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| Content Provider | Springer Nature Link |
|---|---|
| Author | Chiarandini, Marco Birattari, Mauro Socha, Krzysztof Rossi Doria, Olivia |
| Copyright Year | 2006 |
| Abstract | The university course timetabling problem is an optimisation problem in which a set of events has to be scheduled in timeslots and located in suitable rooms. Recently, a set of benchmark instances was introduced and used for an ‘International Timetabling Competition’ to which 24 algorithms were submitted by various research groups active in the field of timetabling. We describe and analyse a hybrid metaheuristic algorithm which was developed under the very same rules and deadlines imposed by the competition and outperformed the official winner. It combines various construction heuristics, tabu search, variable neighbourhood descent and simulated annealing. Due to the complexity of developing hybrid metaheuristics, we strongly relied on an experimental methodology for configuring the algorithms as well as for choosing proper parameter settings. In particular, we used racing procedures that allow an automatic or semi-automatic configuration of algorithms with a good save in time. Our successful example shows that the systematic design of hybrid algorithms through an experimental methodology leads to high performing algorithms for hard combinatorial optimisation problems. |
| Starting Page | 403 |
| Ending Page | 432 |
| Page Count | 30 |
| File Format | |
| ISSN | 10946136 |
| Journal | Journal of Scheduling |
| Volume Number | 9 |
| Issue Number | 5 |
| e-ISSN | 10991425 |
| Language | English |
| Publisher | Kluwer Academic Publishers |
| Publisher Date | 2006-01-01 |
| Publisher Place | Boston |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | University course timetabling Local search methods Metaheuristics Hybrid algorithms Experimental methodology Algorithm engineering Artificial Intelligence (incl. Robotics) Optimization Calculus of Variations and Optimal Control Operations Research/Decision Theory Production/Logistics |
| Content Type | Text |
| Resource Type | Article |
| Subject | Artificial Intelligence Management Science and Operations Research Engineering Software |
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