  The manpower scheduling problem is a kind of critical combinational
optimization problem. Researching solutions to scheduling problems can improve
the efficiency of companies, hospitals, and other work units. This paper
proposes a new model combined with deep learning to solve the multi-shift
manpower scheduling problem based on the existing research. This model first
solves the objective function's optimized value according to the current
constraints to find the plan of employee arrangement initially. It will then
use the scheduling table generation algorithm to obtain the scheduling result
in a short time. Moreover, the most prominent feature we propose is that we
will use the neural network training method based on the time series to solve
long-term and long-period scheduling tasks and obtain manpower arrangement. The
selection criteria of the neural network and the training process are also
described in this paper. We demonstrate that our model can make a precise
forecast based on the improvement of neural networks. This paper also discusses
the challenges in the neural network training process and obtains enlightening
results after getting the arrangement plan. Our research shows that neural
networks and deep learning strategies have the potential to solve similar
problems effectively.
