E. K. Burke
Three methods to automate the space allocation process in UK universities
Burke, E. K.; Cowling, P.; Landa Silva, J. D.; McCollum, Barry
Authors
P. Cowling
Professor DARIO LANDA SILVA DARIO.LANDASILVA@NOTTINGHAM.AC.UK
PROFESSOR OF COMPUTATIONAL OPTIMISATION
Barry McCollum
Abstract
The space allocation problem within UK universities is highly constrained, has multiple objectives, varies greatly among different institutions, requires frequent modifications and has a direct impact on the functionality of the university. As in every optimisation problem, the application of different advanced search methodologies such as local search, metaheuristics and evolutionary algorithms provide a promising way forward. In this paper we discuss three well known methods applied to solve the space allocation problem: hill climbing, simulated annealing and a genetic algorithm. Results and a comprehensive comparison between all three techniques are presented using real test data. Although these algorithms have been extensively studied in different problems, our major objective is to investigate the application of these techniques to the variants of the space allocation problem, comparing advantages and disadvantages to achieve a better understanding of the problem and propose future hybridisation of these and additional methods. © Springer-Verlag Berlin Heidelberg 2001.
Citation
Burke, E. K., Cowling, P., Landa Silva, J. D., & McCollum, B. (2000, August). Three methods to automate the space allocation process in UK universities. Presented at Third International Conference, PATAT 2000, Konstanz, Germany
Presentation Conference Type | Edited Proceedings |
---|---|
Conference Name | Third International Conference, PATAT 2000 |
Start Date | Aug 16, 2000 |
End Date | Aug 18, 2000 |
Publication Date | Jan 1, 2001 |
Deposit Date | Feb 10, 2020 |
Publisher | Springer |
Peer Reviewed | Peer Reviewed |
Pages | 254-273 |
Series Title | Lecture Notes in Computer Science |
Series Number | 5467 |
Series ISSN | 1611-3349 |
Book Title | Practice and Theory of Automated Timetabling III |
ISBN | 978-3-642-01019-4 |
DOI | https://doi.org/10.1007/978-3-642-01020-0_38 |
Public URL | https://nottingham-repository.worktribe.com/output/3088203 |
Publisher URL | https://link.springer.com/chapter/10.1007/3-540-44629-X_16 |
You might also like
Local-global methods for generalised solar irradiance forecasting
(2024)
Journal Article
Evolving Deep CNN-LSTMs for Inventory Time Series Prediction
(2019)
Presentation / Conference Contribution