基于多班教学优化的多目标分布式混合流水车间调度  

Multi-class teaching-learning-based optimization for multi-objective distributed hybrid flow shop scheduling

作　　者：雷德明[1] 苏斌 LEI De-ming;SU Bin(School of Automation,Wuhan University of Technology,Wuhan 430070,China)

机构地区：[1]武汉理工大学自动化学院,武汉430070

出　　处：《控制与决策》2021年第2期303-313,共11页Control and Decision

基　　金：国家自然科学基金项目(61573264)。

摘　　要：单工厂环境下的混合流水车间调度问题已受到广泛关注,而多工厂环境下的分布式混合流水车间调度问题(distributed hybrid flow shop scheduling problem,DHFSP)研究进展则较小.针对考虑顺序相关准备时间的DHFSP,提出一种多班教学优化(multi-class teaching-learning-based optimization,MTLBO)算法以同时最小化最大完成时间和最大延迟时间.该算法采用双串编码方式,将种群划分成s个班级,每个班级的进化都由两个教师阶段和一个学生阶段组成;引入一种班级质量评价方式,实现奖惩机制和淘汰过程.通过大量实验测试MTLBO的性能,计算结果表明,MTLBO对于所求解的DHFSP具有较强优势.Hybrid flow shop scheduling problems have been extensively addressed in single factory,however,distributed hybrid flow shop scheduling problems(DHFSP)are seldom investigated in multiple factories.In this study,the DHFSP with sequence-dependent setup times is studied and a multi-class teaching-learning-based optimization(MTLBO)algorithm is proposed to minimize makespan and maximum tardiness simultaneously.A two-string representation is adopted.The s classes are formed and the evolution of each class consists of two teacher phases and one learner phase.Class evaluation is introduced,a reward and punishment mechanism is executed on classes,and elimination process is also applied.A number of experiments are conducted and the computational results demonstrate that the MTLBO is very competitive for the DHFSP.

关 键 词：混合流水车间调度 分布式调度 多班教学优化 多目标优化 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]