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Acknowledgments

The first author is very grateful for the valuable discussion and help of Robert Irwin in converting and formatting the registration data prior to the scheduling process. We also would like to thank Andrew Gee and Martin Simmen for the useful comments and suggestions, and Carsten Peterson for the pointers and comments about his papers. Many thanks go to Karen Bedard for providing us with the data and answering so many questions we had about it, Meg Cortese for providing us with a set of building constraints for various departments, and Prof. Ben Ware, Vice President for Research and Computing at Syracuse University, for his support and encouragement.

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Table 1: Size of the data set for each of the three semesters.

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Table 2: The sparseness ratios of the problem for the data sets for each of the three semesters. Lower values indicate a harder problem.

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Table 3: Simulated annealing (SA) results: percentage of scheduled classes, averaged over 10 runs of the same initial temperature and other parameters for three terms. Expert system (ES) results: percentage of scheduled classes is averaged over 10 runs. Mean-field annealing (MFA) results: percentage of scheduled classes is also averaged over 10 runs. No preprocessor was used with the three methods.

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Table 4: Percentage of scheduled classes, averaged over 10 runs of the same initial temperature and other parameters, for three terms using simulated anealing with an expert system as preprocessor.



Saleh Elmohamed
Tue Apr 29 19:08:49 EDT 1997