系统仿真学报 ›› 2019, Vol. 31 ›› Issue (12): 2626-2635.doi: 10.16182/j.issn1004731x.joss.19-FZ0275

• 仿真建模理论与方法 • 上一篇    下一篇

云制造系统并行任务优化调度

冯晨微, 王艳   

  1. 江南大学,教育部物联网技术应用工程中心,江苏 无锡 214122
  • 收稿日期:2019-03-08 修回日期:2019-07-02 发布日期:2019-12-13
  • 作者简介:冯晨微(1995-),男,湖北潜江,硕士生,研究方向为云制造系统优化调度;王艳(1978-),女,江苏盐城,博士,教授,研究方向为智能制造系统能效优化。
  • 基金资助:
    国家自然科学基金(61973138)

Parallel Tasks Optimization Scheduling in Cloud Manufacturing System

Feng Chenwei, Wang Yan   

  1. Engineering Research Center of Internet of Things Technology Applications Ministry of Education, Jiangnan University, Wuxi 214122, China
  • Received:2019-03-08 Revised:2019-07-02 Published:2019-12-13

摘要: 为解决同类型任务在云制造系统中并行执行时资源需求不均衡以及资源的利用率不高的问题。建立了以成本最低化、时间最小化、可靠度最高化、质量最优化为目标的任务资源调度模型。采用基于参考点的非支配排序遗传算法(NSGA-Ⅲ),结合实数矩阵编码方式以及基于实数编码的交叉变异策略代替普通的进化策略对模型进行求解,使用基于层次分析法和熵值法的组合优化决策方法对结果进行评价。分别讨论了资源充足和资源受限时调度系统的性能,通过实例证明该方法是可行的。

关键词: 云制造, 并行任务, 虚拟资源, 优化调度, NSGA-Ⅲ

Abstract: To solve the problem of unbalanced resource requirements and low resource utilization when the same type of tasks are executed in parallel in the cloud manufacturing system, a task resource scheduling model with the goal of minimizing cost, minimizing time, maximizing reliability and optimizing quality is established. A non-dominated sorting genetic algorithm based on reference points (NSGA-III) is adopted to solve the model by combining real number matrix coding and crossover and mutation based on real number coding instead of common evolutionary strategy. And an optimal decision strategy based on combination of analytic hierarchy process and entropy value method is used to evaluate the solutions. The performance of the scheduling system with sufficient and limited resources is discussed respectively. The example proves that method is feasible.

Key words: cloud manufacturing, parallel tasks, virtual resource, optimal scheduling, NSGA-Ⅲ

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