Twan Dollevoet

Delay Management Including Capacities of Stations

The question of delay management is whether passenger trains should wait for delayed feeder trains or should depart on time. Solutions to this problem strongly depend on the available capacity of the railway infrastructure. Although the limited capacity of the tracks has been considered in delay management models, the limited capacity of the stations has been neglected so far. In this paper, we develop a model for the delay management problem that includes the capacities of the stations. This model allows rescheduling the platform track assignment. Furthermore, we propose an iterative heuristic in which we first solve the delay management model with a fixed platform track assignment, and then improve this platform track assignment in each step. We show that the latter problem can be solved in polynomial time by describing it as a minimum cost flow model. Finally, we present an extension of the model that balances the delay of the passengers on one hand and the number of changes in the platform track assignment on the other. All models are evaluated on real-world instances from Netherlands Railways.

Robust UAV mission planning

Unmanned Aerial Vehicles (UAVs) can provide significant contributions to information gathering in military missions. UAVs can be used to capture both full motion video and still imagery of specific target locations within the area of interest. In order to improve the effectiveness of a reconnaissance mission, it is important to visit the largest number of interesting target locations possible, taking into consideration operational constraints related to fuel usage, weather conditions and endurance of the UAV. We model this planning problem as the well-known orienteering problem, which is a generalization of the traveling salesman problem. Given the uncertainty in the military operational environment, robust planning solutions are required. Therefore, our model takes into account uncertainty in the fuel usage between targets, for instance due to weather conditions. We report results for using different uncertainty sets that specify the degree of uncertainty against which any feasible solution will be protected. We also compare the probability that a solution is feasible for the robust solutions on one hand and the solution found with average fuel usage on the other. These probabilities are assessed both by simulation and by derivation of problem specific theoretical bounds on the probability of constraint feasibility. In doing so, we show how the sustainability of a UAV mission can be significantly improved. Additionally, we suggest how the robust solution can be operationalized in a realistic setting, by complementing the robust tour with agility principles.

Solving Large Scale Crew Scheduling Problems in Practice

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.

Fast heuristics for delay management with passenger rerouting

Delay management models determine which connections should be maintained in case of a delayed feeder train. Recently, delay management models are developed that take into account that passengers will adjust their routes when they miss a connection. However, for large-scale real-world instances, these extended models become too large to be solved with standard integer programming techniques. We therefore develop several heuristics to tackle these larger instances. The dispatching rules that are used in practice are our first heuristic. Our second heuristic applies the classical delay management model without passenger rerouting. Finally, the third heuristic updates the parameters of the classical model iteratively. We compare the quality of these heuristic solution methods on real-life instances from Netherlands Railways. In this experimental study, we show that our iterative heuristic can solve large-scale real-world instances within a short computation time. Furthermore, the solutions obtained by this iterative heuristic are of good quality.

Analysis of FPTASes for the multi-objective shortest path problem

textabstractWe propose a new FPTAS for the multi-objective shortest path problem. The algorithm uses elements from both an exact labeling algorithm and an FPTAS proposed by Tsaggouris and Zaroliagis (2009). We analyze the running times of these three algorithms both from a the- oretical and a computational point of view. Theoretically, we show that there are instances for which the new FPTAS runs an arbitrary times faster than the other two algorithms. Fur- thermore, for the bi-objective case, the number of approximate solutions generated by the proposed FPTAS is at most the number of Pareto-optimal solutions multiplied by the number of nodes. By performing a set of computational tests, we show that the new FPTAS performs best in terms of running time in case there are many dominated paths and the number of Pareto-optimal solutions is not too small.

Application of an iterative framework for real-time railway rescheduling

Since disruptions in railway networks are inevitable, railway operators and infrastructure managers need reliable measures and tools for disruption management. Current literature on railway disruption management focuses most of the time on rescheduling one resource (timetable, rolling stock or crew) at the time. In this research, we describe an iterative framework in which all three resources are considered. The framework applies existing models and algorithms for rescheduling the individual resources. We extensively test our framework on instances from Netherlands Railways and show that schedules which are feasible for all three resources can be obtained within short computation times. This shows that the framework and the existing rescheduling approaches can be of great value in practice.

Delay Propagation and Delay Management in Transportation Networks

Should connecting trains wait for delayed feeder trains? Or is it better for the passengers if trains depart on time?

Integrating Timetabling and Crew Scheduling at a Freight Railway Operator

We investigate to what degree we can integrate a train timetabling/engine scheduling problem with a crew scheduling problem. In the timetabling/engine scheduling problem, we determine for each demand a specific time within its time window when the demand should be serviced. Furthermore, we generate engine duties for the demands. In our solution approach for the overall problem, we first obtain an optimal solution for the timetabling/engine scheduling problem. When solving the crew scheduling problem, we then exploit the fact that numerous optimal and near optimal solutions exist for the previous problem. We consider all these solutions that can be obtained from the optimal engine schedule by shifting the demands in time, while keeping the order of demands in the engine duties intact. In particular, in the crew scheduling stage it is allowed to retime the service of demands if the additional cost is outweighed by the crew savings. This information is implemented in a mathematical model for the crew scheduling problem. The model is solved using a column generation scheme. We perform computational experiments based on a case at a freight railway operator, DB Schenker Rail Scandinavia, and show that significant cost savings can be achieved.