考虑光伏辅助供能的柔性作业车间调度

Flexible Job Shop Scheduling Considering Photovoltaic Auxiliary Energy Supply

本文针对柔性作业车间调度,引入光伏辅助供能,构建一种光伏和普通电网交替供电的柔性作业车间调度模型,提出一种多学习对象的蛙跳算法(multi-learning-object shuffled frog leaping algorithm,MLOSFLA),以同时优化碳排放和最小化最大完成时间。此算法提出一种新的学习对象选择方法,使模因组内的最差解的学习对象多样化,避免算法过早陷入局部最优。通过实例验证MLO-SFLA解决此问题的有效性,根据实验结果,MLO-SFLA对于所研究的考虑光伏辅助供能的柔性作业车间调度问题具有较明显的优势。

In view of flexible job shop scheduling,photovoltaic auxiliary energy supply is introduced to build a flexible job shop scheduling model with alternating power supply between photovoltaic and ordinary power grid.A multi-learning-object shuffled frog leaping algorithm(MLO-SFLA)was proposed to optimize carbon emissions and minimize maximum completion time.In this algorithm,a new learning object selection method is proposed to diversify the learning objects of the worst solution in the meme group and avoid the algorithm falling into local optimum prematurely.An example is given to verify the effectiveness of MLO-SFLA in solving this problem.According to the experimental results,MLO-SFLA has obvious advantages for the studied flexible job shop sched‐uling problem with photovoltaic assisted energy supply.