基于改进遗传算法的柔性作业车间调度优化  

Flexible Job Shop Scheduling Optimization Based on Improved Genetic Algorithm

作　　者：周毅君 金健[1] 黄斌 胡宜 张航军 ZHOU Yi-jun;JIN Jian;HUANG Bin;HU Yi;ZHANG Hang-jun(National Nc System Engineering Research Center, Huazhong University of Science & Technology, Wuhan 430074, China)

机构地区：[1]华中科技大学国家数控系统工程技术研究中心,武汉430074

出　　处：《科学技术与工程》2022年第1期259-266,共8页Science Technology and Engineering

基　　金：国家重点研发计划(2018YFB1701183)。

摘　　要：在实际生产中,加工成本愈发成为企业关注的重要因素。对以最小化加工成本与完工时间为目标的柔性车间调度问题进行了研究。首先,根据实际约束构建调度模型,提出改进遗传算法对模型进行求解,引入质量基因段来增强对染色体适应度值的评价,加速淘汰质量差的个体。其次,为了优化求解质量,提出了基于整体负荷最小与局部负荷最小的种群初始化方法,并设计了精确变异机制来维持种群多样性。最后,用标准算例进行测试,相比于其他改进遗传算法,求解速度得到提高,求解质量也得到了提升,验证了此改进遗传算法的有效性。In the actual production process,it has increasingly become an important factor for companies to pay attention to of the processing cost.The flexible job-shop scheduling problem aiming at minimizing processing cost and makespan was studied.Firstly,a scheduling model based on actual constraints was constructed,and the improved genetic algorithm was purposed to solve the model.Quality gene segment was introduced to enhance the evaluation of chromosome fitness values and accelerate the elimination of poor quality chromosomes.Secondly,a population initialization method based on the minimum overall load and the minimum local load was proposed for quality optimization of the chromosomes,and a precise mutation mechanism was designed to maintain the diversity of the population.Finally,the improved genetic algorithm was tested on a set of benchmark instance taken from the literature and compared with other approaches.The results demonstrate that the proposed algorithm outperforms others in terms of convergence speed and accuracy,which verify the effectiveness.

关 键 词：柔性作业车间调度 改进遗传算法 质量基因 优化 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]