Disruption Management of Rolling Stock in Passenger Railway Transportation

This paper deals with real-time disruption management of rolling stock in passenger railway transportation. We present a generic framework for modeling disruptions in railway rolling stock schedules. The framework is presented as an online combinatorial decision problem where the uncertainty of a disruption is modeled by a sequence of information updates. To decompose the problem we propose a rolling horizon approach where only rolling stock decisions within a certain time horizon from the time of rescheduling are taken into account. The schedules are then revised as the situation progresses and more accurate information becomes available. We extend an existing model for rolling stock scheduling to the specific requirements of the real-time case and apply it in the rolling horizon framework. We perform computational tests on instances constructed from real life cases and explore the consequences of different settings of the approach for the trade-off between solution quality and computation time

Railway timetabling from an operations research

In this paper we describe Operations Research (OR) models and techniques that can be used for determining (cyclic) railway timetables. We discuss the two aspects of railway timetabling: ($i$) the determination of arrival and departure times of the trains at the stations and other relevant locations such as junctions and bridges, and ($ii$) the assignment of each train to an appropriate platform and corresponding inbound and outbound routes in every station. Moreover, we discuss robustness aspects of both subproblems

An adjustable robust optimization approach for periodic timetabling

In this paper, we consider the Robust Periodic Timetabling Problem (RPTP), the problem of designing a periodic timetable that can easily be adjusted in case of small periodic disturbances. We develop a solution method for a parametrized class of uncertainty regions. This class relates closely to uncertainty regions known in the robust optimization literature, and naturally defines a metric for the robustness of the timetable. The proposed solution method combines a linear decision rule with well-known reformulation techniques and cutting-plane methods. We show that the RPTP can be solved for practical-sized instances by applying the solution method to practical cases of Netherlands Railways (NS). In particular, we show that the trade-off between the efficiency and robustness of a timetable can be analyzed using our solution method

Rescheduling of Railway Rolling Stock with Dynamic Passenger Flows

Traditional rolling stock rescheduling applications either treat passengers as static objects whose influence on the system is unchanged in a disrupted situation, or they treat passenger behavior as a given input. In case of disruptions however, we may expect the flow of passengers to change significantly. In this paper we present a model for passenger flows during disruptions and we describe an iterative heuristic for optimizing the rolling stock to the disrupted passenger flows. The model is tested on realistic problem instances of NS, the major operator of passeng

A rolling stock circulation model for combining and splitting of passenger trains.

This paper addresses the railway rolling stock circulation problem. Given the departure and arrival times as well as the expected numbers of passengers, we have to assign the rolling stock to the timetable services. We consider several objective criteria that are related to operational costs, service quality and reliability of the railway system. Our model is an extension of an existing rolling stock model for routing train units along a number of connected train lines. The extended model can also handle underway combining and splitting of trains. We illustrate our model by computational experiments based on instances of NS Reizigers, the main Dutch operator of passenger train

Re-scheduling in railways: the rolling stock balancing problem

This paper addresses the Rolling Stock Balancing Problem (RSBP). This problem arises at a passenger railway operator when the rolling stock has to be re-scheduled due to changing circumstances. These problems arise both in the planning process and during operations. The RSBP has as input a timetable and a rolling stock schedule where the allocation of the rolling stock among the stations does not fit to the allocation before and after the planning period. The problem is then to correct these off-balances, leading to a modified schedule that can be implemented in practice. For practical usage of solution approaches for the RSBP, it is important to solve the problem quickly. Therefore, the focus is on heuristic approaches. In this paper, we describe two heuristics and compare them with each other on some (variants of) real-life instances of NS, the main Dutch passenger railway operator. Finally, to get some insight in the quality of the proposed heuristics, we also compare their outcomes with optimal solutions obtained by solving existing rolling stock circulation models

Stochastic Improvement of Cyclic Railway Timetables

Real-time railway operations are subject to stochastic disturbances. However, a railway timetable is a deterministic plan. Thus a timetable should be designed in such a way that it can cope with the stochastic disturbances as well as possible. For that purpose, a timetable usually contains time supplements in several process times and buffer times between pairs of consecutive trains. This paper describes a Stochastic Optimization Model that can be used to allocate the time supplements and the buffer times in a given timetable in such a way that the timetable becomes maximally robust against stochastic disturbances. The Stochastic Optimization Model was tested on several instances of NS Reizigers, the main operator of passenger trains in the Netherlands. Moreover, a timetable that was computed by the model was operated in practice in a timetable experiment on the so-called “Zaanlijn”. The results show that the average delays of trains can often be reduced significantly by applying relatively small modifications to a given timetable

Shuttle Planning for Link Closures in Urban Public Transport Networks

Urban Public Transport systems must periodically close certain links for main- tenance, which can have significant effects on the service provided to passengers. In practice, the effects of closures are mitigated by replacing the link with a simple shuttle service. However, alternative shuttle services could reduce inconvenience at lower op- erating cost. This paper proposes a model to select shuttle lines and frequencies under budget constraints. A new formulation is proposed that allows a minimal frequency restriction on any line that is operated, and minimizes passenger inconvenience cost, including transfers and frequency-dependent waiting time. This model is applied to a shuttle design problem based on a real world case study of the MBTA network of Boston (USA). The results show that additional shuttle routes can reduce passenger delay in comparison to the standard industry practice, while also distributing delay more equally over passengers, at the same operating budget. The results are robust under different assumptions about passenger route choice behavior. Computational experiments show that the proposed formulation, coupled with a preprocessing step, can be solved faster than prior formulations

A Quasi-Robust Optimization Approach for Resource Rescheduling

If a disruption takes place in a complex task-based system, where tasks are carried out by a number of resource units or servers, real-time disruption management usually has to deal with an uncertain duration of the disruption. In this paper we present a novel approach for rescheduling such systems, thereby taking into account the uncertain duration of the disruption. We assume that several possibilities for the duration of the disruption are given. We solve the rescheduling problem as a two-stage optimization problem. In the first stage, at the start of the disruption, we reschedule the plan based on the optimistic scenario for the duration of the disruption, while taking into account the possibility that another scenario will be realized. In fact, we require a prescribed number of the rescheduled resource duties to be recoverable. This means that they can be easily recovered if it turns out that another scenario than the optimistic one is realized. We demonstrate the effectiveness of our approach by an application in real-time railway crew rescheduling. This is an important subproblem in the disruption management process of a railway company with a lot of uncertainty about the duration of a disruption. We test our approach on a number of instances of Netherlands Railways (NS), the main operator of passenger trains in the Netherlands. The numerical experiments show that the approach indeed finds schedules which are easier to adjust if it turns out that another scenario than the optimistic one is realized