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

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 train

Allocation of Railway Rolling Stock for Passenger Trains

For a commercially operating railway company, providing a high level of service for the passengers is of utmost importance. The latter requires a high punctuality of the trains and an adequate rolling stock capacity. Unfortunately, the latter is currently (2002) one of the bottlenecks in the service provision by the main Dutch railway operator NS Reizigers. Especially during the morning rush hours, many passengers cannot be transported according to the usual service standards due to a shortage of the rolling stock capacity. On the other hand, a more effective allocation of the available rolling stock capacity seems to be feasible, since there are also several trains with some slack capacity. The effectiveness of the rolling stock capacity is determined mainly by the allocation of the train types and subtypes to the lines. Therefore, we describe in this paper a model that can be used to find an optimal allocation of train types and subtypes to train series. This optimal allocation is more effective than the manually planned one, which is accomplished by minimizing the shortages of capacity during the rush hours. The model is implemented in the modeling language OPL Studio 3.1, solved by CPLEX 7.0, and tested on several scenarios based on the 2001-2002 timetable of NS Reizigers. The results of the model were received positively, both by the planners and by the management in practice, since these results showed that a significant service improvement over the manually planned allocation can be achieved within a shorter throughput time of the involved part of the planning process.operations research;transportation;railways;capacity allocation;rolling stock

Operations research in passenger railway transportation

In this paper, we give an overview of state-of-the-art OperationsResearch models and techniques used in passenger railwaytransportation. For each planning phase (strategic, tactical andoperational), we describe the planning problems arising there anddiscuss some models and algorithms to solve them. We do not onlyconsider classical, well-known topics such as timetabling, rollingstock scheduling and crew scheduling, but we also discuss somerecently developed topics as shunting and reliability oftimetables.Finally, we focus on several practical aspects for each of theseproblems at the largest Dutch railway operator, NS Reizigers.passenger railway transportation;operation research;planning problems

Algorithmic Support for Railway Disruption Management

Disruptions of a railway system are responsible for longer travel times and much discomfort for the passengers. Since disruptions are inevitable, the railway system should be prepared to deal with them effectively. This paper explains that, in case of a disruption, rescheduling the timetable, the rolling stock circulation, and the crew duties is so complex that solving them manually is too time consuming in a time critical situation where every minute counts. Therefore, algorithmic support is badly needed. To that end, we describe models and algorithms for real-time rolling stock rescheduling and real-time crew rescheduling that are currently being developed and that are to be used as the kernel of decision support tools for disruption management. Furthermore, this paper argues that a stronger passenger orientation, facilitated by powerful algorithmic support, will allow to mitigate the adverse effects of the disruptions for the passengers. The latter will contribute to an increased service quality provided by the railway system. This will be instrumental in increasing the market share of the public transport system in the mobility market.

Re-scheduling in railways: the rolling stock balancing problem

This paper addresses the Rolling Stock Balancing Problem (RSBP). This problem arises at a passenger railway operator when the rolling stock has to be re-scheduled due to changing circumstances. These problems arise both in the planning process and during operations. The RSBP has as input a timetable and a rolling stock schedule where the allocation of the rolling stock among the stations does not fit to the allocation before and after the planning period. The problem is then to correct these off-balances, leading to a modified schedule that can be implemented in practice.For practical usage of solution approaches for the RSBP, it is important to solve the problem quickly. Therefore, the focus is on heuristic approaches. In this paper, we describe two heuristics and compare them with each other on some (variants of) real-life instances of NS, the main Dutch passenger railway operator. Finally, to get some insight in the quality of the proposed heuristics, we also compare their outcomes with optimal solutions obtained by solving existing rolling stock circulation models.heuristics;railway planning;integer linear programming;rolling stock re-scheduling

Allocation of Railway Rolling Stock for Passenger Trains

For a commercially operating railway company, providing a high level of service for the passengers is of utmost importance. The latter requires a high punctuality of the trains and an adequate rolling stock capacity. Unfortunately, the latter is currently (2002) one of the bottlenecks in the service provision by the main Dutch railway operator NS Reizigers. Especially during the morning rush hours, many passengers cannot be transported according to the usual service standards due to a shortage of the ro

Robust rolling stock under uncertain demand in rapid transit networks

This paper focuses on the railway rolling stock circulation problem in rapid transit networks where the known demand and train schedule must be met by a given fleet. In rapid transit networks the frequencies are high and distances are relatively short. Although the distances are not very large, service times are high due to the large number of intermediate stops required to allow proper passenger flow. The previous circumstances and the reduced capacity of the depot stations and that the rolling stock is shared between the different lines, force the introduction of empty trains and a careful control on shunting operation. In practice the future demand is generally unknown and the decisions must be based on uncertain forecast. We have developed a stochastic rolling stock formulation of the problem. The computational experiments were developed using a commercial line of the Madrid suburban rail network operated by RENFE (The main Spanish operator of suburban trains of passengers). Comparing the results obtained by deterministic scenarios and stochastic approach some useful conclusions may be obtained

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