Distributed optimization for railway track maintenance operations planning


Reference:

M. Faris, A. Núñez, Z. Su, and B. De Schutter, "Distributed optimization for railway track maintenance operations planning," Proceedings of the 2018 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, Hawaii, pp. 1194-1201, Nov. 2018.

Abstract:

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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Bibtex entry:

@inproceedings{FarNun:18-027,
author={M. Faris and A. N{\'{u}}{\~{n}}ez and Z. Su and B. {D}e Schutter},
title={Distributed optimization for railway track maintenance operations planning},
booktitle={Proceedings of the 2018 21st International Conference on Intelligent Transportation Systems (ITSC)},
address={Maui, Hawaii},
pages={1194--1201},
month=nov,
year={2018},
doi={10.1109/ITSC.2018.8569335}
}



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