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2012-10-31Buch DOI: 10.18452/8428
Optimizing existing railway timetables by means of stochastic programming
dc.contributor.authorVekas, Peter
dc.contributor.authorVlerk, Maarten H. van der
dc.contributor.authorHaneveld, Willem K. Klein
dc.contributor.editorHigle, Julie L.
dc.contributor.editorRömisch, Werner
dc.contributor.editorSen, Surrajeet
dc.date.accessioned2017-06-16T20:27:37Z
dc.date.available2017-06-16T20:27:37Z
dc.date.created2012-11-05
dc.date.issued2012-10-31
dc.date.submitted2012-06-14
dc.identifier.urihttp://edoc.hu-berlin.de/18452/9080
dc.description.abstractWe present some models to find the best allocation of a limited amount of so-called runningtime supplements (extra minutes added to a timetable to reduce delays) on a railway line. Bythe 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 alreadyexisting and well-functioning one. We model this inherently stochastic optimization problemby using two-stage recourse models from stochastic programming, following Vromans [9]. Wepresent an improved formulation, allowing for an efficient solution using a standard algorithmfor recourse models. We include a case study that we managed to solve about 180 times fasterthan it was solved in [9]. By comparing our solution with other, seemingly intuitive solutions,we show that finding the best allocation is not obvious, and implementing it in practicepromises a significant improvement in the punctuality of trains. A technique to estimate themodel parameters from empirical data and an approximating deterministic problem are alsopresented, along with some practical ideas that are meant to enhance the applicability of ourmodels.eng
dc.language.isoeng
dc.publisherHumboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät II, Institut für Mathematik
dc.subject.ddc510 Mathematik
dc.titleOptimizing existing railway timetables by means of stochastic programming
dc.typebook
dc.identifier.urnurn:nbn:de:kobv:11-100205493
dc.identifier.doihttp://dx.doi.org/10.18452/8428
local.edoc.container-titleStochastic Programming E-Print Series
local.edoc.pages17
local.edoc.type-nameBuch
local.edoc.container-typeseries
local.edoc.container-type-nameSchriftenreihe
local.edoc.container-volume2012
local.edoc.container-issue8
local.edoc.container-erstkatid2936317-2

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