Container ships travel on 'round-robin' routes. At each port of destination (POD) along the ship's journey containers may be unloaded whilst additional containers destined for subsequent ports may be loaded. Determining which legal configuration of containerss best facilitates this process, in a cost- effective way, comprises the container stowage problem. Determining all acceptable arrangement of containers is an error-prone process that traditionally relies oil the intuitive skills of human planners. The planner must, determine a suitable arrangement of containers so that constraints are satisfied and handling costs are minimised. The paper explains and compares a range of approaches to the solution of the container-ship stowage problem, which focuses on the reliable production of valid, sub-optimal solutions. These include the strong decomposition of the problem into different conceptual levels of planning to which branch & bound, Tabu search techniques and Genetic algorithms have been applied. A particular focus lies on the relationships between the methods of solution and the corresponding models of cargo and stowage spaces, and the consequences that these models have for the accuracy and usefulness of the solutions produced.


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    Titel :

    Artificial intelligence for automatic container stowage planning optimisation


    Weitere Titelangaben:

    Künstliche Intelligenz für die Optimierung der automatischen Container-Stauplanung


    Beteiligte:
    Kemp, S.E. (Autor:in) / Roach, P.A. (Autor:in) / Ware, A.J. (Autor:in) / Wilson, I.D. (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2003


    Format / Umfang :

    6 Seiten, 15 Quellen



    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Print


    Sprache :

    Englisch




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