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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