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A GRASP algorithm for the container stowage slot planning problem

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  • Parreño, Francisco
  • Pacino, Dario
  • Alvarez-Valdes, Ramon

Abstract

This work presents a generalization of the Slot Planning Problem which raises when the liner shipping industry needs to plan the placement of containers within a vessel (stowage planning). State-of-the-art stowage planning relies on a heuristic decomposition where containers are first distributed in clusters along the vessel. For each of those clusters a specific position for each container must be found. Compared to previous studies, we have introduced two new features: the explicit handling of rolled out containers and the inclusion of separations rules for dangerous cargo. We present a novel integer programming formulation and a Greedy Randomized Adaptive Search Procedure (GRASP) to solve the problem. The approach is able to find high-quality solution within 1s. We also provide comparison with the state-of-the-art on an existing and a new set of benchmark instances.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:transe:v:94:y:2016:i:c:p:141-157
    DOI: 10.1016/j.tre.2016.07.011
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    References listed on IDEAS

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    1. 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.
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    Cited by:

    1. Fazi, Stefano, 2019. "A decision-support framework for the stowage of maritime containers in inland shipping," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 131(C), pages 1-23.
    2. Iris, Çağatay & Christensen, Jonas & Pacino, Dario & Ropke, Stefan, 2018. "Flexible ship loading problem with transfer vehicle assignment and scheduling," Transportation Research Part B: Methodological, Elsevier, vol. 111(C), pages 113-134.
    3. Turkay Yildiz, 2019. "Design and Analysis of a Lightweight Composite Shipping Container Made of Carbon Fiber Laminates," Logistics, MDPI, vol. 3(3), pages 1-20, July.
    4. Christensen, Jonas & Erera, Alan & Pacino, Dario, 2019. "A rolling horizon heuristic for the stochastic cargo mix problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 123(C), pages 200-220.
    5. Zhang, Daihui & Qu, Zhuohau & Wang, Wenxin & Yu, Jiagen & Yang, Zaili, 2020. "New uncertainty modelling for cargo stowage plans of general cargo ships," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 144(C).
    6. 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.
    7. Byung Kwon Lee & Joyce M. W. Low, 2022. "A constraint programming approach to capacity planning in container vessels," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 24(2), pages 415-438, June.
    8. 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.
    9. 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.

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