Quantitative Analysis and Strategy Research on the Improvement of Human Resource Allocation Efficiency in Big Data Environment

In this paper, for the problem of optimal allocation of human resources in multiple R&D projects, the traditional genetic algorithm is introduced into the Tabu taboo search algorithm, which is able to jump out of the local optimal solution while ensuring the diversity of the population, so as to achieve the purpose of the global optimal solution. Three software system development projects of J Software Service Company have been selected as practice cases to explore the advantages of the improved genetic algorithm in optimizing human resource allocation. Fuzzy comprehensive evaluation and questionnaire surveys are also used to explore the efficiency and satisfaction of optimized solutions. According to the experiments, the enhanced genetic algorithm that is based on the enhanced genetic algorithm performs better than other algorithms. The efficiency value of human resource allocation in the project of J Software Service Company is 71.6, and more than 90% of the employees surveyed in the survey of the optimized scheme of staffing are considered to be satisfied.