求解无等待Flow shop调度问题的改进蛙跳算法  

Improved Frog Leaping Algorithm for Solving No-waiting Flow Shop Scheduling Problem

作　　者：吴永明[1] 王亚昆 张晗[1] 赵旭东 陈琳升 史海鹏 WU Yong-ming;WANG Ya-kun;ZHANG Han;ZHAO Xu-dong;CHEN Lin-sheng;SHI Hai-peng(Key Laboratory of Advanced Manufacturing Technology,Ministry of Education,Guizhou University,Guiyang 550025,China)

机构地区：[1]贵州大学现代制造技术教育部重点实验室,贵阳550025

出　　处：《组合机床与自动化加工技术》2020年第7期81-84,共4页Modular Machine Tool & Automatic Manufacturing Technique

基　　金：国家自然科学基金资助项目(51505094);贵州省科技支撑计划项目(黔科合支撑[2017]2029);贵州省科技计划项目(黔科合基础[2016]1037);贵州省应用基础研究计划重大项目子课题(黔科合JZ字(2014)2001);贵州大学引进人才科研项目(贵大人基合字(2014)60)。

摘　　要：无等待Flow shop调度一直是研究的热点问题,为减少无等待流水车间调度总完工时间,文章提出了一种结合高斯变异和扰动因子的蛙跳算法。在蛙群更新策略中引入扰动因子,平衡了蛙跳算法搜索的深度,利用高斯变异产生子代新蛙代替随机产生子代新蛙,增强了蛙跳算法的寻优速度;同时引入交叉操作,增加蛙群的多样性,提高蛙群中所有青蛙的局部搜索能力以及全局搜索能力。研究了无等待Flow shop调度算例,将蛙跳算法与遗传算法的求解结果进行了对比,结果验证了该文改进蛙跳算法在解决无等待流水车间调度问题的有效性和可行性。The no-waiting Flow shop scheduling has always been a hot issue. In this paper, a frog leaping algorithm combining Gaussian variability and disturbance factor is proposed to reduce the total completion time of the no-waiting flow shop. In the frog group update strategy, the disturbance factor is introduced to balance the depth of the leapfrog algorithm search. The Gaussian mutation is used to generate the new generation of the frog instead of the random generation of the new frog, which enhances the speed of the leapfrog algorithm. Crossover operation is introduced, which increases the frog group diversity, the local search ability and global search ability of all frogs in the frog group. The no-waiting Flow shop scheduling example is studied. The result obtained by the leapfrog algorithm is compared with that obtained by the genetic algorithm, which verifies the effectiveness and feasibility of the improved leapfrog algorithm in solving the problem of no-wait flow shop scheduling.

关 键 词：Flow shop 蛙跳算法 调度 

分 类 号：TH162[机械工程—机械制造及自动化] TG506[金属学及工艺—金属切削加工及机床]