Automating Personnel Rostering by Learning Constraints Using Tensors

Many problems in operations research require that constraints be specified in the model. Determining right constraints is a hard and laborsome task. We propose an approach to automate this process using artificial intelligence and machine learning principles. 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 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. The algorithm also identifies inherent clusters in the data and uses it as background knowledge to learn more detailed constraints. 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.