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Stowing a containership: the master bay plan problem

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  • Ambrosino, Daniela
  • Sciomachen, Anna
  • Tanfani, Elena

Abstract

In this paper we are involved with the so-called master bay plan problem (MBPP), that is the problem of finding optimal plans for stowing containers into a containership, with respect to a set of structural and operational restrictions. We describe in detail such constraints and give a basic 0-1 Linear Programming model for MBPP. Successively, we present a heuristic approach that enables us to relax some relations from the model and give some prestowage rules for being able to solve this combinatorial optimization problem. In particular, we split the set of available locations of the ship into different subsets and force the stowage of containers within them depending on their features and handling operations. A validation of the proposed approach is given together with the analysis of real instances of the problem coming from a maritime terminal located in the city of Genoa.

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  • Ambrosino, Daniela & Sciomachen, Anna & Tanfani, Elena, 2004. "Stowing a containership: the master bay plan problem," Transportation Research Part A: Policy and Practice, Elsevier, vol. 38(2), pages 81-99, February.
    • Handle: RePEc:eee:transa:v:38:y:2004:i:2:p:81-99
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    Cited by:

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    2. 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.
    3. Imai, Akio & Sasaki, Kazuya & Nishimura, Etsuko & Papadimitriou, Stratos, 2006. "Multi-objective simultaneous stowage and load planning for a container ship with container rehandle in yard stacks," European Journal of Operational Research, Elsevier, vol. 171(2), pages 373-389, June.
    4. 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.
    5. 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.
    6. Goerigk, Marc & Knust, Sigrid & Le, Xuan Thanh, 2016. "Robust storage loading problems with stacking and payload constraints," European Journal of Operational Research, Elsevier, vol. 253(1), pages 51-67.
    7. 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.
    8. Christensen, Jonas & Pacino, Dario, 2017. "A matheuristic for the Cargo Mix Problem with Block Stowage," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 97(C), pages 151-171.
    9. 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.
    10. Sciomachen, Anna & Tanfani, Elena, 2007. "A 3D-BPP approach for optimising stowage plans and terminal productivity," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1433-1446, December.
    11. Ding, Ding & Chou, Mabel C., 2015. "Stowage planning for container ships: A heuristic algorithm to reduce the number of shifts," European Journal of Operational Research, Elsevier, vol. 246(1), pages 242-249.

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