基于多小组协同学习教学算法的车间作业调度问题  

Teaching-learning-based optimization algorithm with group collaboration for job shop scheduling problem

作　　者：张梅[1] 杨晟轩 朱金辉 ZHANG Mei;YANG Sheng-xuan;ZHU Jin-hui(College of Automatic Science and Engineering,South China University of Technology,Guangzhou 510641,China;College of Software,South China University of Technology,Guangzhou 510641,China)

机构地区：[1]华南理工大学自动化科学与工程学院,广州510641 [2]华南理工大学软件学院,广州510641

出　　处：《控制与决策》2018年第8期1354-1362,共9页Control and Decision

基　　金：广州市科技计划项目(201707010437);中央高校基本科研业务费专项资金项目(2015zz100);广东省科技计划项目(2014A010104004)

摘　　要：为求解车间作业调度问题(JSSP),提出一种新颖的多小组协同学习的教学算法,实现小组间学习的协同及基于学习能力的深度和广度搜索策略.针对JSSP问题因其复杂度较高容易导致算法陷入局部最优的不足,引入学习小组协同学习,通过组内学习和组内交流,使学习过程跳出当前的局限.为了兼顾局部和全局搜索能力,引入基于学习能力的深度和广度搜索策略,小组内学生按照学习能力强弱进行学习,较优的学生进行深度的学习,较差的学生进行广度的学习.最后,对OR-Library中的标准仿真实例进行实验,结果表明,所提出的教学算法在JSSP问题上的收敛精度和搜索能力较其他算法均得到了有效的提高.This paper presents a novel teaching-learning-based optimization（TLBO） algorithm with group collaboration for the job shop scheduling problem（JSSP）. Firstly, a collaborative learning strategy between groups is introduced to avoid being trapped into local optimum when solving the JSSP with high complexity. After learning within the group, individuals would communicate and collaborate with individuals of other groups so that they can jump out of the limitation of the current learning process. Then, in order to balance the local and global searching ability, a depth or width searching strategy is introduced, which enables individuals to learn in different ways according to their learning ability. For individuals with stronger learning ability, the depth learning will be adopted, and for individuals with weaker learning ability, the width learning will be adopted, so that different individuals can play different roles in the learning process. Experimental results on benchmark instances of OR-Library show that the search ability and convergence accuracy can been effectively improved in solving the JSSP.

关 键 词：小组学习 教学优化算法 协同进化 车间作业调度 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]