Design of a robust railway line system for severe winter conditions in The Netherlands

Winter weather has a major impact on railway operations in The Netherlands. To stay in control, the number of trains is reduced by half in a special “winter timetable”. This results in a more robust network, but an insufficient amount of transport capacity. Adapting the line system can result in more transport capacity without losing robustness. This paper therefore focuses on the performance of a line system under extreme weather conditions. We define several criteria to assess the performance of the line system in terms of robustness and transport capacity. A case study has been conducted on the railway network in The Netherlands, which indicates that all alternatives are more robust and yield more transport capacity than the current winter timetable

A Next Step in Disruption Management: Combining Operations Research and Complexity Science

Railway systems occasionally get into a state of out-of-control, meaning that there is barely any train is running, even though the required resources (infrastructure, rolling stock and crew) are available. These situations can either be caused by large disruptions or unexpected propagation and accumulation of delays. Because of the large number of aected resources and the absence of detailed, timely and accurate information, currently existing methods cannot be applied in out-of-control situations. Most of the contemporary approaches assume that there is only one single disruption with a known duration, that all information about the resources is available, and that all stakeholders in the operations act as expected. Another limitation is the lack of knowledge about why and how disruptions accumulate and whether this process can be predicted. To tackle these problems, we develop a multidisciplinary framework aiming at reducing the impact of these situations and - if possible - avoiding them. The key elements of this framework are (i) the generation of early warning signals for out-of-control situations using tools from complexity science and (ii) a set of rescheduling measures robust against the features of out-of-control situations, using tools from operations research