Journal of Heuristics

, Volume 22, Issue 5, pp 667–697 | Cite as

Roster evaluation based on classifiers for the nurse rostering problem

  • Roman VáclavíkEmail author
  • Přemysl Šůcha
  • Zdeněk Hanzálek


The personnel scheduling problem is a well-known NP-hard combinatorial problem. Due to the complexity of this problem and the size of the real-world instances, it is not possible to use exact methods, and thus heuristics, meta-heuristics, or hyper-heuristics must be employed. The majority of heuristic approaches are based on iterative search, where the quality of intermediate solutions must be calculated. Unfortunately, this is computationally highly expensive because these problems have many constraints and some are very complex. In this study, we propose a machine learning technique as a tool to accelerate the evaluation phase in heuristic approaches. The solution is based on a simple classifier, which is able to determine whether the changed solution (more precisely, the changed part of the solution) is better than the original or not. This decision is made much faster than a standard cost-oriented evaluation process. However, the classification process cannot guarantee 100 % correctness. Therefore, our approach, which is illustrated using a tabu search algorithm in this study, includes a filtering mechanism, where the classifier rejects the majority of the potentially bad solutions and the remaining solutions are then evaluated in a standard manner. We also show how the boosting algorithms can improve the quality of the final solution compared with a simple classifier. We verified our proposed approach and premises, based on standard and real-world benchmark instances, to demonstrate the significant speedup obtained with comparable solution quality.


Neural network Nurse rostering problem Adaptive boosting Pattern learning 



This work was supported by ARTEMIS FP7 EU and by the Ministry of Education of the Czech Republic under the project DEMANES 295372 and by the Grant Agency of the Czech Republic under the Project GACR FOREST P103-16-23509S.


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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Roman Václavík
    • 1
    Email author
  • Přemysl Šůcha
    • 1
  • Zdeněk Hanzálek
    • 1
    • 2
  1. 1.Department of Control Engineering, Faculty of Electrical EngineeringCzech Technical University in PraguePrague 2Czech Republic
  2. 2.Czech Institute of Informatics, Robotics, and CyberneticsCzech Technical University in PraguePrague 6Czech Republic

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