Enhancing case-based reasoning for personnel rostering with selected tabu search concepts

In this paper, we investigate the advantages of using case-based reasoning (CBR) to solve personnel rostering problems. Constraints for personnel rostering problems are commonly categorized as either `hard' or `soft'. Hard constraints are those that must be satisfied and a roster that violates none of these constraints is considered to be `feasible'. Soft constraints are more flexible and are often used to measure roster quality in terms of staff satisfaction. We introduce a method for repairing hard constraint violations using CBR. CBR is an artificial intelligence paradigm whereby new problems are solved by considering the solutions to previous similar problems. A history of hard constraint violations and their corresponding repairs, which is captured from human rostering experts, is stored and used to solve similar violations in new rosters. The soft constraints are not defined explicitly. Their treatment is captured implicitly during the repair of hard constraint violations. The knowledge in the case-base is combined with selected tabu search concepts in a hybrid meta-heuristic algorithm. Experiments on real-world data from a UK hospital are presented. The results show that CBR can guide a meta-heuristic algorithm towards feasible solutions with high staff satisfaction, without the need to explicitly define soft constraint objectives.