Knowledge worker scheduling optimization model based on bacterial foraging algorithm

• Published in: Future generation computer systems Vol. 124; pp. 330 - 337
• Main Authors: Dan, Yufang, Tao, Jianwen
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.11.2021
• Subjects: Artificial intelligence management; Bacterial foraging algorithm; Convergence accuracy; Convergence rate; Enterprise employee scheduling; Multi difficulty scheduling problem; Convergence rate; Artificial intelligence management; Enterprise employee scheduling; Bacterial foraging algorithm; Convergence accuracy; Multi difficulty scheduling problem
• ISSN: 0167-739X; 1872-7115
• DOI: 10.1016/j.future.2021.05.028

Bacterial foraging algorithm comes from the best survival selection mechanism of animals in nature. As the representative of the heuristic algorithm, the bacterial foraging algorithm has unique advantages in solving the multi difficulty scheduling problem effectively. In order to realize the artificial intelligent management of the enterprise’s staff scheduling, this paper constructs the knowledge staff scheduling model by using a bacterial foraging algorithm and analyzes the implementation principle, advantages, and disadvantages of the algorithm. The influence of the basic parameters in the algorithm model on the algorithm performance is analyzed. In order to optimize the unconventional foraging strategy, the improvement measures of bacterial foraging behavior were proposed. Finally, the performance of the optimized bacterial foraging algorithm is tested and compared with the basic bacterial foraging algorithm, genetic algorithm, and particle swarm optimization algorithm. The experimental results show that the optimized bacterial foraging algorithm can achieve better convergence accuracy and shorter convergence speed for the objective function, and it can solve the scheduling optimization problem of knowledge workers more quickly, accurately, and effectively. The research in this paper shows that the optimization of four aspects of the basic bacterial foraging algorithm improves the performance of the algorithm and provides a theoretical reference for the optimization of the bacterial foraging algorithm. •A bacterial foraging algorithm is proposed.•In this paper, bacterial foraging algorithm is used to build a knowledge-based employee scheduling model.•It can help bacteria improve the quality of reproduction, that is, improve the quality of the algorithm.•It is proved that the optimized bacterial foraging algorithm has better accuracy and efficiency.