, with an average optimality gap of 4.34% given a limit of one second per instance. This is an improvement over the results provided by previous works.

Suggested Citation

  • Korach, Aleksandra & Brouer, Berit Dangaard & Jensen, Rune Møller, 2020. "Matheuristics for slot planning of container vessel bays," European Journal of Operational Research, Elsevier, vol. 282(3), pages 873-885.
    • Handle: RePEc:eee:ejores:v:282:y:2020:i:3:p:873-885
    DOI: 10.1016/j.ejor.2019.09.042
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    References listed on IDEAS

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    1. David Pisinger & Stefan Ropke, 2010. "Large Neighborhood Search," International Series in Operations Research & Management Science, in: Michel Gendreau & Jean-Yves Potvin (ed.), Handbook of Metaheuristics, chapter 0, pages 399-419, Springer.
    2. Monaco, Maria Flavia & Sammarra, Marcello & Sorrentino, Gregorio, 2014. "The Terminal-Oriented Ship Stowage Planning Problem," European Journal of Operational Research, Elsevier, vol. 239(1), pages 256-265.
    3. Delgado, Alberto & Jensen, Rune Møller & Janstrup, Kira & Rose, Trine Høyer & Andersen, Kent Høj, 2012. "A Constraint Programming model for fast optimal stowage of container vessel bays," European Journal of Operational Research, Elsevier, vol. 220(1), pages 251-261.
    4. I D Wilson & P A Roach, 2000. "Container stowage planning: a methodology for generating computerised solutions," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 51(11), pages 1248-1255, November.
    5. Parreño, Francisco & Pacino, Dario & Alvarez-Valdes, Ramon, 2016. "A GRASP algorithm for the container stowage slot planning problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 94(C), pages 141-157.
    6. Daniela Ambrosino & Davide Anghinolfi & Massimo Paolucci & Anna Sciomachen, 2009. "A new three-step heuristic for the Master Bay Plan Problem," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 11(1), pages 98-120, March.
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    Cited by:

    1. Chaemin Lee & Mun Keong Lee & Jae Young Shin, 2020. "Lashing Force Prediction Model with Multimodal Deep Learning and AutoML for Stowage Planning Automation in Containerships," Logistics, MDPI, vol. 5(1), pages 1-15, December.
    2. Kong, Lingrui & Ji, Mingjun & Gao, Zhendi, 2021. "Joint optimization of container slot planning and truck scheduling for tandem quay cranes," European Journal of Operational Research, Elsevier, vol. 293(1), pages 149-166.
    3. Gao, Yinping & Zhen, Lu, 2024. "A decision framework for decomposed stowage planning for containers," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 183(C).
    4. van Twiller, Jaike & Sivertsen, Agnieszka & Pacino, Dario & Jensen, Rune Møller, 2024. "Literature survey on the container stowage planning problem," European Journal of Operational Research, Elsevier, vol. 317(3), pages 841-857.
    5. Dalia Rashed & Amr Eltawil & Mohamed Gheith, 2021. "A Fuzzy Logic-Based Algorithm to Solve the Slot Planning Problem in Container Vessels," Logistics, MDPI, vol. 5(4), pages 1-24, September.

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