52 research outputs found
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
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
Dynamic Algorithms for Recoverable Robustness Problems
Recently, the recoverable robustness model has been introduced in the optimization area. This model allows to consider disruptions (input data changes) in a unified way, that is, during both the strategic planning phase and the operational phase. Although the model represents a significant improvement, it has the following drawback: we are typically not facing only one disruption, but many of them might appear one after another. In this case, the solutions provided in the context of the recoverable robustness are not satisfying.
In this paper we extend the concept of recoverable robustness to deal not only with one single recovery step, but with arbitrarily many recovery steps. To this aim, we introduce the notion of dynamic recoverable robustness problems. We apply the new model in the context of timetabling and delay management problems. We are interested in finding efficient dynamic robust algorithms for solving the timetabling problem and in evaluating the price of robustness of the proposed solutions
Recoverable Robust Timetable Information
Timetable information is the process of determining a suitable travel route for a passenger. Due to delays in the original timetable, in practice it often happens that the travel route cannot be used as originally planned. For a passenger being already en route, it would hence be useful to know about alternatives that ensure that his/her destination can be reached.
In this work we propose a recoverable robust approach to timetable information; i.e., we aim at finding travel routes that can easily be updated when delays occur during the journey. We present polynomial-time algorithms for this problem and evaluate the performance of the routes obtained this way on schedule data of the German train network of 2013 and simulated delay scenarios
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
Recoverable Robustness for Railway Rolling Stock Planning
In this paper we explore the possibility of applying the notions
of Recoverable Robustness and Price of Recoverability (introduced
by [5]) to railway rolling stock planning, being interested in recoverability measures that can be computed in practice, thereby evaluating the robustness of rolling stock schedules. In order to lower bound the Price of Recoverability for any set of recovery algorithms, we consider an "optimal" recovery algorithm and propose a Benders decomposition approach to assess the Price of Recoverability for this "optimal" algorithm. We evaluate the approach on real-life rolling stock planning problems of NS, the main operator of passenger trains in the Netherlands. The preliminary results show that, thanks to Benders decomposition, our lower bound can be computed within relatively short time for our case study
Comparative Application of Methods for Nodes Capacity Assessment
Nowadays a considerable percentage of trains mature delays due to nodes and stations congestion. They are normally a combination of effects of routes conflicts in stations on lines and propagation in stations of delays suffered along the lines. Station areas represent the bottlenecks of railway operations, due to many incompatible train routes crossing each other that lead to many potential conflicts between trains. Goal of the research is to compare some literature methods to study nodes capacity, by application of if-then processes to analyze stability or variability of results obtained by various timetabling to occupy the minimum capacity and increase the number of trains. It is a typical critical circuit, which can be seen as the bottleneck of the timetable to prevent conflicts and delays. In order to tackle the purpose, the paper introduces synthetically the methods and applies them systematically to a complex network,
including single and double track lines and various typologies of stations. The further development includes the single and double track lines and various typologies of stations. The further development includes the
comparativ
Combining robustness and recovery in rapid transit network design
When designing a transport network, decisions are made according to an expected value for network state variables, such as infrastructure and vehicle conditions, which are uncertain at a planning horizon of up to decades. Because disruptions, such as infrastructure breakdowns, will arise and affect the network on the day of operations, actions must be taken from the network design. Robust network designs may be implemented but they are extremely expensive
if disruptions do not realise. In this paper, we propose a novel approach to the network design problem where robustness and recovery are combined. We look for the trade-off between efficiency and robustness accounting for the possibility of recovering from disruptions: recoverable robust network design. Computational experiments
drawn from fictitious and realistic networks show how the
presented approach reduces the price of robustness and recovery costs as compared to traditional robust and non-robust rapid transit network design approaches
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