Optimization adjustment of human resources based on dynamic heterogeneous network

The human resources network, involves enterprise social networks and job networks, can be abstracted as heterogeneous networks or multi-layers networks. Adjusting the position assignments to maximize employee productivity and minimize the company's cost is the goal of organization optimization. Taking the churn and interaction among the staff into account, this paper puts forward a dynamic optimization model for human resource adjustment, which is based on heterogeneous network, to describe the influence among individuals who are in personal relationship or professional relationship. More specifically, intimacy and loyalty are constructed to form the basis of churn rate, which indicate the influence of the personal and professional relationship respectively. With the operation of the organization, the change of intimacy and loyalty leads to the churn process, which are simulated with Monte Carlo method in a dynamic process among the heterogeneous network. After churning, an optimal strategy of recruitment and position adjustment is obtained using the Genetic Algorithm. In general, the human resource optimization process consists three periodic parts: loyalty and intimacy transformation, staff churn simulation and position assignment. Finally, a case study of an organization with 370 employee positions is carried out to demonstrate the whole process.