多目标粒子群算法在交叉培训规划中的应用

Multi-objective particle swarm optimization algorithm for cross-training programming

中文关键词  交叉培训规划  员工满意度  学习效率曲线  多目标粒子群算法  柔性单元装配线

英文关键词  cross-training programming  labor satisfaction  learning curve  multi-objective PSO  flexible assembly cells

中文摘要

      为了进一步提高人力资源交叉培训规划的实用性, 增加了对于员工学习行为的考虑, 提出了在保证任务覆盖水平的基础上, 获得员工满意度最大和学习效率最高的多目标优化模型. 本文针对问题的特征, 采用多目标粒子群(MOPSO)算法对多目标优化模型进行了求解, 并设计了多种算法策略, 以适应不同的问题环境. 通过数值实验, 分析了不同问题规模下, 针对不同性能指标算法参数和策略的适用性. 最后, 以柔性单元装配生产线为例, 进行了数值实验, 实验结果表明了模型的有效性和合理性.

英文摘要

      In order to improve the practicability of a cross-training programming, the factor of human learning behavior is considered. A multi-objective optimization model is presented on the basis of task redundancy policy, in which the objective functions describe the labor satisfaction and the learning efficiency. A cross-training programming based on multi-objective particle swarm optimization algorithm (MOPSO) is proposed. The MOPSO solves for the solutions of the proposed multi-objective optimization model and designs algorithm policies for different problem environments. Several flexible cell assemblies in different scales are presented for modeling the environment in a series of numerical experiments. Results in each environment are analyzed in the aspects of diversity, distribution and convergence index. The analyzed results show that the method presented in this paper can solve cross-training programming problems effectively.