|edoc-Server der Humboldt-Universität zu Berlin|
Peter Vekas, Corvinus University of Budapest|
Maarten H. van der Vlerk, University of Groningen
Willem K. Klein Haneveld, University of Groningen
|Title:||Optimizing existing railway timetables by means of stochastic programming|
|Date of Acceptance:||31.10.2012|
Stochastic Programming E-Print Series |
|Editors:||Julie L. Higle; Werner Römisch; Surrajeet Sen|
|Complete Preprint:||pdf (urn:nbn:de:kobv:11-100205493)|
|Metadata export: To export the complete metadata set as Endote or Bibtex format please click to the appropriate link.||Endnote Bibtex|
|print on demand: If you click on this icon you can order a print copy of this publication.|
|Diese Seite taggen: These icons lead to social bookmarking systems where you can create and manage personal bookmarks and discover bookmakrs of other users.|
|We present some models to find the best allocation of a limited amount of so-called running time supplements (extra minutes added to a timetable to reduce delays) on a railway line. By the best allocation, we mean the solution under which the sum of expected delays is minimal. Instead of trying to invent a completely new timetable, our aim is to finely adjust an already existing and well-functioning one. We model this inherently stochastic optimization problem by using two-stage recourse models from stochastic programming, following Vromans . We present an improved formulation, allowing for an efficient solution using a standard algorithm for recourse models. We include a case study that we managed to solve about 180 times faster than it was solved in . By comparing our solution with other, seemingly intuitive solutions, we show that finding the best allocation is not obvious, and implementing it in practice promises a significant improvement in the punctuality of trains. A technique to estimate the model parameters from empirical data and an approximating deterministic problem are also presented, along with some practical ideas that are meant to enhance the applicability of our models.|
These data concerning access statistics for individual documents
have been compiled using the webserver log files aggregated by AWSTATS.
They refer to a monthly access count to the full text documents as well as to the entry page.
As for format versions of a document which consist of multiple files (such as HTML) the highest monthly access number to one of the files (chapters) is shown respectivly.
To see the detailled access numbers please move the mouse pointer over the single bars of the digaram.
Gesamtzahl der Zugriffe seit Apr 2012: