Neural networked-assisted method for the nurse rostering problem

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To formulate a systematic and scientific nurse scheduling plan based on the nurse rostering problem (NRP), individual preferences and legal and other constraints must be fully considered. The NRP aims to optimize the allocation of human resources and to effectively reduce the workload to improve the efficiency and quality of nurses’ work. As various constraints must be considered, the NRP is complicated and is known to be NP-hard. The fast attainment of an efficient schedule is a challenging problem that requires urgent resolution. In this paper, we propose a neural network-assisted method for automating the complex and costly design of the heuristics. The method integrates a generic deep neural network (DNN) model and a generic recurrent neural network (RNN) model. The DNN model is used to guide the heuristic selection; the RNN model is embedded into the reconstruction mechanism to escape the local optimum. By treating schedules as matrices, the neural networks can help decide which heuristic to apply next and generate feasible solutions. Four experiments with different focuses based on benchmark instances are conducted by comparing different parameters as well as other state-of-the-art methods. Detailed computational results revealed the performance of the proposed method, results from the proposed algorithm outperforms the results reported in recent literature and state-of-the-art heuristic approaches in at least half of the instances, the proposed method also shows better performance compared with three reproduced deep reinforcement (DRL) learning methods, it is also competitive as an component of the hybrid method. • An attempt to apply neural network to nurse rostering problem. • Neural network models are general models. • A better performance for most of the instances in experimental results. • Experimental evaluation of different parameter settings. • Hybrid search strategy between depth-first and limited discrepancy searches.