Jens Maue

Multistage methods for freight train classification

In this article, we study the train classification problem. Train classification basically is the process of rearranging the cars of a train in a specified order, which can be regarded as a special sorting problem. This sorting is done in a special railway installation called a classification yard, and a classification process is described by a classification schedule. In this article, we develop a novel encoding of classification schedules, which allows characterizing train classification methods simply as classes of schedules. Applying this efficient encoding, we achieve a simpler, more precise analysis of well-known classification methods. Furthermore, we elaborate a valuable optimality condition inherent in our encoding, which we succesfully apply to obtain tight lower bounds for the length of schedules in general and to develop new classification methods. Finally, we present complexity results and algorithms to derive optimal schedules for several real-world settings. Together, our theoretical results provide a solid foundation for improving train classification in practice.

Goal-directed shortest-path queries using precomputed cluster distances

We demonstrate how Dijkstras algorithm for shortest path queries can be accelerated by using precomputed shortest path distances. Our approach allows a completely flexible tradeoff between query time and space consumption for precomputed distances. In particular, sublinear space is sufficient to give the search a strong “sense of direction”. We evaluate our approach experimentally using large, real-world road networks.

Goal directed shortest path queries using precomputed cluster distances

We demonstrate how Dijkstras algorithm for shortest path queries can be accelerated by using precomputed shortest path distances. Our approach allows a completely flexible tradeoff between query time and space consumption for precomputed distances. In particular, sublinear space is sufficient to give the search a strong “sense of direction”. We evaluate our approach experimentally using large, real-world road networks.

Shunting for Dummies: An Introductory Algorithmic Survey

In this survey we present a selection of commonly used and new train classification methods from an algorithmic perspective.

Engineering algorithms for approximate weighted matching

We present a systematic study of approximation algorithms for the maximum weight matching problem. This includes a new algorithm which provides the simple greedy method with a recent path heuristic. Surprisingly, this quite simple algorithm performs very well, both in terms of running time and solution quality, and, though some other methods have a better theoretical performance, it ranks among the best algorithms.

Track Allocation in Freight-Train Classification with Mixed Tracks

We consider the process of forming outbound trains from cars of inbound trains at rail-freight hump yards. Given the arrival and departure times as well as the composition of the trains, we study t ...

Robust algorithms for sorting railway cars

We consider a sorting problem from railway optimization called train classification: incoming trains are split up into their single cars and reassembled to form new outgoing trains. Trains are subject to delay, which may turn a prepared sorting schedule infeasible for the disturbed situation. The classification methods applied today deal with this issue by completely disregarding the input order of cars, which provides robustness against any amount of disturbance but also wastes the potential contained in the a priori knowledge about the input. We introduce a new method that provides a feasible sorting schedule for the expected input and allows to flexibly insert additional sorting steps if the schedule has become infeasible after revealing the disturbed input. By excluding disruptions that almost never occur from our consideration, we obtain a classification process that is quicker than the current railway practice but still provides robustness against realistic delays. In fact, our algorithm allows flexibly trading off fast classification against high degrees of robustness depending on the respective need. We further explore this flexibility in experiments on real-world traffic data, underlining our algorithm improves on the methods currently applied in practice.

Experimental evaluation of approximation and heuristic algorithms for sorting railway cars

In this paper we consider a sorting problem from railway optimization called train classification, which is NP-hard in general. We introduce two new variants of an earlier developed 2-approximation as well as a new heuristic for finding feasible classification schedules, i.e. solutions for the train classification problem. We evaluate the four algorithms experimentally using various synthetic and real-world traffic instances and further compare them to an exact IP approach. It turns out that the heuristic matches up to the basic approximation, but both are clearly outperformed by our heuristically improved 2-approximations. Finally, with an average objective value of only 5.4 % above optimal, the best algorithm gets close to the real-world schedules of the IP approach, so we obtain very satisfactory practical schedules extremely quickly.