The Nurse Scheduling Problem (NSP) is a well-known combinatorial optimization problem with significant practical implications in healthcare workforce management. In this paper, we investigate the use of Answer Set Programming (ASP) to model and solve realistic nurse scheduling scenarios in two Italian healthcare institutions: the Mariano Santo and the Annunziata hospitals. We design ASP encodings capable of handling both general and institution-specific constraints, including shift coverage requirements, rotation rules, and personal unavailability. We analyze the impact of solver configurations and optimization strategies, comparing the performance of clingo and wasp across multiple solving modes. Our experimental results show that unsatisfiable core-based strategies are able to find optimal solutions for the tested instances within a few seconds.