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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    Title :

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


    Contributors:


    Publication date :

    2022-10-08


    Size :

    653892 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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