Maintenance in Railway Rolling Stock Rescheduling for Passenger Railways

This paper addresses the Rolling Stock Rescheduling Problem (RSRP), while taking maintenance appointments into account. After a disruption, the rolling stock of passenger trains has to be rescheduled in order to maintain a feasible rolling stock circulation. A limited number of rolling stock units have a scheduled maintenance appointment during the day: these appointments need to be taken into account while rescheduling. In this paper we propose three different models for this. The Extra Unit Type model extends the known Composition model by adding additional rolling stock types for every rolling stock unit that requires maintenance. The Shadow-Account model keeps track of a shadow account for all units that require maintenance. The Job-Composition model is a combination of the Job model and the Composition model, both known in the literature. Paths are created such that maintenance units are on time for their maintenance appointment. All models are tested on instances of Netherlands Railways. The results show that the models are able to efficiently take maintenance appointments into account

A Comparison of Two Exact Methods for Passenger Railway Rolling Stock (Re)Scheduling

The assignment of rolling stock units to timetable services in passenger railways is an important optimization problem that has been addressed by many papers in different forms. Solution approaches have been proposed for different planning phases: strategic, tactical, and also operational planning. In this paper we compare two approaches within two operational planning phases (i.e. the daily and the real time planning). The first exact approach is based on a Mixed Integer Linear Program (MILP) which is solved using CPLEX. The second approach is an extension of a recently introduced column generation approach. In this paper, we benchmark the performance of the methods on networks of two countries (Denmark and The Netherlands). We use the approaches to make daily schedules and we test their real time applicability by performing tests with different disruption scenarios. The computational experiments demonstrate that both models can be used on both networks and are able to find optimal rolling stock circulations in the different planning phase

Practice Oriented Algorithmic Disruption Management in Passenger Railways

How to deal with a disruption is a question railway companies face on a daily basis. This thesis focusses on the subject how to handle a disruption such that the passenger service is upheld as much as possible. The current mathematical models for disruption management can not yet be applied in practice, because several important practical considerations are not taken into account. In this thesis several models are presented which take important practical details into account: (1) Creating a macroscopic global feasible solution for all three resource schedules, instead of focussing on one individual resource schedule. (2) Scheduled maintenance appointments required

A Variable Neighborhood Search Heuristic for Rolling Stock Rescheduling

We present a Variable Neighborhood Search heuristic for the rolling stock rescheduling problem. Rolling stock rescheduling is needed when a disruption leads to cancellations in the timetable. In rolling stock rescheduling, we then assign duties, i.e., sequences of trips, to the available train units in such a way that both passenger comfort and operational performance are taken into account. For our heuristic, we introduce three neighborhoods that can be used for rolling stock rescheduling, which respectively focus on swapping duties between train units, on improving the individual duties and on changing the shunting that occurs between trips. These neighborhoods are used for both a Variable Neighborhood Descent local search procedure and for perturbing the current solution in order to escape from local optima. We apply our heuristic to instances of Netherlands Railways (NS). The results show that the heuristic is able to find high-quality solutions in a reasonable amount of time. This allows rolling stock dispatchers to use our heuristic in real-time rescheduling