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Solving the pre-marshalling problem to optimality with A* and IDA

Author

Listed:
  • Kevin Tierney

    (University of Paderborn)

  • Dario Pacino

    (Technical University of Denmark)

  • Stefan Voß

    (University of Hamburg
    Pontificia Universidad Católica de Valparaíso)

Abstract

We present a novel solution approach to the container pre-marshalling problem using the A* and IDA* algorithms combined with several novel branching and symmetry breaking rules that significantly increases the number of pre-marshalling instances that can be solved to optimality. A* and IDA* are graph search algorithms that use heuristics combined with a complete graph search to find optimal solutions to problems. The container pre-marshalling problem is a key problem for container terminals seeking to reduce delays of inter-modal container transports. The goal of the container pre-marshalling problem is to find the minimal sequence of container movements to shuffle containers in a set of stacks such that the resulting stacks are arranged according to the time each container must leave the stacks. We evaluate our approach on three well-known datasets of pre-marshalling problem instances, solving over 500 previously unsolved instances to optimality, which is nearly twice as many instances as the current state-of-the-art method solves.

Suggested Citation

  • Kevin Tierney & Dario Pacino & Stefan Voß, 2017. "Solving the pre-marshalling problem to optimality with A* and IDA," Flexible Services and Manufacturing Journal, Springer, vol. 29(2), pages 223-259, June.
    • Handle: RePEc:spr:flsman:v:29:y:2017:i:2:d:10.1007_s10696-016-9246-6
    DOI: 10.1007/s10696-016-9246-6
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    References listed on IDEAS

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    Cited by:

    1. Boge, Sven & Goerigk, Marc & Knust, Sigrid, 2020. "Robust optimization for premarshalling with uncertain priority classes," European Journal of Operational Research, Elsevier, vol. 287(1), pages 191-210.
    2. Jin, Bo & Tanaka, Shunji, 2023. "An exact algorithm for the unrestricted container relocation problem with new lower bounds and dominance rules," European Journal of Operational Research, Elsevier, vol. 304(2), pages 494-514.
    3. Damla Kizilay & Deniz Türsel Eliiyi, 2021. "A comprehensive review of quay crane scheduling, yard operations and integrations thereof in container terminals," Flexible Services and Manufacturing Journal, Springer, vol. 33(1), pages 1-42, March.
    4. Parreño-Torres, Consuelo & Alvarez-Valdes, Ramon & Ruiz, Rubén & Tierney, Kevin, 2020. "Minimizing crane times in pre-marshalling problems," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 137(C).
    5. Maniezzo, Vittorio & Boschetti, Marco A. & Gutjahr, Walter J., 2021. "Stochastic premarshalling of block stacking warehouses," Omega, Elsevier, vol. 102(C).
    6. Ignacio Araya & Martín Toledo, 2023. "A fill-and-reduce greedy algorithm for the container pre-marshalling problem," Operational Research, Springer, vol. 23(3), pages 1-29, September.
    7. Parreño-Torres, Consuelo & Alvarez-Valdes, Ramon & Parreño, Francisco, 2022. "A beam search algorithm for minimizing crane times in premarshalling problems," European Journal of Operational Research, Elsevier, vol. 302(3), pages 1063-1078.
    8. Tanaka, Shunji & Voß, Stefan, 2019. "An exact algorithm for the block relocation problem with a stowage plan," European Journal of Operational Research, Elsevier, vol. 279(3), pages 767-781.
    9. Tanaka, Shunji & Tierney, Kevin & Parreño-Torres, Consuelo & Alvarez-Valdes, Ramon & Ruiz, Rubén, 2019. "A branch and bound approach for large pre-marshalling problems," European Journal of Operational Research, Elsevier, vol. 278(1), pages 211-225.
    10. Zweers, Bernard G. & Bhulai, Sandjai & van der Mei, Rob D., 2020. "Optimizing pre-processing and relocation moves in the Stochastic Container Relocation Problem," European Journal of Operational Research, Elsevier, vol. 283(3), pages 954-971.
    11. Filom, Siyavash & Amiri, Amir M. & Razavi, Saiedeh, 2022. "Applications of machine learning methods in port operations – A systematic literature review," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 161(C).

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