INTELLIGENT DATA ANALYSIS IN ENTERPRISE MANAGEMENT BASED ON THE ANNEALING SIMULATION ALGORITHM

Abstract

The article considers an analytical review of the annealing simulation algorithm for the problem of efficient enterprise management. The optimization of the annealing simulation algorithm for the problem of efficient enterprise management has been carried out. For the analysis of cases, the optimization of the work schedule of workers in the organization was used. Established worker scheduling model with strong and weak constraints. The simulated annealing algorithm is used to optimize the strategy for solving the staff scheduling model. The simulated annealing algorithm is an algorithm suitable for solving large-scale combinatorial optimization problems. It also evaluates and obtains the optimal scheduling strategy. The simulated annealing algorithm has a good effect on the data mining of human resource management. Big data mining can help companies conduct dynamic analysis in talent recruitment, and the talent recruitment plan is carried out in a quality and standard way to analyze the characteristics of various talents from many angles and improve the level of human resource management. An algorithm has been developed that implements the operation of the annealing simulation algorithm. The simulated annealing algorithm makes new decisions based on the Metropolis criterion, so in addition to making an optimized decision, it also makes a reduced decision in a limited range. The Metropolis algorithm is a sampling algorithm mainly used for complex distribution functions. It is somewhat similar to the variance sampling algorithm, but here the auxiliary distribution function changes over time. Experimental studies have been carried out that show that a worker scheduling model based on strong and weak constraints is significantly better than a manual scheduling model, achieving an effective balance between controlling wage costs in an organization and increasing employee satisfaction. The successful application of a workforce scheduling model based on a simulated annealing algorithm brings new insights and insights to solve large-scale worker scheduling problems. The results presented can serve as a starting point for studying personnel management systems based on data mining technology.