计算机集成制造系统 ›› 2022, Vol. 28 ›› Issue (1): 161-174.DOI: 10.13196/j.cims.2022.01.016

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考虑团队交互学习效应的单工作组可中断任务调度模型

李斌,谢乃明+   

  1. 南京航空航天大学经济与管理学院
  • 出版日期:2022-01-31 发布日期:2022-02-12
  • 基金资助:
    国家自然科学基金资助项目(71671090);中央高校基本科研业务费资助项目(NP2020022);江苏省“青蓝工程”中青年学术带头人计划资助项目。

Single workgroup interruptible task scheduling model considering team interactive learning effect

  • Online:2022-01-31 Published:2022-02-12
  • Supported by:
    Project supported by the National Natural Science Foundation,China(No.71671090),the Fundamental Research Funds for the Central Universities,China(No.NP2020022),and the Qinglan Project for Excellent Youth or Middle-Aged Academic Leaders in Jiangsu Province,China.

摘要: 工作组完成工作任务时团队成员间存在交互学习效应,考虑工作组规模、人员结构等因素可以更好地配置资源实现优化调度。基于标准加工时间和单人学习效应定义了团队交互学习效应,给出了基于团队交互学习效应和人员加工可中断约束的工时表征公式,构建了以最大完工时间最小为目标的单工作组可中断任务调度模型,设计了基于精英保留遗传算法(e-GA)的模型求解策略,并设计了不同规模、不同人员结构的测试算例验证所构建模型和算法的有效性。实验结果表明,所构建的模型和算法能够实现优化调度。

关键词: 团队交互学习效应, 单工作组调度, 人员加工可中断, 精英保留遗传算法

Abstract: There is an interactive learning effect among team members when the workgroup completes its work tasks.Considering the size of the workgroup,personnel structure and other factors,resources can be better allocated to achieve optimal scheduling.Based on the standard processing time and the worker learning effect,the team interactive learning effect was researched,a work-hour representation formula based on team interactive learning effect and the interruptible personnel processing constraint was given,and a single workgroup interruptible task scheduling model was constructed with the maximum completion time as the goal.For the task scheduling model,a solving strategy based on e-GA was designed,and test cases of different scales and different personnel structures were designed to verify the effectiveness of the constructed model and algorithm.Experimental results showed that the constructed model and algorithm could achieve optimal scheduling.

Key words: team interactive learning effect, single workgroup scheduling, interruptible personnel processing, elitist genetic algorithm

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