Being an NP-hard problem, multicriteria shortest path search is difficult to solve with speed satisfactory for real-world use. Therefore, this article examines the combination of t-discarding kPC-MLS [1] and multiple pruning heuristics. Apart from comparing the efficiency of the individual techniques, the research also evaluates the ability of $t$-discarding kPC-MLS to employ such heuristics. Since the experiments were conducted on country-size roadmaps, the results are expected to be relevant to real-world applications. According to the measurements, $t$-discarding kPC-MLS gains a higher speedup than standard MLS [2], operating on comparable roadmaps in a matter of seconds.


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    Titel :

    Heuristics for Fast One-to-Many Multicriteria Shortest Path Search


    Beteiligte:
    Kurbanov, Temirlan (Autor:in) / Cuchy, Marek (Autor:in) / Vokrinek, Jiri (Autor:in)


    Erscheinungsdatum :

    2022-10-08


    Format / Umfang :

    653892 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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