Gábor Maróti

The New Dutch Timetable: The OR Revolution

In December 2006, Netherlands Railways introduced a completely new timetable. Its objective was to facilitate the growth of passenger and freight transport on a highly utilized railway network, and improve the robustness of the timetable resulting in less train delays in the operation. Further adjusting the existing timetable constructed in 1970 was not option anymore, because further growth would then require significant investments in the rail infrastructure. Constructing a railway timetable from scratch for about 5,500 daily trains was a complex problem. To support this process, we generated several timetables using sophisticated operations research techniques, and finally selected and implemented one of these timetables. Furthermore, because rolling-stock and crew costs are principal components of the cost of a passenger railway operator, we used innovative operations research tools to devise efficient schedules for these two resources. The new resource schedules and the increased number of passengers resulted in an additional annual profit of 40 million euros (

A rolling stock circulation model for combining and splitting of passenger trains

60 million) of which about 10 million euros were created by additional revenues. We expect this to increase to 70 million euros (

Disruption Management in Passenger Railway Transportation

105 million) annually in the coming years. However, the benefits of the new timetable for the Dutch society as a whole are much greater: more trains are transporting more passengers on the same railway infrastructure, and these trains are arriving and departing on schedule more than they ever have in the past. In addition, the rail transport system will be able to handle future transportation demand growth and thus allow cities to remain accessible. Therefore, people can switch from car transport to rail transport, which will reduce the emission of greenhouse gases.

A Rolling Horizon Approach for Disruption Management of Railway Rolling Stock

This paper addresses the railway rolling stock circulation problem. Given the departure and arrival times as well as the expected numbers of passengers, we have to assign the rolling stock to the timetable services. We consider several objective criteria that are related to operational costs, service quality and reliability of the railway system. Our model is an extension of an existing rolling stock model for routing train units along a number of connected train lines. The extended model can also handle underway combining and splitting of trains. We illustrate our model by computational experiments based on instances of NS Reizigers, the main Dutch operator of passenger trains. � 2005 Elsevier B.V. All rights reserved. Keyword: Railway rolling stock circulation

Maintenance Routing for Train Units: The Transition Model

This paper deals with disruption management in passenger railway transportation. In the disruption management process, many actors belonging to different organizations play a role. In this paper we therefore describe the process itself and the roles of the different actors. Furthermore, we discuss the three main subproblems in railway disruption management: timetable adjustment, and rolling stock and crew re-scheduling. Next to a general description of these problems, we give an overview of the existing literature and we present some details of the specific situations at DSB S-tog and NS. These are the railway operators in the suburban area of Copenhagen, Denmark, and on the main railway lines in The Netherlands, respectively. Finally, we address the integration of the re-scheduling processes of the timetable, and the resources rolling stock and crew.

Maintenance routing for train units: The interchange model

This paper deals with real-time disruption management of rolling stock in passenger railway transportation. We describe a generic framework for dealing with disruptions of railway rolling stock schedules. The framework is presented as an online combinatorial decision problem, where the uncertainty of a disruption is modeled by a sequence of information updates. To decompose the problem and to reduce the computation time, we propose a rolling horizon approach: rolling stock decisions are only considered if they are within a certain time horizon from the time of rescheduling. The schedules are then revised as time progresses and new information becomes available. We extend an existing model for rolling stock scheduling to the specific requirements of the real-time situation, and we apply it in the rolling horizon framework. We perform computational tests on instances constructed from real-life cases of Netherlands Railways (NS), the main operator of passenger trains in the Netherlands. We explore the consequences of different settings of the approach for the trade-off between solution quality and computation time.

Railway Rolling Stock Planning: Robustness Against Large Disruptions

textabstractTrain units need regular preventive maintenance. Given the train units that require maintenance in the forthcoming 1-3 days, the rolling stock schedule must be adjusted so that these urgent units reach the maintenance facility in time. In an earlier paper Maroti and Kroon propose a model that requires a large amount of input data. In this paper we describe a less involved multicommodity flow type model for this maintenance routing problem. We study the complexity of the problem. It turns out that the feasibility problem for a single urgent train unit is polynomially solvable but the optimisation version is NP-hard. Finally, we report our computational experiments on practical instances of NS Reizigers, the main Dutch operator of passenger trains

Rescheduling of Railway Rolling Stock with Dynamic Passenger Flows

Train units need regular preventive maintenance. Given the train units that require maintenance in the forthcoming 1-3 days, the rolling stock schedule must be adjusted so that these urgent units reach the maintenance facility in time. In this paper, we present an integer programming model for solving this problem, give complexity results, suggest solution methods, and report our computational results based on practical instances of NS Reizigers, the main Dutch operator of passenger trains.

Rescheduling in passenger railways: the rolling stock rebalancing problem

In this paper we describe a two-stage optimization model for determining robust rolling stock circulations for passenger trains. Here robustness means that the rolling stock circulations can better deal with large disruptions of the railway system. The two-stage optimization model is formulated as a large mixed-integer linear programming (MILP) model. We first use Benders decomposition to determine optimal solutions for the LP-relaxation of this model. Then we use the cuts that were generated by the Benders decomposition for computing heuristic robust solutions for the two-stage optimization model. We call our method Benders heuristic. We evaluate our approach on the real-life rolling stock-planning problem of Netherlands Railways, the main operator of passenger trains in the Netherlands. The computational results show that, thanks to Benders decomposition, the LP-relaxation of the two-stage optimization problem can be solved in a short time for a representative number of disruption scenarios. In addition, they demonstrate that the robust rolling stock circulation computed heuristically has total costs that are close to the LP lower bounds. Finally, we discuss the practical effectiveness of the robust rolling stock circulation: When a large number of disruption scenarios were applied to these robust circulations and to the nonrobust optimal circulations, the former appeared to be much more easily recoverable than the latter.

A Quasi-Robust Optimization Approach for Crew Rescheduling

In this paper we describe a real-time rolling stock rescheduling model for disruption management of passenger railways. Large-scale disruptions, e.g., due to malfunctioning infrastructure or rolling stock, usually result in the cancellation of train services. As a consequence, the passenger flows change, because passengers will look for alternative routes to get to their destinations. Our model takes these dynamic passenger flows into account. This is in contrast with most traditional rolling stock rescheduling models that consider the passenger flows either as static or as given input. Furthermore, we describe an iterative heuristic for solving the rolling stock rescheduling model with dynamic passenger flows. The model and the heuristic were tested on realistic problem instances of Netherlands Railways, the major operator of passenger trains in the Netherlands. The computational results show that the average delay of the passengers can be reduced significantly by taking into account the dynamic behavior of the passenger flows on the detour routes, and that the computation times of the iterative heuristic are appropriate for an application in real-time disruption management.