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The personnel rostering problem is the problem of finding an optimal way toassign employees to shifts, subject to a set of hard constraints which allvalid solutions must follow, and a set of soft constraints which define therelative quality of valid solutions. The problem has received significantattention in the literature and is addressed by a large number of exact andmetaheuristic methods. In order to make the complex and costly design ofheuristics for the personnel rostering problem automatic, we propose a newmethod combined Deep Neural Network and Tree Search. By treating schedules asmatrices, the neural network can predict the distance between the currentsolution and the optimal solution. It can select solution strategies byanalyzing existing (near-)optimal solutions to personnel rostering probleminstances. Combined with branch and bound, the network can give every node aprobability which indicates the distance between it and the optimal one, sothat a well-informed choice can be made on which branch to choose next and toprune the search tree.

Neural Networked Assisted Tree Search for the Personnel Rostering Problem