基于TSAPO的柔性作业车间计划和调度

摘要

To make the multi-objective flexible job shop planning and scheduling more accord with the dynamic changing. Flexible Job Shop(FJS) planning model with dynamic feedback and Two Stages Ant Particle Optimization(TSAPO) algorithm are proposed. The update and feedback of practical product data are realized by dynamic monitoring. Through the decomposition of optimization objects by two stages, scheduling algorithm with feedback is designed. Experimental result shows the algorithm has better optimization effect in solving multi-objective flexible job shop scheduling problem.%为使多目标柔性作业车间计划与调度的制定更适合实际生产的动态变化,提出增加动态反馈的闭环柔性作业车间计划模型及二阶式蚁群粒子群混合优化算法TSAPO.通过增加动态监视功能,及时更新和反馈实际生产数据.利用对优化目标的二阶段分解,设计带有反馈机制的调度算法.实验结果证明,该算法在求解多目标柔性作业车间调度问题中具有较好的优化效果.