Penjadwalan Pemeliharaan Trek Kereta dengan Metode Distributed Model Predictive Control
Abstract
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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