Dynamic railway junction rescheduling using population based ant colony optimisation

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Efficient rescheduling after a perturbation is an important concern of the railway industry. Extreme delays can result in large fines for the train company as well as dissatisfied customers. The problem is exacerbated by the fact that it is a dynamic one; more timetabled trains may be arriving as the perturbed trains are waiting to be rescheduled. The new trains may have different priorities to the existing trains and thus the rescheduling problem is a dynamic one that changes over time. The aim of this research is to apply a population-based ant colony optimisation algorithm to address this dynamic railway junction rescheduling problem using a simulator modelled on a real-world junction in the UK railway network. The results are promising: the algorithm performs well, particularly when the dynamic changes are of a high magnitude and frequency

Algorithmic Support for Railway Disruption Management

Disruptions of a railway system are responsible for longer travel times and much discomfort for the passengers. Since disruptions are inevitable, the railway system should be prepared to deal with them effectively. This paper explains that, in case of a disruption, rescheduling the timetable, the rolling stock circulation, and the crew duties is so complex that solving them manually is too time consuming in a time critical situation where every minute counts. Therefore, algorithmic support is badly needed. To that end, we describe models and algorithms for real-time rolling stock rescheduling and real-time crew rescheduling that are currently being developed and that are to be used as the kernel of decision support tools for disruption management. Furthermore, this paper argues that a stronger passenger orientation, facilitated by powerful algorithmic support, will allow to mitigate the adverse effects of the disruptions for the passengers. The latter will contribute to an increased service quality provided by the railway system. This will be instrumental in increasing the market share of the public transport system in the mobility market.

Ant Colony Optimisation for Dynamic and Dynamic Multi-objective Railway Rescheduling Problems

Recovering the timetable after a delay is essential to the smooth and efficient operation of the railways for both passengers and railway operators. Most current railway rescheduling research concentrates on static problems where all delays are known about in advance. However, due to the unpredictable nature of the railway system, it is possible that further unforeseen incidents could occur while the trains are running to the new rescheduled timetable. This will change the problem, making it a dynamic problem that changes over time. The aim of this work is to investigate the application of ant colony optimisation (ACO) to dynamic and dynamic multiobjective railway rescheduling problems. ACO is a promising approach for dynamic combinatorial optimisation problems as its inbuilt mechanisms allow it to adapt to the new environment while retaining potentially useful information from the previous environment. In addition, ACO is able to handle multi-objective problems by the addition of multiple colonies and/or multiple pheromone and heuristic matrices. The contributions of this work are the development of a junction simulator to model unique dynamic and multi-objective railway rescheduling problems and an investigation into the application of ACO algorithms to solve those problems. A further contribution is the development of a unique two-colony ACO framework to solve the separate problems of platform reallocation and train resequencing at a UK railway station in dynamic delay scenarios. Results showed that ACO can be e ectively applied to the rescheduling of trains in both dynamic and dynamic multi-objective rescheduling problems. In the dynamic junction rescheduling problem ACO outperformed First Come First Served (FCFS), while in the dynamic multi-objective rescheduling problem ACO outperformed FCFS and Non-dominated Sorting Genetic Algorithm II (NSGA-II), a stateof- the-art multi-objective algorithm. When considering platform reallocation and rescheduling in dynamic environments, ACO outperformed Variable Neighbourhood Search (VNS), Tabu Search (TS) and running with no rescheduling algorithm. These results suggest that ACO shows promise for the rescheduling of trains in both dynamic and dynamic multi-objective environments.Engineering and Physical Sciences Research Council (EPSRC

Susceptibility of optimal train schedules to stochastic disturbances of process times

This work focuses on the stochastic evaluation of train schedules computed by a microscopic scheduler of railway operations based on deterministic information. The research question is to assess the degree of sensitivity of various rescheduling algorithms to variations in process times (running and dwell times). In fact, the objective of railway traffic management is to reduce delay propagation and to increase disturbance robustness of train schedules at a network scale. We present a quantitative study of traffic disturbances and their effects on the schedules computed by simple and advanced rescheduling algorithms. Computational results are based on a complex and densely occupied Dutch railway area; train delays are computed based on accepted statistical distributions, and dwell and running times of trains are subject to additional stochastic variations. From the results obtained on a real case study, an advanced branch and bound algorithm, on average, outperforms a First In First Out scheduling rule both in deterministic and stochastic traffic scenarios. However, the characteristic of the stochastic processes and the way a stochastic instance is handled turn out to have a serious impact on the scheduler performance

