改进遗传算法解决带有机器恶化效应的柔性作业车间调度问题  

Improved Genetic Algorithm for Solving Flexible Job Shop Scheduling Problem with Machine Deterioration Effect

作　　者：安璐 张鹏[2] 聂宇晨 AN Lu;ZHANG Peng;NIE Yuchen(Software Institute,Dalian Jiaotong University,Dalian 116054,China;Innovation Entrepreneurship Institute of Education,Dalian Jiaotong University,Dalian 116028,China)

机构地区：[1]大连交通大学软件学院,辽宁大连116054 [2]大连交通大学创新创业教育学院,辽宁大连116028

出　　处：《大连交通大学学报》2020年第6期112-116,共5页Journal of Dalian Jiaotong University

基　　金：辽宁省自然科学基金资助项目(20180550499,2019-ZD-0109);辽宁省教育厅科学研究计划资助项目(JDL2019022)。

摘　　要：针对考虑了带有机器恶化效应的多目标柔性作业车间调度,提出了一种改进云自适应遗传退火算法(IGSA),针对遗传算法局部搜索能力差、易早熟收敛等缺点,对适应度计算、交叉操作等进行改进.适应度计算结合模拟退火算法,利用其局部搜索能力强和概率突跳性跳出局部最优解;交叉操作加入海明相似度,进行相似度计算,增强了算法的运行效率,随机产生标准位置和交叉位置进行改进并加入云模型改进交叉概率,增强了算法的全局搜索能力.通过仿真实验进行对比测试,验证了该算法对于集成型多目标车间调度算法的有效性.Aiming at the multi-objective flexible job shop scheduling with machine deterioration effect,an improved cloud adaptive genetic annealing algorithm is proposed,which is improved for the local search ability of the genetic algorithm with easy premature convergence and improved fitness calculation.The fitness calculation combined with the simulated annealing algorithm uses its local search ability and probability jump to jump out of the local optimal solution.The cross operation adds the Hamming similarity,performs the similarity calculation,enhances the operating efficiency of the algorithm and randomly generates the standard position.The cross-position is improved,and the cloud model is added to improve the crossover probability,which enhances the global search capability of the algorithm.The simulation experiments are conducted to verify the effectiveness of the algorithm for the integrated multi-objective shop scheduling algorithm.

关 键 词：改进遗传算法 云模型 机器恶化效应 海明相似度 

分 类 号：TH165[机械工程—机械制造及自动化] TP18[自动化与计算机技术—控制理论与控制工程]