东北大学学报（自然科学版）

东北大学学报(自然科学版) ›› 2011, Vol. 32 ›› Issue (12): 1696-1699.DOI: -

• 论著 • 上一篇    下一篇

基于改进NSGA-Ⅱ的交叉培训规划多目标优化

李倩;宫俊;唐加福;   

  1. 东北大学流程工业综合自动化国家重点实验室;
  • 收稿日期:2013-06-19 修回日期:2013-06-19 发布日期:2013-04-04
  • 通讯作者: -
  • 作者简介:-
  • 基金资助:
    国家自然科学基金资助项目(70971019);;

Multi-objective optimal cross-training plan models using non-dominated sorting genetic algorithm-II

Li, Qian (1); Gong, Jun (1); Tang, Jia-Fu (1)   

  1. (1) State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819, China
  • Received:2013-06-19 Revised:2013-06-19 Published:2013-04-04
  • Contact: Gong, J.
  • About author:-
  • Supported by:
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摘要： 针对柔性制造单元的员工交叉培训规划问题,从人性化和经济效益的角度考虑,提出了将多能工水平和任务覆盖水平等培训策略作为约束条件,以培训员工平均满意度最大化和任务平均支付工资最小化为目标的多目标优化方法.针对多目标优化模型,采用了非支配排序遗传算法(NSGA-Ⅱ)求解,并采用了Pareto解集过滤器技术.实验结果表明,改进的算法在一定程度上提高了运算效率和改善了Pareto解的多样性.

关键词: 交叉培训, 单元装配线, 员工满意度, 多目标优化, 非支配排序遗传算法

Abstract: To solve the problems of a cross-training plan for staffs in a flexible assembly cell from the point of views of humanization and economy, a multi-objective optimal cross-training plan is proposed. Average labor satisfaction and average task payment are chosen as the goals of the model, and the multi-functionality and task coverage policies are used as the constraints. To solve the multi-objective optimal model, the non-dominated sorting genetic algorithm-II based on double terminated rules and Pareto filter techniques is proposed. Simulation results show that this algorithm improves the operation efficiency and diversity of the Pareto solutions.

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