摘要
针对可重入混合流水车间调度问题(Reentrant Hybrid Flow Shop Scheduling Problem,RHFSP),提出一种基于新型优化机理的教学优化(Teaching-Learning-Based Optimization,TLBO)算法以最小化最大完成时间,该算法将学生分成好学生和差学生,主要步骤为教师阶段和学生阶段,其中,教师阶段包括教师的自学和交互学习,学生阶段包括学生接受教师的教学、好学生相互学习和差学生的强化学习。运用多邻域搜索实现教师的自学,其他阶段都通过全局搜索来实现。取消差学生的相互学习以避免低效率搜索。大量的实验结果表明,新型TLBO是解决RHFSP的一种有效方法。
To solve reentrant hybrid flow shop scheduling problem(RHFSP), a new teaching-learning-based optimization(TLBO) algorithm with new optimization mechanism is presented for minimizing makespan, in which students are divided into good ones and bad ones. The main steps of TLBO are teacher stage and student stage. Teacher stage includes the self-learning and interactive learning of teachers and student stage includes the learning from teacher, interactive learning of good students and the reinforced learning of bad students. The self-learning of teachers is implemented using multiple neighborhood search and global search is used to implement other actives of teachers and students. The interactive learning of bad students is deleted to avoid the low-efficiency search. The extensive experiments are done and the results show that the new TLBO is an effective method for RHFSP.
作者
许智伟
吕聪
雷德明
XU Zhi-wei;LV Cong;LEI De-ming(School of Automation,Wuhan University of Technology,Wuhan 430070,China)
出处
《控制工程》
CSCD
北大核心
2020年第10期1812-1819,共8页
Control Engineering of China
基金
国家自然科学基金项目(61573264)。
关键词
混合流水车间调度
可重入
教学优化算法
Hybrid flow shop scheduling
reentrant
teaching-learning-based optimization algorithm
