Restart techniques for randomizing complete search algorithms were proposed recently by Gomes et al., for solving hard combinatorial problems. A new iterative algorithm that uses restart techniques is proposed, and its behavior analyzed on random timetabling problems. Employee timetabling problems (ETPs) can be naturally represented by constraints networks for real world instances which are large and difficult. Complete search algorithms, even with good heuristics are unable to solve large enough instances of ETPs. In fact, several local search techniques have been proposed in the past decade for solving timetabling problems. In particular, it has been shown that local search can efficiently solve large ETPs. Random instances of ETPs that as generated based on real world ETPs are used to test the proposed iterative restart algorithm, GIRA. The two main parameters of GIRA are pointed out and investigated: the initial cutoff value for restart and the number of idle iterations. For random sets of a large range of hardness of ETPs, the successful values of these two parameters tend to be surprisingly low. We conclude by recommending an optimal range of values for the initial cutoff and demonstrate experimentally that the number of idle iterations is of little consequence.
