  The personnel rostering problem is the problem of finding an optimal way to
assign employees to shifts, subject to a set of hard constraints which all
valid solutions must follow, and a set of soft constraints which define the
relative quality of valid solutions. The problem has received significant
attention in the literature and is addressed by a large number of exact and
metaheuristic methods. In order to make the complex and costly design of
heuristics for the personnel rostering problem automatic, we propose a new
method combined Deep Neural Network and Tree Search. By treating schedules as
matrices, the neural network can predict the distance between the current
solution and the optimal solution. It can select solution strategies by
analyzing existing (near-)optimal solutions to personnel rostering problem
instances. Combined with branch and bound, the network can give every node a
probability which indicates the distance between it and the optimal one, so
that a well-informed choice can be made on which branch to choose next and to
prune the search tree.
