Cargo Forecasting and Staff Scheduling Model Based on BP Neural Network Model and Integer Programming

In this paper, the first prediction is made through BP neural network model. And according to the graph and related data, it is found that the prediction results can be well in line with the trend, and the prediction results are more accurate. Finally, according to the discrete nature of scheduling personnel and constraints, a scheduling model based on integer planning is established, and reasonable scheduling of employees is realized under the conditions of the given data. In further validation, by changing the parameter definitions, decision variables, objective functions and constraints, it is possible to verify whether the attendance rate of employees and the number of employees attending every day meet the requirements according to the actual data, which reflects the better applicability of the model in this paper.