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The manpower scheduling problem is a kind of critical combinationaloptimization problem. Researching solutions to scheduling problems can improvethe efficiency of companies, hospitals, and other work units. This paperproposes a new model combined with deep learning to solve the multi-shiftmanpower scheduling problem based on the existing research. This model firstsolves the objective function's optimized value according to the currentconstraints to find the plan of employee arrangement initially. It will thenuse the scheduling table generation algorithm to obtain the scheduling resultin a short time. Moreover, the most prominent feature we propose is that wewill use the neural network training method based on the time series to solvelong-term and long-period scheduling tasks and obtain manpower arrangement. Theselection criteria of the neural network and the training process are alsodescribed in this paper. We demonstrate that our model can make a preciseforecast based on the improvement of neural networks. This paper also discussesthe challenges in the neural network training process and obtains enlighteningresults after getting the arrangement plan. Our research shows that neuralnetworks and deep learning strategies have the potential to solve similarproblems effectively.

Apply Artificial Neural Network to Solving Manpower Scheduling Problem