Abstract
Team selection optimization is the foundation of enterprise strategy realization; it is of great significance for maximizing the effectiveness of organizational decision-making. Thus, the study of team selection/team foundation has been a hot topic for a long time. With the rapid development of information technology, big data has become one of the significant technical means and played a key role in many researches. It is a frontier of team selection study by the means of combining big data with team selection, which has the great practical significance. Taking strategic equilibrium matching and dynamic gain as association constraints and maximizing revenue as the optimization goal, the Hadoop enterprise information management platform is constructed to discover the external environment, organizational culture, and strategic objectives of the enterprise and to discover the potential of the customer. And in order to promote the renewal of production and cooperation mode, a team selection optimization model based on DPSO is built. The simulation experiment method is used to qualitatively analyze the main parameters of the particle swarm optimization in this paper. By comparing the iterative results of genetic algorithm, ordinary particle swarm algorithm, and discrete particle swarm algorithm, it is found that the DPSO algorithm is effective and preferred in the study of team selection with the background of big data.
Highlights
IntroductionGenetic algorithm and analytic hierarchy process were used to evaluate the effectiveness of team selection, in the background of today’s big data era, corporate teams generate huge amounts of data every day, and genetic algorithms can no longer solve large-scale computational problems
We could draw the conclusion from the related literature above that most researches put the emphasis on the qualitative analysis of index of selecting members and the quantitative research of team members’ optimal selection so far
Most researches put the emphasis on the qualitative analysis of index of selecting members and the quantitative research of team members’ optimal selection so far
Summary
Genetic algorithm and analytic hierarchy process were used to evaluate the effectiveness of team selection, in the background of today’s big data era, corporate teams generate huge amounts of data every day, and genetic algorithms can no longer solve large-scale computational problems. The relationship of optimization of enterprise information management and team selection under the background of big data and the establishment of analysis platform for enterprise information management big data based on Hadoop was introduced in Section 2; theoretical basis which included genetic algorithm and PSO algorithm was presented ; the principle of the optimization in team selection and matching was demonstrated, which contained strategic balanced matching principle and the principle of resource gain effect. In the end of the paper, the preferred optimization method for team selection in the context of big data and the conclusion were presented
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