A Genetic Algorithm Model for Human Resource Management Optimization in the Internet Marketing Era

In the context of the Internet marketing era, rational use of big data for quantitative analysis of human resource management can effectively help each unit better understand the industry talent and analyze the development trend of the industry. Personnel competency is the key factor affecting job p...

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Bibliographic Details
Published inMathematical problems in engineering Vol. 2022; pp. 1 - 9
Main Authors Li, Yi, Linna, Wang
Format Journal Article
LanguageEnglish
Published New York Hindawi 31.08.2022
Hindawi Limited
Subjects
Constraints
Consumption
Cost analysis
Efficiency
Employees
Genetic algorithms
Human performance
Human resource management
Industrial development
Integer programming
Internet
Marketing
Optimization
Optimization models
Personnel
Professional development
Project management
Renewable resources
Resource scheduling
Scheduling
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Summary:In the context of the Internet marketing era, rational use of big data for quantitative analysis of human resource management can effectively help each unit better understand the industry talent and analyze the development trend of the industry. Personnel competency is the key factor affecting job performance. The renewable resources in the resource constrained project scheduling problem are transformed into human resources with competency differences through a series of scientific and reasonable methods. A human resource constrained project scheduling problem model emphasizing competency differences is constructed. The most prominent advantage of this model lies in the selection of indicators that can objectively and reasonably evaluate the competency of personnel, and the rigorous and scientific relationship is provided. The complex multiproject double-objective minimization problem of the total construction period and total cost is transformed into a comprehensive index single-objective maximization problem. Then, a mathematical optimization model is established and solved by a genetic algorithm. Finally, the effectiveness of the algorithm is shown by comparing numerical experiments with other algorithms.
ISSN:1024-123X
1563-5147
DOI:10.1155/2022/6931386