of strategy 2 is much larger than that of strategy 3. Thus strategy 2 searches a much larger search space

![0_image_0.png](0_image_0.png)

![0_image_1.png](0_image_1.png) than that of strategy 3. In our future work we will test these strategies with adaptively adjusted parameter s on large size problems to further investigate the effect of fragment selection based on online constraint propagation during the search.

![0_image_2.png](0_image_2.png)

## 5 Conclusions And Future Work

In this paper we investigate a constraint-directed large neighborhood search approach for solving nurse rostering problems by integrating constraint programming techniques with a local search approach. Indentified by current research in the literature, a key factor in the large neighborhood search approach is the design of neighborhoods i.e. the choice of related variables to be relaxed and re-optimized within the fragment. We focus on designing several tailored and general fragment selection strategies and compared their effect upon the performance of the large neighborhood search approach. It is shown that both the domain specific knowledge and the information of constraint propagation contribute to identifying the appropriate fragment in the large neighborhood search. Results have been analyzed to recommend future work.