A mixed integer linear programming model with heuristic improvements for single-track railway rescheduling problem

A rescheduling algorithm for trains on a single-track railway was developed in case of disturbances that would cause conflicts between trains. This algorithm is based on mixed integer linear programming (MILP) with speed-up routines. The model considers station capacities explicitly (i.e., the number of available tracks for meeting and overtaking operations). Because the model is too hard for the solvers (CPLEX in this study) to tackle, three speed-up routines were devised when rescheduling trains. These routines are a greedy heuristic to reduce the solution space, using the lazy constraint attribute of the solver and a multiobjective approach to find a good initial feasible solution that conforms to actual railway operation. The algorithm was tested on a hypothetical rail line for different sizes of timetable instances with disturbed trains in a maximum two-hour time horizon. It managed to solve the hardest instances within a three-minute time limit thus minimizing the total weighted delay of rescheduled trains. The optimality gap metric is used to show the effectiveness and efficiencies of the speed-up heuristics developed

Optimal Scheduling of Trains on a Single Line Track

This paper describes the development and use of a model designed to optimise train schedules on single line rail corridors. The model has been developed with two major applications in mind, namely: as a decision support tool for train dispatchers to schedule trains in real time in an optimal way; and as a planning tool to evaluate the impact of timetable changes, as well as railroad infrastructure changes. The mathematical programming model described here schedules trains over a single line track. The priority of each train in a conflict depends on an estimate of the remaining crossing and overtaking delay, as well as the current delay. This priority is used in a branch and bound procedure to allow and optimal solution to reasonable size train scheduling problems to be determined efficiently. The use of the model in an application to a 'real life' problem is discussed. The impacts of changing demand by increasing the number of trains, and reducing the number of sidings for a 150 kilometre section of single line track are discussed. It is concluded that the model is able to produce useful results in terms of optimal schedules in a reasonable time for the test applications shown here

Evaluating the Applicability of Advanced Techniques for Practical Real-time Train Scheduling

AbstractThis paper reports on the practical applicability of published techniques for real-time train scheduling. The final goal is the development of an advanced decision support system for supporting dispatchers’ work and for guiding them toward near-optimal real-time re-timing, re-ordering and re-routing decisions. The paper focuses on the optimization system AGLIBRARY that manages trains at the microscopic level of block sections and block signals and at a precision of seconds. The system outcome is a detailed conflict-free train schedule, being able to avoid deadlocks and to minimize train delays. Experiments on a British railway nearby London demonstrate that AGLIBRARY can quickly compute near-optimal solutions

Structure and Simulation Evaluation of an Integrated Real-Time Rescheduling System for Railway Networks

A critical problem faced by railways is how to increase capacity without investing heavily in infrastructure and impacting on schedule reliability. One way of increasing capacity is to reduce the buffer time added to timetables. Buffer time is used to reduce the impact of train delays on overall network reliability. While reducing buffer times can increase capacity, it also means that small delays to a single train can propagate quickly through the system causing knock-on delays to trains impacted by the delayed train. The Swiss Federal Railways (SBB) and Swiss Federal Institute of Technology (ETH) are researching a new approach for real-time train rescheduling that could enable buffer times to be reduced without impacting schedule reliability. This approach is based on the idea that if trains can be efficiently rescheduled to address delays, then less buffer time is needed to maintain the same level of system schedule reliability. The proposed approach combines a rescheduling algorithm with very accurate train operations (using a driver-machine interface). This paper describes the proposed approach, some system characteristics that improve its efficiency, and results of a microscopic simulation completed to help show the effectiveness of this new approach. The results demonstrate that the proposed integrated real-time rescheduling system enables capacity to be increased and may reduce knock-on delays. The results also clearly showed the importance of accurate train operations on the rescheduling system's effectivenes