http://repub.eur.nl/res/aut/8446/ List of Publications en http://repub.eur.nl/static-eur/img/logo.png http://repub.eur.nl http://repub.eur.nl/res/pub/32050/ 2011-06-01T00:00:00Z This paper deals with large-scale crew scheduling problems arising at the main Dutch railway operator, Netherlands Railways (NS). NS operates about 30000 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. http://repub.eur.nl/res/pub/21711/ 2010-12-07T00:00:00Z 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. http://repub.eur.nl/res/pub/25657/ 2010-08-01T00:00:00Z Passenger railway operations are based on an extensive planning process for generating the timetable, the rolling stock circulation, and the crew duties for train drivers and conductors. In particular, crew scheduling is a complex process. After the planning process has been completed, the plans are carried out in the real-time operations. Preferably, the plans are carried out as scheduled. However, in case of delays of trains or large disruptions of the railway system, the timetable, the rolling stock circulation and the crew duties may not be feasible anymore and must be rescheduled. This paper presents a method based on multi-agent techniques to solve the train driver rescheduling problem in case of a large disruption. It assumes that the timetable and the rolling stock have been rescheduled already based on an incident scenario. In the crew rescheduling model, each train driver is represented by a driver-agent. A driver-agent whose duty has become infeasible by the disruption starts a recursive task exchange process with the other driver-agents in order to solve this infeasibility. The task exchange process is supported by a route-analyzer-agent, which determines whether a proposed task exchange is feasible, conditionally feasible, or not feasible. The task exchange process is guided by several cost parameters, and the aim is to find a feasible set of duties at minimal total cost. The train driver rescheduling method was tested on several realistic disruption instances of Netherlands Railways (NS), the main operator of passenger trains in the Netherlands. In general the rescheduling method finds an appropriate set of rescheduled duties in a short amount of time. This research was carried out in close cooperation by NS and the D-CIS Lab. http://repub.eur.nl/res/pub/18654/ 2009-06-01T00:00:00Z 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. http://repub.eur.nl/res/pub/18643/ 2009-02-01T00:00:00Z 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, thus resulting in fewer operational train delays. Modifications to the existing timetable, which was constructed in 1970, were not an option; additional growth would 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. Furthermore, because rolling-stock and crew costs are principal components of the costs 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 ($60 million); the additional revenues generated approximately 10 million of this profit. We expect this profit to increase to 70 million ($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 to more people. Therefore, we expect that many will switch from car transport to rail transport, thus reducing the emission of greenhouse gases. http://repub.eur.nl/res/pub/13767/ 2008-11-10T00:00:00Z 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. http://repub.eur.nl/res/pub/11701/ 2008-03-18T00:00:00Z 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 conductors. No available crew scheduling algorithm can solve such huge instances at once. A common approach to deal with these huge weekly instances, is to split them into several daily instances and solve those separately. However, we found out that this can be rather inefficient. In this paper, we discuss several methods to partition huge instances into several smaller ones. These smaller instances are then solved with the commercially available crew scheduling algorithm TURNI. We compare these partitioning methods with each other, and we report several results where we applied different partitioning methods after each other. The results show that all methods significantly improve the solution. With the best approach, we were able to cut down crew costs with about 2\\% (about 6 million euro per year). http://repub.eur.nl/res/pub/7248/ 2006-01-24T00:00:00Z 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. http://repub.eur.nl/res/pub/14174/ 2005-10-01T00:00:00Z In 2001, the 6,500+ drivers and conductors of the Dutch railway operator NS Reizigers were very dissatisfied with the structure of their duties, which led to nationwide strikes. However, the successful application of an operations research model supported the development of an alternative set of scheduling rules. This alternative set of rules, called Sharing-Sweet-and-Sour, satisfied the drivers' and conductors' requests for more variety in their duties and improved the railway operator's punctuality and efficiency. The application of the operations research model cut personnel costs by about $4.8 million (1.2 percent) per year. Moreover, we showed that the railway operator could even reduce personnel costs by over $7 million per year. http://repub.eur.nl/res/pub/1427/ 2004-07-28T00:00:00Z 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%) per year. Moreover, it was shown that cost reductions of over $7 million per year are also achievable. http://repub.eur.nl/res/pub/14175/ 2004-02-01T00:00:00Z For a commercially operating railway company, providing a high level of service for the passengers is of utmost importance. The latter enquires high punctuality of the trains and an adequate rolling stock capacity. Unfortunately, the latter is currently (in 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 because of 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, because 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 the lines. 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, because 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. http://repub.eur.nl/res/pub/201/ 2002-05-17T00:00:00Z 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.