Variable Neighborhood Search for Nurse Rostering Problems

Abstract

Nurse rostering problems consist of assigning varying tasks, represented as shift types, to hospital personnel with different skills and work regulations. The goal is to satisfy as many soft constraints and personal preferences as possible while constructing a schedule which meets the required personnel coverage of the hospital over a predefined planning period. Real-world situations are often so constrained that finding a good quality solution requires advanced heuristics to keep the calculation time down. In this paper, we present a variable neighbourhood search approach for the nurse rostering problem. It takes the broad variety of constraints into consideration and succeeds better in escaping from local optima than other previously tested meta- heuristics. Hidden parts of the solution space become accessible by applying appro- priate problem specific neighbourhoods. The method allows for a better exploration of the search space, by combining shortsighted neighbourhoods, and very greedy ones. Experiments demonstrate how heuristics and neighbourhoods can be assem- bled for finding good quality schedules in a short calculation time. We modelled Belgian nurse rostering problems, that appear to be among the most complex in the world. However, the nurse rostering search algorithms discussed in this paper are not aimed at specific hospitals. On the contrary, the intention is that such algorithms should be applicable across the whole sector.