Solving large scale crew scheduling problems by using iterative partitioning

This paper deals with large-scale crew scheduling problems arisingat the Dutch railway operator, Netherlands Railways (NS). NSoperates about 30,000 trains a week. All these trains need a driverand a certain number of conductors. No available crew schedulingalgorithm can solve such huge instances at once. A common approachto deal with these huge weekly instances, is to split them intoseveral daily instances and solve those separately. However, wefound out that this can be rather inefficient.In this paper, we discuss several methods to partition hugeinstances into several smaller ones. These smaller instances arethen solved with the commercially available crew schedulingalgorithm TURNI. We compare these partitioning methods with eachother, and we report several results where we applied differentpartitioning methods after each other. The results show that allmethods significantly improve the solution. With the best approach,we were able to cut down crew costs with about 2\\% (about 6 millioneuro per year).crew scheduling;large-scale optimization;partitioning methods

Solving large scale crew scheduling problems by using iterative partitioning

This paper deals with large-scale crew scheduling problems arising at the Dutch railway operator, Netherlands Railways (NS). NS opera

Decision support for crew rostering at NS

This paper describes a method for solving the cyclic crewrostering problem (CCRP). This is the problem of cyclicallyordering a set of duties for a number of crew members, such thatseveral complex constraints are satisfied and such that thequality of the obtained roster is as high as possible. Thedescribed method was tested on a number of instances of NS, thelargest operator of passenger trains in the Netherlands. Theseinstances involve the generation of rosters for groups of traindrivers or conductors of NS. The tests show that high qualitysolutions for practical instances of the CCRP can be generated inan acceptable amount of computing time. Finally, we describe anexperiment where we constructed rosters in an automatic way for agroup of conductors. They preferred our - generated - rosters overtheir own manually constructed rosters.

Crew Management in Passenger Rail Transport

__Abstract__ Crew management in passenger rail transport is an important factor that contributes to both the quality of service to the railway passengers and to the operational costs of the train operating company. This thesis describes how the (railway) Crew Management process can be improved with the introduction of advanced decision support systems, based on advanced mathematical models and algorithms. We provide a managerial perspective on the change process, related to the introduction of these systems, and give an overview of the lessons learned. We have shown that introducing decision support can give substantial improvements in the overall performance of a railway company. Within NS, the support for the Crew Management process has led to a stable relationship between management and train crew. In addition, the lead-time of the planning process is shortened from months to hours and NS is now able to perform scenario analyses, e.g., to study effects of adjusting the labour rules. Also, NS can adjust their service when severe weather conditions are expected, by creating a specific winter timetable shortly before the day of operation. Finally, we also introduced a decision support system for real-time rescheduling of crew duties on the day of operations. This enables us to adapt the actual crew schedules very quickly. As a result, we reduce the number of cancelled trains and fewer trains will be delayed in case of unforeseen disruptions

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

Decision support for crew rostering at NS

This paper describes a method for solving the cyclic crew rostering problem (CCRP). This is the problem of cyclically ordering a set of duties for a number of crew members, such that several complex constraints are satisfied and such that the quality of the obtained roster is as high as possible. The described method was tested on a number of instances of NS, the largest operator of passenger trains in the Netherlands. These instances involve the generation of rosters for groups of train drivers or conductors of NS. The tests show that high quality solutions for practical instances of the CCRP can be generated in an acceptable amount of computing time. Finally, we describe an experiment where we constructed rosters in an automatic way for a group of conductors. They preferred our - generated - rosters over their own manually constructed rosters

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.

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

Reinventing Crew Scheduling at Netherlands Railways

In this paper we describe the successful application of a sophisticated Operations Research model and the corresponding solution techniques for scheduling the 6,500+ drivers and conductors of the Dutch railway operator NS Reizigers (Netherlands Railways). In 2001 the drivers and conductors were very dissatisfied with the structure of their duties, which led to nation wide strikes. However, the application of the model described in this paper led to the development of an alternative production model (‘Sharing Sweet & Sour’) that both satisfied the drivers and conductors, and at the same time supported an increment of the punctuality and efficiency of the railway services. The plans produced according to the alternative production model trimmed personnel costs by about $4.8million (or1.2$7 million per year are also achievable

Solving Large Scale Crew Scheduling Problems in Practice

This paper deals with large-scale crew scheduling problems arising at the Dutch railway operator, Netherlands Railways (NS). NS operates about 30,000 trains a week. All these trains need a driver and a certain number of guards. Some labor rules restrict the duties of a certain crew base over the complete week. Therefore splitting the problem in several subproblems per day leads to suboptimal solutions. In this paper, we present an algorithm, called LUCIA, which can solve such huge instances without splitting. This algorithm combines Lagrangian heuristics, column generation and fixing techniques. We compare the results with existing practice. The results show that the new method significantly improves the solution