Computing path bundles in bipartite networks

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    5 Citations (Scopus)

    Abstract

    Path bundling, a class of path planning problem, consists of compounding multiple routes to minimize a global distance metric. Naturally, a tree-like structure is obtained as a result wherein roots play the role of coordinating the joint transport of information, goods, and people. In this paper we tackle the path bundling problem in bipartite networks by using gradient-free optimization and a convex representation. Then, by using 7,500 computational experiments in diverse scenarios with and without obstacles, implying 7.5 billion shortest path computations, show the feasibility and efficiency of the mesh adaptive search.

    Original languageEnglish
    Title of host publicationSIMULTECH 2017 - Proceedings of the 7th International Conference on Simulation and Modeling Methodologies, Technologies and Applications
    PublisherSciTePress
    Pages422-427
    Number of pages6
    ISBN (Electronic)9789897582653
    Publication statusPublished - 2017
    Event7th International Conference on Simulation and Modeling Methodologies, Technologies and Applications, SIMULTECH 2017 - Madrid, Spain
    Duration: 2017 Jul 262017 Jul 28

    Other

    Other7th International Conference on Simulation and Modeling Methodologies, Technologies and Applications, SIMULTECH 2017
    CountrySpain
    CityMadrid
    Period17/7/2617/7/28

    Keywords

    • Bipartite Networks
    • Optimization
    • Path Bundling

    ASJC Scopus subject areas

    • Computer Science Applications
    • Modelling and Simulation
    • Computational Theory and Mathematics
    • Information Systems

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  • Cite this

    Parque Tenorio, V., Miura, S., & Miyashita, T. (2017). Computing path bundles in bipartite networks. In SIMULTECH 2017 - Proceedings of the 7th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (pp. 422-427). SciTePress.