Case-based reasoning in personnel rostering

In this thesis a novel Case-Based Reasoning (CBR) system called CABAROST (CAsedBAsed ROSTering) for a complex, real-world, personnel rostering problem is presented. CBR is an artificial intelligence paradigm which attempts to solve new problems using information about the solution to previously encountered problems. CBR is used to capture and store examples of expert rostering behaviour which are then used to solve future problems. Previous examples of constraint violations in rosters and the repairs that were used to solve the violations are stored as cases. CABAROST generates repairs for violations found in a roster which imitate the decision making practice of the expert which trained it. A number of research issues that arise from using CBR for personnel rostering problems are addressed including: (a) representation of the nurse rostering problem as a constraint optimisation problem; (b) generalisation, selection, and weighting of case indices which will make the cases applicable to new problem instances; (c) retrieving and adapting cases from the case-base so that they are suitable to new problems; (d) hybridisation of CBR with meta-heuristic search methods; (e) and on-going case-base training and learning from failure. The research was carried out in collaboration with the Queen’s Medical Centre University Hospital NHS Trust, Nottingham, UK. They provided their experience in rostering in the form of examples and real-world data, and are actively involved in the testing and evaluation of the developed software system. In addition, the methods are applicable more generally to a wide variety of scheduling and other combinatorial optimisation problems.