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A Fuzzy Logic-Based Algorithm to Solve the Slot Planning Problem in Container Vessels

Author

Listed:
  • Dalia Rashed

    (Department of Industrial and Manufacturing Engineering, Egypt-Japan University of Science and Technology (E-JUST), Alexandria 21934, Egypt)

  • Amr Eltawil

    (Department of Industrial and Manufacturing Engineering, Egypt-Japan University of Science and Technology (E-JUST), Alexandria 21934, Egypt)

  • Mohamed Gheith

    (Department of Industrial and Manufacturing Engineering, Egypt-Japan University of Science and Technology (E-JUST), Alexandria 21934, Egypt
    Production Engineering Department, Faculty of Engineering, Alexandria University, Alexandria 21544, Egypt)

Abstract

Background: The slot planning problem is a container allocation problem within a certain location on a vessel. It is considered a sub-problem of a successful decomposition approach for the container vessel stowage planning problem. This decision has a direct effect on container handling operations and the vessel berthing time, which are key indicators for the container terminal efficiency. Methods : In this paper, an approach combining a rule-based fuzzy logic algorithm with a rule-based search algorithm is developed to solve the slot planning problem. The rules in the proposed fuzzy logic algorithm aim at improving the objective function and minimizing/eliminating constraint violation. Results: The computational results of 236 slot planning instances illustrate the efficiency and effectiveness of the proposed algorithm. Conclusions : The results show that the proposed approach is fast and can produce optimal or near-optimal solutions for a comprehensive industrial set of instances.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jlogis:v:5:y:2021:i:4:p:67-:d:645132
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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.
    2. Mordecai Avriel & Michal Penn & Naomi Shpirer & Smadar Witteboon, 1998. "Stowage planning for container ships to reduce the number of shifts," Annals of Operations Research, Springer, vol. 76(0), pages 55-71, January.
    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. 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.
    5. 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.
    6. 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.
    7. 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.
    8. R. Roberti & D. Pacino, 2018. "A Decomposition Method for Finding Optimal Container Stowage Plans," Service Science, INFORMS, vol. 52(6), pages 1444-1462, December.
    9. Daniela Ambrosino & Anna Sciomachen, 2015. "Using a Bin Packing Approach for Stowing Hazardous Containers into Containerships," Springer Optimization and Its Applications, in: Giorgio Fasano & János D. Pintér (ed.), Optimized Packings with Applications, edition 1, chapter 0, pages 1-18, Springer.
    10. 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.
    11. 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.
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