|
|
Detailed schedule
Click on a link for more details
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
- Combining metaheuristics and exact methods to solve the multiobjective multidimensional knapsack problem
Thibaut Lust (Faculté Polytechnique de Mons) Co-authors: Jacques Teghem
- Hyper-heuristics learning a varying set of low-level heuristics
Mustafa Misir (KaHo Sint-Lieven - Katholieke Universiteit Leuven) Co-authors: Katja Verbeeck, Greet Vanden Berghe, Patrick De Causmaecker Abstract: The main motivation behind using hyper-heuristics is related to providing generality for solving different combinatorial optimisation problems. Hyper-heuristics perform on a higher level than traditional search and optimisation strategies. They operate on a set of solution approaches (i.e. low-level heuristics) rather than on the set of solutions directly. The performance of heuristics can vary from a problem(-instance) to a problem(-instance). They may even behave differently in various search regions of one problem. Therefore, using a management mechanism on top of a number of search algorithms can help to find the most appropriate heuristics to apply. This kind of management can be handled by choice hyper-heuristics. A simple choice hyper-heuristic consists of a heuristic selection mechanism and a move acceptance mechanism. While a heuristic selection mechanism chooses heuristics for applying to a solution(s) at hand, a move acceptance mechanism concludes whether the new solution(s) is good enough to accept.
In this study, we focus on the heuristic selection part. We propose a method for determining the set of best performing heuristics that should be used during different phases of a search. This subset is formed based on the relative improvement per execution time of each heuristic. At the end of each phase, heuristics are ranked according to some quality related values (quality index). That is, the quality index of the best performing heuristic is set to n and for the worst heuristic, this value is set to 1. The index value of heuristics that do not provide any improvement are automatically set to 1. All heuristics which have a quality index value lower than average are excluded from the search for the next phase. In addition, when the set of best performing heuristics becomes too small, all the excluded heuristics are entered in the heuristic set again, and the algorithm repeats itself.
We apply our new hyper-heuristic approach to a set of home care scheduling problem instances.
- Using the PlayStation3 for speeding up metaheuristic optimization
Sofie Van Volsem (Ghent University) Co-authors: S. Neirynck
- No-Wait Scheduling of a Single-Machine to Minimize the Maximum Lateness
Imed Kacem (UNIVERSITY PAUL VERLAINE METZ) Co-authors: Hans Kellerer
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
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
|
|