Railroads use wayside inspection technologies to monitor the health of passing railcars. Because of resource constraints, an efficient and feasible installation plan should be integrated into a multiperiod decision–support framework to derive the maximum benefit over a long planning horizon. A large-scale network optimization framework was proposed to solve this problem. The problem was formulated into two equivalent mathematical models: a maximum coverage model and a K-median model. A set of solution algorithms including a greedy heuristic, a 1-interchange heuristic, and a Lagrangian relaxation method were developed for these models, and their computational performances were investigated. A numerical case study was developed to illustrate the use of these models in real-world settings, and insights into empirical applications of these models were drawn.


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    Titel :

    Installation of Railroad Wayside Defect Detectors


    Untertitel :

    Multiperiod Design


    Weitere Titelangaben:

    Transportation Research Record: Journal of the Transportation Research Board


    Beteiligte:
    Peng, Fan (Autor:in) / Li, Xiaopeng (Autor:in) / Ouyang, Yanfeng (Autor:in)


    Erscheinungsdatum :

    01.01.2011




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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