Data Mining Method of Enterprise Human Resource Management Based on Simulated Annealing Algorithm

Mingwei Xu,Chuang Li,Jian Su

doi:10.1155/2021/6342970

Abstract

The human resources department of an enterprise relies on the “mining” of big data when carrying out human resource management and proposes a data mining method for enterprise human resource management based on the simulated annealing algorithm. Applying the simulated annealing algorithm, using the Metropolis algorithm to generate the sequence of solutions to the combinatorial optimization problem, finding the overall optimal solution of the combinatorial optimization problem, using big data directional mining and analysis to help companies establish and find a “radar” system suitable for talents, the maximum tree method is adopted; that is, a special graph is constructed to realize the effective application of data mining technology in enterprise human resource management. The optimization of nurse scheduling in a hospital was used for case analysis. The results show that the target value of the nurse scheduling model is 43.43% lower than the actual manual scheduling target value, the salary cost is reduced by 10.8%, and the nurse’s satisfaction with the shift is increased by 35.24%. After several iterations based on the simulated annealing algorithm, the optimal value of the solution of the simulated annealing algorithm remains unchanged at the 60th generation. Then, the search process is stopped when the 100th generation is reached, and the solution at this time is the optimal optimization value found by the algorithm.

Highlights

Academic Editor: Jian Su e human resources department of an enterprise relies on the “mining” of big data when carrying out human resource management and proposes a data mining method for enterprise human resource management based on the simulated annealing algorithm
After several iterations based on the simulated annealing algorithm, the optimal value of the solution of the simulated annealing algorithm remains unchanged at the 60th generation. en, the search process is stopped when the 100th generation is reached, and the solution at this time is the optimal optimization value found by the algorithm
Adapting to the changes of the times and innovating the means and methods of human resource management is the biggest challenge and opportunity faced by enterprise human resource managers in the era of big data, and it is the key for enterprises to maintain their competitiveness in the fierce market competition. e simulated annealing (SA) algorithm [1] is an algorithm suitable for solving large-scale combinatorial optimization problems. e simulated annealing algorithm is derived from the simulation of the cooling process of solid annealing, using Metropolis criteria, including state space, state generation function, cooling schedule, Metropolis criteria, and internal and external cycle termination criteria

Summary

Research Article

Received 10 August 2021; Revised 18 September 2021; Accepted 27 September 2021; Published 14 October 2021. E simulated annealing algorithm is derived from the simulation of the cooling process of solid annealing, using Metropolis criteria, including state space, state generation function, cooling schedule, Metropolis criteria, and internal and external cycle termination criteria It is suitable for enterprise human resource management data mining.

Physical annealing

Week Number of nurses required

Findings

Number of iterations