Thijs Dewilde

Improving the robustness in railway station areas

In order to improve the robustness of a railway system in station areas, this paper introduces an iterative approach to successively optimize the train routing through station areas and to enhance this solution by applying some changes to the timetable in a tabu search environment. We present our vision on robustness and describe how this vision can be used in practice. By introducing the spread of the trains in the objective function for the route choice and timetabling module, we improve the robustness of a railway system. Using a discrete event simulation model, the performance of our algorithms is evaluated based on a case study for the Brussels’ area. The computational results indicate an average improvement in robustness of 6.2% together with a decrease in delay propagation of about 25%. Furthermore, the effect of some measures like changing the train offer to further increase the robustness is evaluated and compared.

Robust railway station planning: An interaction between routing, timetabling and platforming

Abstract In this paper, we consider complex, busy stations whose limited capacity is one of the main reasons of delay propagation. Our goal is to improve, during the planning phase, the robustness of a complex station by fully exploiting the potential of the available capacity. The main feature of our approach is the interaction between routing decisions, timetabling and platform assignments. By altering one of these, slack can be created to allow improvements by the others as well. An objective function that maximizes the time span between any two trains is defined and the timing of the trains and the way how trains are routed to the platforms are optimized in the scope of this objective. By maximizing the spread of the trains, potential conflicts are avoided which is beneficial for – but not identical to – robustness. Using our approach, the robustness in the station zone of Brussels, Belgium’s main railway bottleneck, can be improved by 8%. Next to that, the amount of knock-on delay arising due to conflicts within this area can be halved. This performance of our approach is confirmed by a second case study based on the station zone of Antwerp.

An iterative approach for reducing the impact of infrastructure maintenance on the performance of railway systems

Planned infrastructure works reduce the available capacity of a railway system and make it more vulnerable to conflicts and delay propagation. The starting point of this paper is a published timetable that needs to be adapted due to the temporary unavailability of some resources. Since the timetable is in operation, changed arrival or departure times and cancelations have an impact on the passengers who need to adapt their travel behavior. In the light of passenger service, a trade-off is made between these inconveniences and the delays that occur in practice due to the reduced capacity. Taking the robustness of the adapted railway timetable into account is a new approach to rescheduling in case of a planned infrastructure unavailability. In this paper, an algorithm that adjusts the train routing and the train schedule to the planned maintenance interventions and keeps the level of passenger service as high as possible is presented. To avoid large inconveniences, the developed algorithm tries to minimize the number of cancelations. Computational results show that by allowing small modifications to the routing and the timetable, the robustness of the resulting solution can improve by more than 10 percent and only few trains need to be canceled.

Automated, Passenger Time Optimal, Robust Timetabling, Using Integer Programming

To design an optimal passenger train timetable one should choose a quality criterium or a combination of criteria. We consider the main quality criterium from a passenger perspective: journey time. This means that the expected time all passengers will spend when our timetable is put in practice is minimal, even taking into account typical train delays. From a train operator or rail infrastructure management company perspective, there are further concerns too, like the number of train units that has to take part in this schedule, their frequency, the number of drivers and other crew members. These factors are all related to cost to maintain the schedule but are here considered secondary and indeed, are here kept constant. We consider only the passenger criterium here. We analytically derive total stochastic expected passenger time as a closed formula, linearize it and use it as a goal function for optimizing the schedule using a mixed integer linear programming model. We applied this to all 224 current Belgian train relations, passing 550 train stations and calculated an optimal schedule in 3 hours. We believe this mathematically optimal approach is unique, in its detailed model of expected, stochastic passenger time, in its scale of implementation and in its use of actual current data from practice.

Heuristics for the traveling repairman problem with profits

In the traveling repairman problem with profits, a repairman (also known as the server) visits a subset of nodes in order to collect time-dependent profits. The objective consists of maximizing the total collected revenue. We restrict our study to the case of a single server with nodes located in the Euclidean plane. We investigate properties of this problem, and we derive a mathematical model assuming that the number of visited nodes is known in advance. We describe a tabu search algorithm with multiple neighborhoods, and we test its performance by running it on instances based on TSPLIB. We conclude that the tabu search algorithm finds good-quality solutions fast, even for large instances.