Random Partial Neighborhood Search for University Course Timetabling Problem

Yuichi Nagata¹ and Isao Ono²

¹Education Academy of Computational Life Sciences, Tokyo Institute of Technology, Japan
nagata@is.tokushima-u.ac.jp

²Institute of Technology and Science, The University of Tokushima, Japan
isao@dis.titech.ac.jp

Abstract. We propose an tabu search algorithm using an candidate list stratety with random sampling for the university course timetabling problem, where the neighborhood size can be adjusted by a parameter ratio. With this framework, we can control the trade-off between exploration and exploitation by adjusting the neighborhood size. Experimental results show that the proposed algorithm outperforms state-of-the-art algorithms when the neighborhood size is set properly.

LNCS 8672, p. 782 ff.

Full article in PDF | BibTeX

lncs@springer.com
© Springer International Publishing Switzerland 2014