Vol.26, No.4, 2020, pp.741-748, doi:10.32604/iasc.2020.010108
Research on Maximum Return Evaluation of Human Resource Allocation Based on Multi-Objective Optimization
  • Hong Zhu1,2,*
1 Xi'an Jiaotong University,Shaanxi,710049,China
2 Shaanxi XueQian Normal University,Shaanxi,710100,China
Mailing address: No. 28, Xianning West Road, Xi 'An City, Shaanxi Province
* Corresponding Author: Hong Zhu, affluent@163.com
In this paper, a human resource allocation method based on the multi-objective hybrid genetic algorithm is proposed, which uses the multi-stage decision model to resolve the problem. A task decision is the result of an interaction under a set of conditions. There are some available decisions in each stage, and it is easy to calculate their immediate effects. In order to give a set of optimal solutions with limited submissions, a multi-objective hybrid genetic algorithm is proposed to solve the combinatorial optimization problems, i.e. using the multiobjective hybrid genetic algorithm to find feasible solutions at all stages and the bilateral matching of the scientific research projects and participants. First, the mathematical description of the bilateral matching problem supporting members grouping is given. On this basis, a bilateral matching multi-objective decision-making model is established with the objective of optimizing three actual indexes of reasonable grouping. According to the characteristics of the model, a multi-objective genetic algorithm-based solution method is designed. Based on the matching model, a human resource management system based on a browser/server architecture is designed to improve the practicability. Finally, an example is given to demonstrate the effectiveness and feasibility of the model.
Human resource allocation, Hybrid genetic algorithm, Multi-objective optimization, Multistage decision making.
Cite This Article
H. Zhu, "Research on maximum return evaluation of human resource allocation based on multi-objective optimization," Intelligent Automation & Soft Computing, vol. 26, no.4, pp. 741–748, 2020.
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