In this paper, distributed optimization approaches are developed for the planning of maintenance operations of large-scale railway infrastructure formulated as a Mixed-Integer Linear Programming (MILP) problem. The proposed planning problem is solved using three different distributed optimization schemes: Parallel Augmented Lagrangian Relaxation (PALR), Alternating Direction Method of Multipliers (ADMM), and Distributed Robust Safe But Knowledgeable (DRSBK). The original distributed algorithms are modified to handle the non-convex nature of the optimization problem and to improve the solution quality. The results of large-scale test instances show that DRSBK can outperform the other distributed approaches, by providing the closest-to-optimum solution while requiring the lowest computation time.


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    Titel :

    Distributed Optimization for Railway Track Maintenance Operations Planning


    Beteiligte:
    Faris, Muhammad (Autor:in) / Nunez, Alfredo (Autor:in) / Su, Zhou (Autor:in) / De Schutter, Bart (Autor:in)


    Erscheinungsdatum :

    2018-11-01


    Format / Umfang :

    241969 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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