Automating Personnel Rostering by Learning Constraints Using Tensors
  Many problems in operations research require that constraints be specified in
the model. Determining the right constraints is a hard and laborsome task. We
propose an approach to automate this process using artificial intelligence and
machine learning principles. So far there has been only little work on learning
constraints within the operations research community. We focus on personnel
rostering and scheduling problems in which there are often past schedules
available and show that it is possible to automatically learn constraints from
such examples. To realize this, we adapted some techniques from the constraint
programming community and we have extended them in order to cope with
multidimensional examples. The method uses a tensor representation of the
example, which helps in capturing the dimensionality as well as the structure
of the example, and applies tensor operations to find the constraints that are
satisfied by the example. To evaluate the proposed algorithm, we used
constraints from the Nurse Rostering Competition and generated solutions that
satisfy these constraints; these solutions were then used as examples to learn
constraints. Experiments demonstrate that the proposed algorithm is capable of
producing human readable constraints that capture the underlying
characteristics of the examples.
