Apply Artificial Neural Network to Solving Manpower Scheduling Problem

• URL: http://arxiv.org/abs/2105.03541v1
• Date: Fri, 7 May 2021 23:54:00 GMT
• Title: Apply Artificial Neural Network to Solving Manpower Scheduling Problem
• Authors: Tianyu Liu and Lingyu Zhang
• Abstract summary: This paper proposes a new model combined with deep learning to solve the multi-shift manpower scheduling problem. We will use the neural network training method based on the time series to solve long-term and long-period scheduling tasks. Our research shows that neural networks and deep learning strategies have the potential to solve similar problems effectively.
• Score: 15.848399017432262
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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