@Article{iasc.2020.010108,
AUTHOR = {Hong Zhu},
TITLE = {Research on Maximum Return Evaluation of Human Resource Allocation  Based on Multi-Objective Optimization},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {26},
YEAR = {2020},
NUMBER = {4},
PAGES = {741--748},
URL = {http://www.techscience.com/iasc/v26n4/40278},
ISSN = {2326-005X},
ABSTRACT = {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.},
DOI = {10.32604/iasc.2020.010108}
}