Personnel Scheduling Problem under Hierarchical Management Based on Intelligent Algorithm

Abstract

This paper studies a special scheduling problem under hierarchical management in nurse staff. This is a more complex rostering problem than traditional nurse scheduling. The first is that the rostering requirements of charge nurses and general nurses are different under hierarchical management. The second is that nurses are preferable for relative fair rather than absolute fair under hierarchical management. The model aims at allocating the required workload to meet the operational requirements, weekend rostering preferences, and relative fairness preferences. Two hybrid heuristic algorithms based on multiobjective grey wolf optimizer (MOGWO) and three corresponding single heuristic algorithms are employed to solve this problem. The experimental results based on real cases from the Third People’s Hospital, Panzhihua, China, show that MOGWO does not as good as it does on other engineering optimization. However, the hybrid algorithms based on MOGWO are better than corresponding single algorithms on generational distance (GD) and spacing (SP) of Pareto solutions. Furthermore, for relative fair rostering objective, NSGAII-MOGWO has more power to find the optimal solution in the dimension of relative fairness.