Joris Wagenaar

Flexible Connections in Pesp Models for Cyclic Passenger Railway Timetabling

In this paper we describe how rolling stock and passenger connections in a cyclic railway timetable can be modeled in a flexible way within the model for the Periodic Event Scheduling Problem (PESP). The PESP model was introduced by Serani and Ukovich (1989). Usually, PESP-models assume that the constraints for rolling stock or passenger connections specify in detail which trains should connect with each other. However, the flexibility described in this paper allows the model to choose which trains should connect with each other in a rolling stock or passenger connection. We express the required number of train compositions in terms of the integer cycle variables of the constraint graph. We also describe an abstract framework, demonstrating that, under certain conditions, the extra flexibility can be modeled purely in terms of PESP constraints. The concept of flexible rolling stock and passenger connections is illustrated by an example based on three intercity lines of Netherlands Railways.

Maintenance Appointments in Railway Rolling Stock Rescheduling

This paper addresses the Rolling Stock Rescheduling Problem (RSRP), while taking maintenance appointments into account. After a disruption, the rolling stock of the disrupted passenger trains has to be rescheduled in order to restore a feasible rolling stock circulation. Usually, a number of train units have a scheduled maintenance appointment during the day: these appointments must be taken into account while rescheduling the rolling stock. In this paper we propose three Mixed Integer Programming (MIP) models for this purpose. All models are extensions of the Composition model from literature, which does not distinguish individual train units. The Extra Unit Type model adds an additional rolling stock type for each train unit that requires maintenance. The Shadow-Account model keeps track of a shadow account for each train unit that requires maintenance. The Job-Composition model creates a path for each train unit such that the train units that require maintenance are on time for their maintenance appointments. All models are tested on instances of Netherlands Railways (NS). The results show that especially the Shadow-Account model and the Job-Composition model are effectively able to take maintenance appointments into account during real-time rescheduling. It depends on the characteristics of an instance whether the Shadow-Account model or the Job-Composition model performs best.

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.

Maintenance in Railway Rolling Stock Rescheduling for Passenger Railways

This paper addresses the Rolling Stock Rescheduling Problem (RSRP), while taking maintenance appointments into account. After a disruption, the rolling stock of passenger trains has to be rescheduled in order to maintain a feasible rolling stock circulation. A limited number of rolling stock units have a scheduled maintenance appointment during the day: these appointments need to be taken into account while rescheduling. In this paper we propose three different models for this. The Extra Unit Type model extends the known Composition model by adding additional rolling stock types for every rolling stock unit that requires maintenance. The Shadow-Account model keeps track of a shadow account for all units that require maintenance. The Job-Composition model is a combination of the Job model and the Composition model, both known in the literature. Paths are created such that maintenance units are on time for their maintenance appointment. All models are tested on instances of Netherlands Railways. The results show that the models are able to efficiently take maintenance appointments into account.

Optimization methods for the Train Unit Shunting Problem

We consider the Train Unit Shunting Problem, an important planning problem for passenger railway operators. This problem entails assigning train units from shunting yards to scheduled train services in such a way that the resulting operations are without conflicts. The problem arises at every shunting yard in the railway network and involves matching train units to arriving and departing train services as well as assigning the selected matchings to appropriate shunting yard tracks. We present an extensive comparison benchmark of multiple solution approaches for this problem, some of which are novel. In particular, we develop a constraint programming formulation, a column generation approach, and a randomized greedy heuristic. We compare and benchmark these approaches with two existing methods, a mixed integer linear program and a two-stage heuristic. The benchmark contains multiple real-life instances provided by the Danish State Railways (DSB) and Netherlands Railways (NS). The results highlight the strengths and weaknesses of the considered approaches.