Case-based reasoning in employee rostering: learning repair strategies from domain experts

The development of methods for solving real world scheduling problems such as employee rostering requires the extensive domain knowledge of manual rostering experts. Rostering problems are subject to numerous conflicting constraints and are difficult to solve. Automated rostering has attracted the attention of the scientific community over the last three decades, however the systematic representation of expert knowledge remains problematic. Furthermore, the definitions of ‘good’ solutions are often subjective and highly dependent on the opinions and work practices of individual experts. We developed a case-based reasoning approach to capture rostering knowledge through the storage, reuse, and adaptation of previous repairs of constraint violations. The technique is applied to the problem of rostering nurses at the Queens Medical Centre, Nottingham.