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

The inherent difficulties in eliciting domain knowledge from experts are often encountered when applying artificial intelligence techniques to real-world problems characterised by multiple conflicting constraints. Definitions of optimal 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 concepts of optimality 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. An iterative roster repair system is presented that learns repair techniques from nurses with rostering experience.

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