摘要
分布式调度是智能制造的新模式,急需新的调度方法来应对动态多变的市场需求。针对分布式置换流水车间问题,采用逆调度方法优化,通过最小调整加工参数,使得尽可能保证原排序的情况下调度最优。以最小化调整加工时间为目标,建立流水车间逆调度数学模型,针对逆调度问题特征,在遗传算法的框架下提出一种混合遗传优化算法。首先,基于逆调度参数可调的特征,提出基于工序的小数机制双层编码方案,能够实现参数的调整,保证可能解;提出改进启发式方法和基于规则的方法相结合的混合初始化方法;其次,采用适合问题特征的交叉、变异操作执行搜索;为协调全局搜索与局部搜索能力,设计局部搜索策略和学习机制的双种群协同搜索策略。为验证算法性能,基于问题实例采用三种算法进行比较,并且进行统计分析,其结果表明所提算法能更有效求解分布式流水线逆调度问题。
Distributed scheduling is a new mode of intelligent manufacturing,which is in urgent need of new scheduling methods to meet the Dynamic and changeable market demand.To solve the distributed permutation flow shop problem,the inverse scheduling method is used to optimize the job shop scheduling by minimizing the processing parameters.Aiming at minimizing the adjusted processing time,a mathematical model of flow shop reverse scheduling is established,and a hybrid genetic optimization Algorithm is proposed under the framework of genetic algorithm.Firstly,based on the characteristics of the inverse scheduling parameters,an improved operation-based decimal mechanism double coding scheme is proposed,which can adjust the parameters and ensure the possible solution.Secondly,a hybrid initialization method is adopted by improving the NEH heuristic method and the rule-based method,in order to coordinate the ability of global search and local search,the local search strategy and the double-population cooperative search strategy with learning mechanism are designed.In order to verify the performance of the proposed algorithm,three algorithms are compared and analyzed based on the problem examples.The results show that the proposed algorithm can solve the distributed pipeline inverse scheduling problem more effectively.
作者
牟健慧
段培永
高亮
彭武良
丛建臣
MU Jianhui;Duan Peiyong;GAO Liang;Peng Wuliang;Cong Jianchen(School of Mechatronics and Automotive Engineering,Yantai University,Yantai 264005;School of Computer and Control Engineering,Yantai University,Yantai 264005;School of Mechanical Engineering and Science,Huazhong University of Science and Technology,Wuhan 430074;School of Economics and Management,Yantai Universty,Yantai 264005;School of Mechanical Engineering,Shandong University of Technology,Zibo 255001)
出处
《机械工程学报》
EI
CAS
CSCD
北大核心
2022年第6期295-308,共14页
Journal of Mechanical Engineering
基金
国家自然科学基金(52175487,62073201)
山东省自然科学基金(ZR2021ME223,ZR2019MEE093)
山东重点研发(2019JZZY010445)
烟台市科技创新发展计划(2022GCCRC158)
烟台市科技计划(2021XDHZ077)资助项目。
关键词
分布式调度
逆调度
流水车间调度
混合遗传算法
种群协同
distributed scheduling
inverse scheduling
flow shop scheduling
hybrid genetic algorithm
population coordination
