769 research outputs found
On the delivery robustness of train timetables with respect to production replanning possibilities
Measuring timetable robustness is a complex task. Previous efforts have mainly
been focused on simulation studies or measurements of time supplements.
However, these measurements don't capture the production flexibility of a
timetable, which is essential for measuring the robustness with regard to the
trains' commercial activity commitments, and also for merging the goals of
robustness and efficiency. In this article we differentiate between production
timetables and delivery timetables. A production timetable contains all stops,
meetings and switch crossings, while a delivery timetable only contains stops for
commercial activities. If a production timetable is constructed such that it can
easily be replanned to cope with delays without breaking any commercial activity
commitments it provides delivery robustness without compromising travel
efficiency. Changing meeting locations is one of the replanning tools available
during operation, and this paper presents a new framework for heuristically
optimising a given production timetable with regard to the number of alternative
meeting locations. Mixed integer programming is used to find two delivery feasible
production solutions, one early and one late. The area between the two solutions
represents alternative meeting locations and therefore also the replanning
enabled robustness. A case study from Sweden demonstrates how the method
can be used to develop better production timetables
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
State of the Art Overview on Automatic Railway Timetable Generation and Optimization
In railway transportation, each train needs to have a timetable that specifies which track at which time will be occupied by it. This task can be addressed by automatization techniques both in generating a timetable and in optimizing an existing one. In this paper, we give an overview on the state of the art of these techniques. We study the computation of a technically valid slot for a train that guarantees a (short) spatial and temporal way through the network. Furthermore, the construction of a cyclic timetable where trains operate e.g. every 60 minutes, and the simultaneous construction of timetables for multiple trains are considered in this paper. Finally, timetables also need to be robust against minor delays. We will review the state of the art in the literature for these aspects of railway timetabling with respect to models, solution algorithms, complexity results and applications in practice
Algorithm Engineering in Robust Optimization
Robust optimization is a young and emerging field of research having received
a considerable increase of interest over the last decade. In this paper, we
argue that the the algorithm engineering methodology fits very well to the
field of robust optimization and yields a rewarding new perspective on both the
current state of research and open research directions.
To this end we go through the algorithm engineering cycle of design and
analysis of concepts, development and implementation of algorithms, and
theoretical and experimental evaluation. We show that many ideas of algorithm
engineering have already been applied in publications on robust optimization.
Most work on robust optimization is devoted to analysis of the concepts and the
development of algorithms, some papers deal with the evaluation of a particular
concept in case studies, and work on comparison of concepts just starts. What
is still a drawback in many papers on robustness is the missing link to include
the results of the experiments again in the design
An Empirical Analysis of Robustness Concepts for Timetabling
Calculating timetables that are insensitive to disturbances has drawn
considerable research efforts due to its practical importance on the one hand
and its hard tractability by classical robustness concepts on the other hand.
Many different robustness concepts for timetabling have been suggested in the
literature, some of them very recently. In this paper we compare such concepts
on real-world instances. We also introduce a new approach that is generically
applicable to any robustness problem. Nevertheless it is able to adapt the
special characteristics of the respective problem structure and hence generates
solutions that fit to the needs of the respective problem
Railway Rolling Stock Planning: Robustness Against Large Disruptions
In this paper we describe a two-stage optimization model for determining robust rolling stock circulations for passenger trains. Here robustness means that the rolling stock circulations can better deal with large disruptions of the railway system. The two-stage optimization model is formulated as a large mixed-integer linear programming (MILP) model. We first use Benders decomposition to determine optimal solutions for the LP-relaxation of this model. Then we use the cuts that were generated by the Benders decomposition for computing heuristic robust solutions for the two-stage optimization model. We call our method Benders heuristic. We evaluate our approach on the real-life rolling stock-planning problem of Netherlands Railways, the main operator of passenger trains in the Netherlands. The computational results show that, thanks to Benders decomposition, the LP-relaxation of the two-stage optimization problem can be solved in a short time for a representative number of disruption scenarios. In addition, they demonstrate that the robust rolling stoc
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