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Staff Rostering Optimization: Ideal Recommendations vs. Real-World Computing Challenges

Conference paper
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Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 283)

Abstract

Staff rostering is a difficult and time-consuming problem that every company or institution that has employees working on shifts or on irregular working days must solve. The Finnish Institute of Occupational Health, which operates under the Ministry of Social Affairs and Health, published their recommendations for shift work in 2019. The recommended values for these individual factors are well justified. However, problems arise when all these recommendations should be satisfied together in real-world staff rostering. This paper shows what can be done to reach the best compromise considering the ideal recommendations, the employer’s point of view and the employees’ point of view. We use the PEAST metaheuristic, a computational intelligence framework, to show how the recommendations and employer requirements compete with each other. We give justification of why we can safely rely on the practical findings given by the metaheuristic.

Keywords

Staff rostering Nurse rostering Workforce scheduling Shift scheduling Shift work Workforce optimization Computational intelligence PEAST metaheuristic Real-world computing 

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Copyright information

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022

Authors and Affiliations

  1. 1.Satakunta University of Applied SciencesPoriFinland

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