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Detailed schedule
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Show all the abstracts
Show all the abstracts
Thursday 11:00:00 Timetabling in education and sport Room 126 - Chair: G. Vanden Berghe
Thursday 11:00:00 Transportation management Room 130 - Chair: F. Semet
Thursday 11:00:00 Networks Room 138 - Chair: B. Fortz
Thursday 11:00:00 Nonconvex optimization 1 Room 035 - Chair: F. Bach
Thursday 14:00:00 Constraint programming models 1 Room 126 - Chair: Y. Deville
Thursday 14:00:00 Vehicle routing Room 130 - Chair: S. Limbourg
Thursday 14:00:00 Combinatorial optimization and IP applications Room 138 - Chair: Q. Louveaux
Thursday 14:00:00 Nonconvex Optimization 2 Room 035 - Chair: R. Sepulchre
Thursday 16:10:00 Constraint programming models 2 Room 126 - Chair: P. Schaus
Thursday 16:10:00 Performance modeling Room 130 - Chair: G. Janssens
Thursday 16:10:00 Scheduling Room 138 - Chair: K. Sorensen
Thursday 16:10:00 Planning under uncertainty Room 035 - Chair: R. Leus
Friday 09:00:00 Metaheuristics Room 126 - Chair: J. Teghem
Friday 09:25:00 Production and distribution (9:25) Room 130 - Chair: Y. Arda
Friday 09:00:00 Multiple criteria Room 138 - Chair: R. Bisdorff
Friday 09:25:00 Stochastic models (9:25) Room 035 - Chair: L. Esch
- Extra-Small Scenario Trees for Multistage Stochastic Programming
Boris Defourny (University of Liege) Co-authors: Damien Ernst and Louis Wehenkel
- Extinction Probability of a Branching Process in a Markovian Random Environment
Sophie Hautphenne (Université Libre de Bruxelles) Co-authors: Guy Latouche
- A clustering approach to estimate route travel time distributions
Wouter Charle (K.U.Leuven) Co-authors: Francesco Viti, Chris M.J. Tampère Abstract: Accurate network travel time estimation is today one of the most challenging problems in trac
theory. The mainstream research on travel time estimation concentrates on the estimation of
mean route travel time or some measures of travel time reliability (i.e. 10th and 90th percentiles).
However, given the growing detail in travel time measurements it is also possible to estimate route
travel time distributions. This serves a broader spectrum of applications and provides more useful
information. For this goal, this research presents a method to calculate the travel time histogram
of a route, based on link travel time observations.
The aim of this new method is to allow the development of an advanced route planner that
is able to optimize route choice reliability. The notion of reliability is dened by the end-user
of the route planner and highly depends on the properties of the route travel time distribution.
Central in the development of the route planner is the distinction between (cheap) o-line storage
and computations and (expensive) on-line computations. For that it is important to minimize the
on-line computational eort of calculating a route travel time histogram. The method described in
this study can be deployed beyond route planning applications and the eld of trac management.
Other applications can be for instance the location optimization of logistic hubs (i.e. airports,
packaging services, . . . ) or public services (i.e. hospitals, re stations, . . . ) and the evaluation of
network performance.
Friday 11:00:00 Constraint programming and Supply Chain Management Room 126 - Chair: Y. Deville
Friday 11:00:00 OR in health management Room 130 - Chair: P. De Causmaecker
Friday 11:00:00 Rankings and importance indices Room 138 - Chair: JL. Marichal
Friday 11:00:00 Queueing Room 035 - Chair: S. Wittevrongel
Friday 15:10:00 Optimization software Room 126 - Chair: E. Loute
Friday 15:10:00 Integrated operations planning Room 130 - Chair: B. Raa
Friday 15:10:00 Cycles in graphs Room 138 - Chair: F. Spieksma
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