Grégory Marliere

RECIFE-MILP: An Effective MILP-Based Heuristic for the Real-Time Railway Traffic Management Problem

The real-time railway traffic management problem consists of selecting appropriate train routes and schedules for minimizing the propagation of delay in case of traffic perturbation. In this paper, we tackle this problem by introducing RECIFE-MILP, a heuristic algorithm based on a mixed-integer linear programming model. RECIFE-MILP uses a model that extends one we previously proposed by including additional elements characterizing railway reality. In addition, it implements performance boosting methods selected among several ones through an algorithm configuration tool. We present a thorough experimental analysis that shows that the performances of RECIFE-MILP are better than the ones of the currently implemented traffic management strategy. RECIFE-MILP often finds the optimal solution to instances within the short computation time available in real-time applications. Moreover, RECIFE-MILP is robust to its configuration if an appropriate selection of the combination of boosting methods is performed.

Real time railway traffic management modeling track-circuits

The real time railway traffic management seeks for the train routing and scheduling that minimize delays after an unexpected event perturbs the operations. In this paper, we propose a mixed integer linear programming formulation for tackling this problem, modeling the infrastructure in terms of track-circuits, which are the basic components for train detection. This formulation considers all possible alternatives for train rerouting in the infrastructure and all rescheduling alternatives for trains along these routes. To the best of our knowledge, we present the first formulation that solves this problem to optimality. We tested the proposed formulation on real perturbation instances representing traffic in a control area including the Lille Flandres station (France), achieving very good performance in terms of computation time.

RECIFE-SAT: A MILP-based algorithm for the railway saturation problem

Abstract Measuring capacity of railway infrastructures is a problem even in its definition. In this paper, we propose RECIFE-SAT, a MILP-based algorithm to quantify capacity by solving the saturation problem. This problem consists of saturating an infrastructure by adding as many trains as possible to an existing (possibly empty) timetable. Specifically, RECIFE-SAT considers a large set of potentially interesting saturation trains and integrates them in the timetable whenever possible. This integration is feasible only when it does not imply the emergence of any conflict with other trains. Thanks to a novel approach to microscopically represent the infrastructure, RECIFE-SAT guarantees the absence of conflicts based on the actual interlocking system deployed in reality. Hence, it can really quantify the actual capacity of the infrastructure considered. The presented version of RECIFE-SAT has two objective functions, namely it maximizes the number of saturation trains scheduled and the number of freight ones. In an experimental analysis performed in collaboration with the French infrastructure manager, we show the promising performance of RECIFE-SAT. To the best of our knowledge, RECIFE-SAT is the first algorithm which is shown to be capable of saturating rather large railway networks considering a microscopic infrastructure representation.

A MILP Reformulation for Train Routing and Scheduling in Case of Perturbation

In this paper we propose a reformulation of RECIFE-MILP aimed at boosting the algorithm performance. RECIFE-MILP is a mixed integer linear programming based heuristic for the real-time railway traffic management problem, that is the problem of re-routing and rescheduling trains in case of perturbation in order to minimize the delay propagation. The reformulation which we propose exploits the topology of the railway infrastructure. Specifically, it capitalizes on the implicit relations between routing and scheduling decisions to reduce the number of binary variables of the formulation. In an experimental analysis based on realistic instances representing traffic in the French Pierrefitte-Gonesse junction, we show the performance improvement achievable through the reformulation.

Real-time railway traffic management optimization and imperfect information: preliminary studies

Railway traffic is often perturbed by unexpected events and appropriate train routing and scheduling shall be applied to minimize delay propagation. A number algorithms for this routing and scheduling problem have been proposed in the literature and they have been tested in different traffic situations. Nonetheless, their performance are almost always studied considering perfect knowledge of future traffic conditions, which is almost impossible to achieve in reality. In this paper, we propose an experimental analysis assessing the usefulness of these algorithms in case of imperfect information. We consider RECIFE-MILP as a traffic management algorithm and advanced or delayed train entrance times in the control area as the source of imperfect information. The results show that the application of traffic management optimization allows outperforming the first-come-first-served management strategy even if the actual traffic conditions are not perfectly known by the optimization algorithm.