Distributed Optimization for Railway Track Maintenance Operations Planning

Abstract

An essential component of railway infrastructures is track ballast. As railway track is used frequently by passing rolling stocks, its performance degrades over time. At certain degradation levels, maintenance interventions must be carried out to improve the track performance so to meet technical and safety regulations. In this way, the risk of accident or derailment can be minimized and the railway interoperability is ensured. Furthermore, the responsibility of designing maintenance plan belongs to infrastructure managers. To help them, predictive strategies based on optimization can suggest the optimal schedule to maintain the track over a certain time period. In this way, track performance and maintenance costs can be explicitly optimized over the whole life cycle of the track.

However, a railway network typically consists of multiple track sections, each of them with different degradation level and parameters. Hence, the optimization of track maintenance can be considered as a large-scale problem which has a large number of decision variables. For such kind of problem, the conventional centralized optimization is very difficult or even not tractable to solve due to limitations on the computational time and resources. One way to overcome this issue is by applying the so-called distributed optimization scheme. In such approach, the original optimization problem is partitioned into multiple smaller, tractable subproblems. Therefore, the optimization is tractable and more preferred for real-life implementations.

This thesis develops distributed optimization approaches for track maintenance operations planning problem. Three different schemes are compared: Parallel Augmented Lagrangian Relaxation (PALR), Alternating Direction Method of Multipliers (ADMM), and Distributed Robust Safe But Knowledgeable (DRSBK). As these distributed approaches basically designed for convex problems, extension techniques to handle non-convex nature of the proposed optimization problem are implemented. Furthermore, some case studies are defined to evaluate the algorithms from both performance and numerical perspectives. In simulations of small, medium, and large-scale instances, it is shown that in most cases, DRSBK can outperform the other distributed approaches, by providing the closest-to-optimum solution to the centralized optimization problem while having the shortest computation time.