Data-Driven Optimal Scheduling Algorithm of Human Resources in Colleges and Universities

At present, the development process of human resource management in Colleges and universities in China has gone through a period of time. In the whole process, the mode of human resource management in colleges and universities is gradually maturing, but there are also some problems. In this paper, d...

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Vydáno v:Scientific programming Ročník 2022; s. 1 - 10
Hlavní autor: Liu, Cong
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Hindawi 22.03.2022
John Wiley & Sons, Inc
Témata:
Algorithms
Colleges & universities
Decision making
Feedback
Fuzzy sets
Human resource management
Learning
Methods
Optimization
Performance appraisal
Probability distribution
Problem solving
Random variables
Resource scheduling
Robustness
Scheduling
Teachers
Teaching
ISSN:1058-9244, 1875-919X
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Shrnutí:At present, the development process of human resource management in Colleges and universities in China has gone through a period of time. In the whole process, the mode of human resource management in colleges and universities is gradually maturing, but there are also some problems. In this paper, data-driven stochastic optimal scheduling algorithm and robust optimal scheduling algorithm are used to model and analyze the human resources. Then, the two models are applied to the human resource management of Nanjing University and Southeast University. The data optimization results show that the robust optimal scheduling algorithm is helpful to the management of new teachers, while the random optimal scheduling algorithm can improve the management of teachers who have been in service for a long time, but there are still some disadvantages. If the combination of two data-driven scheduling algorithms is adopted, it can manage the human resources of colleges and universities well. In short, this paper provides some theoretical and experimental support for the specific application of data-driven human resources optimal scheduling algorithm in colleges and universities.
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ISSN:1058-9244
1875-919X
DOI:10.1155/2022/8602015