求解柔性作业车间调度的遗传算法综述  

Survey on genetic algorithms for solving flexible job-shop scheduling problem

作　　者：黄学文[1] 陈绍芬 周阗玉 孙宇婷[1] HUANG Xuewen;CHEN Shaofen;ZHOU Tianyu;SUN Yuting(Faculty of Economics and Management, Dalian University of Technology, Dalian 116024, China)

机构地区：[1]大连理工大学经济管理学院,辽宁大连116024

出　　处：《计算机集成制造系统》2022年第2期536-551,共16页Computer Integrated Manufacturing Systems

基　　金：国家科技支撑计划资助项目(2015BAF09B01)。

摘　　要：柔性作业车间调度(FJSP)是一类具有广泛应用背景的调度问题,作为求解FJSP最受欢迎的算法之一,遗传算法引起了广泛关注。针对求解FJSP的遗传算法,特别是5类主要染色体编码方法以及相关的交叉和变异算子进行全面综述,并从编码可行性、编码空间与解空间的映射关系、染色体存储空间、解码复杂性、编码完备性、遗传操作复杂性和遗传操作多样性7个维度综合评价了5类编码方法。结果表明,MSOS-I编码是遗传算法求解FJSP较好的染色体编码方法,其染色体结构简单,并可选用较多类型的交叉和变异算子。Flexible Job-Shop Scheduling Problem(FJSP)is an important scheduling problem with extensive applications.As one of the most popular methods for solving FJSP,Genetic algorithms(GAs)have attracted significant attentions of a number of researchers.A survey of recent works on GAs for solving FJSP was given,especially five main chromosome representations and relevant crossover and mutation operators in GAs.Then seven evaluation criteria including encoding feasibility,mapping relation,memory space,decoding complexity,encoding completeness,the complexity of genetic operation and the diversity of genetic operation were proposed to evaluate the five chromosome representations.

关 键 词：柔性作业车间调度 遗传算法 染色体编码方法 遗传操作 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]