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

Practice Oriented Algorithmic Disruption Management in Passenger Railways

How to deal with a disruption is a question railway companies face on a daily basis. This thesis focusses on the subject how to handle a disruption such that the passenger service is upheld as much as possible. The current mathematical models for disruption management can not yet be applied in practice, because several important practical considerations are not taken into account. In this thesis several models are presented which take important practical details into account: (1) Creating a macroscopic global feasible solution for all three resource schedules, instead of focussing on one individual resource schedule. (2) Scheduled maintenance appointments required

Risk-based inspection planning of rail infrastructure considering operational resilience

This research proposes a response model for a disrupted railway track inspection plan. The proposed model takes the form of an active acceptance risk strategy while having been developed under the disruption risk management framework. The response model entails two components working in a series; an integrated Nonlinear Autoregressive model with eXogenous input Neural Network (iNARXNN), alongside a risk-based value measure for predicting track measurements data and an output valuation. The neural network fuses itself to Bayesian inference, risk aversion and a data-driven modelling approach, as a means of ensuring the utmost standard of prediction ability. Testing on a real dataset indicates that the iNARXNN model provides a mean prediction accuracy rate of 95%, while also successfully preserving data characteristics across both time and frequency domains. This research also proposes a network-based model that highlights the value of accepting iNARXNN’s outputs. The value is formulated as the ratio of rescheduling cost to a change in the risk level from a missed opportunity to repair a defective track, i.e., late defect detection. The value model demonstrates how the resilience action is useful for determining a rescheduling strategy that has (negative) value when dealing with a disrupted track inspection pla

Crew Management in Passenger Rail Transport

__Abstract__ Crew management in passenger rail transport is an important factor that contributes to both the quality of service to the railway passengers and to the operational costs of the train operating company. This thesis describes how the (railway) Crew Management process can be improved with the introduction of advanced decision support systems, based on advanced mathematical models and algorithms. We provide a managerial perspective on the change process, related to the introduction of these systems, and give an overview of the lessons learned. We have shown that introducing decision support can give substantial improvements in the overall performance of a railway company. Within NS, the support for the Crew Management process has led to a stable relationship between management and train crew. In addition, the lead-time of the planning process is shortened from months to hours and NS is now able to perform scenario analyses, e.g., to study effects of adjusting the labour rules. Also, NS can adjust their service when severe weather conditions are expected, by creating a specific winter timetable shortly before the day of operation. Finally, we also introduced a decision support system for real-time rescheduling of crew duties on the day of operations. This enables us to adapt the actual crew schedules very quickly. As a result, we reduce the number of cancelled trains and fewer trains will be delayed in case of unforeseen disruptions

A Column Generation Approach for the Integrated Crew Re-Planning Problem

In this paper, we propose a column generation approach for crew re-planning, i.e., the construction of new duties and rosters for the employees, given changes in the timetable and rolling stock schedule. In the current practice, the feasibility of the new rosters is `assured' by allowing the new duties to deviate only slightly from the original ones. In the Integrated Crew Re-Planning Problem (ICRPP), we loosen this requirement and allow more exibility: The ICRPP considers the re-scheduling of crew for multiple days simultaneously, thereby explicitly taking the feasibility of the rosters into account, and hence allowing arbitrary deviations from the original duties. We propose a mathematical formulation for the ICRPP and develop a column generation approach to solve the problem. We apply our solution approach to practical instances from NS, and show the benefit of integrating the re-scheduling process

Rolling Stock Rescheduling in Passenger Railways: Applications in short-term planning and in disruption management

Modern society is highly dependent on a reliable railway system for workforce mobility and easy access to the cities. However, the daily operations of a large passenger railway system are subject to unexpected disruptions such as rolling stock breakdowns or malfunctioning infrastructure. In a disrupted situation, the railway operator must adapt the timetable, rolling stock and crew to the modified conditions. This adaptation of resource allocations requires the solution of complex combinatorial problems in very short time and thus represents a major challenge for the involved dispatchers. In this thesis we develop models and solution methods for the rescheduling of the rolling stock during disruptions. The models incorporate service aspects (such as seat capacity), efficiency aspects (such as number of kilometers driven by the rolling stock), and process related aspects (such as the need for night-time relocation of rolling stock). The thesis contains applications of the developed models in three different contexts. First, we present a framework for applying the rescheduling models in the highly uncertain environment of railway disruption management, and we demonstrate the trade-off between computation time and solution quality. Second, we embed the rolling stock rescheduling models in a simulation framework to account for the dynamic passenger behavior during disruptions. This framework allows us to significantly decrease the delays experienced by passengers. Third, we apply the rescheduling models to real-life planning problems from the short-term planning department of the Netherlands Railways. The models lead to a considerable speed-up of the process and significant savings

Train planning in a fragmented railway: a British perspective

Train Planning (also known as railway scheduling) is an area of substantial importance to the success of any railway. Through train planning, railway managers aim to meet the needs of customers whilst using as low a level of resources (infrastructure, rolling stock and staff) as possible. Efficient and effective train planning is essential to get the best possible performance out of a railway network. The author of this thesis aims, firstly, to analyse the processes which are used to develop train plans and the extent to which they meet the objectives that they might be expected to meet and, secondly, to investigate selected new and innovative software approaches that might make a material difference to the effectiveness and/or efficiency of train planning processes. These aims are delivered using a range of primarily qualitative research methods, including literature reviews, interviews, participant observation and case studies, to understand these processes and software. Conclusions regarding train planning processes include how the complexity of these processes hinders their effectiveness, the negative impact of the privatisation of British Rail on these processes and the conflicting nature of objectives for train planning in the privatised railway. Train planning software is found not to adequately support train planners in meeting the objectives they are set. The potential for timetable generation using heuristics and for timetable performance simulation to improve the effectiveness of train planning are discussed and recommendations made for further research and development to address the limitations of the software currently available

Crew Planning at Netherlands Railways: Improving Fairness, Attractiveness, and Efficiency

The development and improvement of decision support voor crew planning at Netherlands Railways (NS