系统仿真学报 ›› 2021, Vol. 33 ›› Issue (3): 732-744.doi: 10.16182/j.issn1004731x.joss.19-0576

• 国民经济仿真 • 上一篇    下一篇

考虑服务水平的IT运维人员调度的智能遗传算法

陈芮莹1, 王承涛2, 刘振元1,3   

  1. 1.华中科技大学 人工智能与自动化学院,湖北 武汉 430074;
    2.武汉问道信息技术有限公司,湖北 武汉 430040;
    3.图像信息处理与智能控制教育部重点实验室,湖北 武汉 430074
  • 收稿日期:2019-11-05 修回日期:2020-01-15 出版日期:2021-03-18 发布日期:2021-03-18
  • 作者简介:陈芮莹(1996-),女,硕士生,研究方向为服务系统调度与智能计算。E-mail:475639185@qq.com
  • 基金资助:
    国家自然科学基金(72071087),中央高校基本科研业务费(HUST:2017KFYXJJ178)

Intelligent Genetic Algorithm for Workforce Scheduling Considering Service Level in IT Maintenance Service

Chen Ruiying1, Wang Chengtao2, Liu Zhenyuan1,3   

  1. 1. School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China;
    2. Wuhan Windoor Information Technology Company. LTD, Wuhan 430040, China;
    3. Key Laboratory of Education Ministry for Image Processing and Intelligent Control, Wuhan 430074, China
  • Received:2019-11-05 Revised:2020-01-15 Online:2021-03-18 Published:2021-03-18

摘要: 在人力资源受限的情况下,针对如何在IT运维服务中使用有限的人力资源,迅速、有效地对发生的故障进行处理,建立了考虑服务水平的IT运维人员调度模型。提出了一种知识模型,设计了多种符合本问题特性的变异、交叉算子,从而形成了改进的遗传算法基于知识模型的智能遗传算法基于知识模型的自适应智能遗传算法。结果表明,自适应变异、交叉概率能加快解的收敛速度,知识模型能加快解的收敛速度并且提高解的优化效果。

关键词: 人员调度, IT运维服务, 服务水平, 智能遗传算法, 知识模型

Abstract: Aiming at resolving the faults quickly and effectively with limited human resource,an IT maintenance service model with the consideration of service level is proposed, A knowledge model is proposed,a variety of mutation operators and crossover operators are designed. and the improved genetic algorithm (IGA), the intelligent genetic algorithm based on knowledge model (KIGA) and the adaptive intelligent genetic algorithm based on knowledge model (KAIGA) are formed. The results show that the adaptive mutation and crossover probability can accelerate the convergence speed of the solution, and the knowledge model can also improve the optimization effect of the solution

Key words: workforce scheduling, IT maintenance service, service level, intelligent genetic algorithm, knowledge model

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