Research on Efficient Model of Enterprise Human Resource Planning and Scheduling under Intelligent Management Framework

Abstract

Aiming at the complex problems of human resource scheduling in enterprise multi-project management, this paper constructs a multi-objective scheduling model. Adaptive pheromone updating strategy is introduced to dynamically adjust the volatility coefficient and increment, and a dynamic candidate list mechanism based on multi-attribute evaluation is designed. Combined with local search optimization, an improved ant colony algorithm is proposed. A case study is conducted to verify the effectiveness of the proposed algorithm. The results show that the improved algorithm can converge to the Pareto optimal solution set after about 50 iterations, and the shortest working time is 23 days. Compared with the improved ACO algorithm in this paper, the SW+Random algorithm has a total cost ratio of 156.34% and a duration ratio of 124.45%. After testing, the proposed scheme can effectively predict the comprehensive execution value of enterprise human resource planning and scheduling, and large enterprises can use it to evaluate various programs in advance and then choose the optimal personnel scheduling strategy.