摘要
针对分布式置换流水车间调度问题,考虑加工完成后的工件交付运输时间影响,以最小化最大工件交付时间为目标,建立整个工件加工与运输调度的数学模型,分析问题特点并设计了改进的灰狼优化算法。采用基于工件加工位置的随机键编码机制,在优化过程中加入局部搜索算子,平衡算法的全局探索与局部开发最优解的能力。大量算例的求解结果表明,文中改进灰狼算法与传统的遗传算法相比具有明显的优越性。
To solve scheduling problem in distributed permutation flow shop,considering the influence of the delivery time of the work piece after the completion of processing,a mathematical model of the whole work piece processing and delivery scheduling was established with the aim of minimizing maximal delivery time.By the analysis of model characteristics,an improved gray wolf algorithm was proposed.A random key encoding mechanism was adopted based on the work piece processing order.The local search operator was added to the optimization process to balance the ability of the algorithm between global exploration and local development of the optimal solution.A large number of test results show that the improved gray wolf algorithm is significantly superior to the traditional genetic algorithm.
作者
夏霖辉
吴瑶
周学良
王海林
Xia Linhui;Wu Yao;Zhou Xueliang;Wang Hailin(School of Mechanical Engineering,Hubei University of Automotive Technology,Shiyan 442002,China)
出处
《湖北汽车工业学院学报》
2022年第4期68-72,80,共6页
Journal of Hubei University Of Automotive Technology
基金
国家自然科学基金(52075107)
中国博士后科学基金(2018M6409120)
湖北省教育厅社科青年项目(19Q130)
湖北汽车工业学院博士科研启动基金(BK201801,BK201601)。
关键词
分布式调度
置换流水车间调度
运输时间
灰狼算法
distributed scheduling
permutation flow shop scheduling
delivery time
gray wolf algorithm
