This work addresses the development of Distributed Model Predictive Control (DMPC) approaches for the planning of maintenance operations of large-scale railway infrastructure formulated as a Mixed-Integer Linear Programming (MILP) problem. The proposed optimization problem is solved using two different decomposition schemes: 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, hence improving the solution quality. The results of large-scale test instances show that DRSBK can outperform the conventional centralized approach and ADMM, by providing the closest-to-optimum solution while requiring the least computation time.
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