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Automatically generating timetables has been an active research area for some time, but the application of this research in practice has been limited. We believe this is due to two reasons. Firstly, some of the models in the literature impose artificial upper bounds on time supplements. This causes a high risk of generating infeasibilities. Secondly, some models that leave out these upper bounds often generate solutions that contain some very large time supplements because these supplements are not penalised in the objective function. The reason is that these objective functions often do not completely correspond to the true goal of a timetable. We solve both problems by minimising our objective function: total passenger travel time, expected in practice. Since this function evaluates and indirectly steers all time related decision variables in the system, we do not need to further restrict the ranges of any of these variables. As a result, our model does not suffer from infeasibilities generated by such artificial upper bounds for supplements. Furthermore, some measures are taken to significantly speed up the solver times of our model. These combined features result in our model being solved more quickly than previous models. As a result, our method can be used for timetabling in practice. We demonstrate our claims by optimising, in about two hours only, the timetable of all 196 hourly passenger trains in Belgium. Assuming primary delay-distributions with an average of 2% on the minima of each activity, the optimised timetable reduces expected passenger time in practice, as evaluated on the macroscopic level, by 3.8% during peak hours. This paper demonstrates that we added two important missing steps to make cyclic timetabling for passengers really useable in practice: (i) the addition of the objective function of expected passenger time in practice and (ii) the reduction of computation time by addition of well chosen additional constraints. publisher: Elsevier articletitle: Reducing the passenger travel time in practice by the automated construction of a robust railway timetable journaltitle: Transportation Research Part B: Methodological articlelink: http://dx.doi.org/10.1016/j.trb.2015.12.007 content_type: article copyright: Copyright © 2015 Elsevier Ltd. All rights reserved. ispartof: Transportation Research Part B vol:84 issue:84 pages:124-156 status: published

If railway companies ask for station capacity numbers, their underlying question is in fact one about the platformability of extra trains. Train platformability depends not only on the infrastructure, buffer times, and the desired departure and arrival times of the trains, but also on route durations, which depend on train speeds and lengths, as well as on conflicts between routes at any given time. We consider all these factors in this paper. We assume a current train set and a future one, where the second is based on the expected traffic increase through the station considered. The platforming problem is about assigning a platform to each train, together with suitable in- and out-routes. Route choices lead to different route durations and imply different in-route-begin and out-route-end times. Our module platforms the maximum possible weighted sum of trains in the current and future train set. The resulting number of trains can be seen as the realistic capacity consumption of the schedule. Our goal function allows for current trains to be preferably allocated to their current platforms. Our module is able to deal with real stations and train sets in a few seconds and has been fully integrated by Infrabel, the Belgian Infrastructure Management Company, in their application called Ocapi, which is now used to platform existing and projected train sets and to determine the capacity consumption. publisher: Elsevier articletitle: The train platforming problem: The infrastructure management company perspective journaltitle: Transportation Research Part B: Methodological articlelink: http://dx.doi.org/10.1016/j.trb.2014.01.004 content_type: article copyright: Copyright © 2014 Elsevier Ltd. All rights reserved. ispartof: Transportation Research Part B vol:61 pages:55-72 status: published

In order to improve the robustness of a railway system in station areas, this paper introduces an iterative approach to successively optimize the train routing through station areas and to enhance this solution by applying some changes to the timetable in a tabu search environment. We present our vision on robustness and describe how this vision can be used in practice. By introducing the spread of the trains in the objective function for the route choice and timetabling module, we improve the robustness of a railway system. Using a discrete event simulation model, the performance of our algorithms is evaluated based on a case study for the Brussels' area. The computational results indicate an average improvement in robustness of 6.2% together with a decrease in delay propagation of about 25%. Furthermore, the effect of some measures like changing the train offer to further increase the robustness is evaluated and compared. publisher: Elsevier articletitle: Improving the robustness in railway station areas journaltitle: European Journal of Operational Research articlelink: http://dx.doi.org/10.1016/j.ejor.2013.10.062 content_type: article copyright: Copyright © 2013 Elsevier B.V. All rights reserved. ispartof: European Journal of Operational Research vol:235 issue:1 pages:276-286 status: published

In this paper, we consider complex, busy stations whose limited capacity is one of the main reasons of delay propagation. Our goal is to improve, during the planning phase, the robustness of a complex station by fully exploiting the potential of the available capacity. The main feature of our approach is the interaction between routing decisions, timetabling and platform assignments. By altering one of these, slack can be created to allow improvements by the others as well. An objective function that maximizes the time span between any two trains is defined and the timing of the trains and the way how trains are routed to the platforms are optimized in the scope of this objective. By maximizing the spread of the trains, potential conflicts are avoided which is beneficial for - but not identical to - robustness. Using our approach, the robustness in the station zone of Brussels, Belgium's main railway bottleneck, can be improved by 8%. Next to that, the amount of knock-on delay arising due to conflicts within this area can be halved. This performance of our approach is confirmed by a second case study based on the station zone of Antwerp. Young Railway Operations Research Award 2013 of IAROR. ispartof: pages:1-20 ispartof: 5th International Seminar on Railway Operations Modelling and Analysis - RailCopenhagen pages:1-20 ispartof: 5th International Seminar on Railway Operations Modelling and Analysis - RailCopenhagen location:Copenhagen, Denmark date:13 May - 15 May 2013 status: published

To design an optimal passenger train timetable one should choose a quality criterium or a combination of criteria. We consider the main quality criterium from a passenger perspective: journey time. This means that the expected time all passengers will spend when our timetable is put in practice is minimal, even taking into account typical train delays. From a train operator or rail infrastructure management company perspective, there are further concerns too, like the number of train units that has to take part in this schedule, their frequency, the number of drivers and other crew members. These factors are all related to cost to maintain the schedule but are here considered secondary and indeed, are here kept constant. We consider only the passenger criterium here. We analytically derive total stochastic expected passenger time as a closed formula, linearize it and use it as a goal function for optimizing the schedule using a mixed integer linear programming model. We applied this to all 224 current Belgian train relations, passing 550 train stations and calculated an optimal schedule in 3 hours. We believe this mathematically optimal approach is unique, in its detailed model of expected, stochastic passenger time, in its scale of implementation and in its use of actual current data from practice.

In the traveling repairman problem with profits, a repairman (also known as the server) visits a subset of nodes in order to collect time-dependent profits. The objective consists of maximizing the total collected revenue. We restrict our study to the case of a single server with nodes located in the Euclidean plane. We investigate the properties of this problem, and we derive a mathematical model assuming that the number of nodes to be visited is known in advance. We describe a tabu search algorithm with multiple neighborhoods, we test its performance by running it on instances from the literature and compare the outcomes with an upper bound. We conclude that the tabu search algorithm finds good-quality solutions fast, even for large instances.