[1]王 雷,邹 新.基于改进免疫克隆选择算法的柔性作业车间调度[J].南京理工大学学报(自然科学版),2018,42(03):345.[doi:10.14177/j.cnki.32-1397n.2018.42.03.013]
 Wang Lei,Zou Xin.Flexible job-shop scheduling based on improved immune clone selection algorithm[J].Journal of Nanjing University of Science and Technology,2018,42(03):345.[doi:10.14177/j.cnki.32-1397n.2018.42.03.013]
点击复制

基于改进免疫克隆选择算法的柔性作业车间调度
分享到:

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

卷:
42卷
期数:
2018年03期
页码:
345
栏目:
出版日期:
2018-06-30

文章信息/Info

Title:
Flexible job-shop scheduling based on improved immune clone selection algorithm
文章编号:
1005-9830(2018)03-0345-07
作者:
王 雷邹 新
安徽工程大学 机械与汽车工程学院,安徽 芜湖 241000
Author(s):
Wang LeiZou Xin
School of Mechanical and Automotive Engineering,Anhui Polytechnic University,Wuhu 241000,China
关键词:
免疫克隆选择算法 柔性作业车间调度 自适应变异 种群分割
Keywords:
immune clone selection algorithm flexible job-shop scheduling adaptive mutation population decomposition
分类号:
TP278; TP301.6
DOI:
10.14177/j.cnki.32-1397n.2018.42.03.013
摘要:
针对柔性作业车间调度问题,以最小化完工时间为优化目标,提出了1种改进的免疫克隆选择算法。建立了柔性作业车间的调度模型。在初始化种群方面采用多种策略以提高种群的初始质量。构造了自适应变异算子。针对标准免疫算法的缺陷,利用种群分割的思想使其具有多样性,提高全局搜索能力。对6工件10机器的标准测试实例进行仿真,利用遗传算法、模拟退火算法、免疫算法求得的完工时间优化结果分别是47 s、48 s和50 s,利用该文算法求得的完工时间优化结果是45 s,该文算法得到最优解的概率为75%。
Abstract:
An improved immune clone selection algorithm is proposed to minimize the makespan for flexible job-shop scheduling problem(FJSP). A flexible job-shop scheduling model is built. Several strategies are used to generate new antibodies in order to improve the quality of initial antibodies. An adaptive mutation operation is constructed. In light of the shortcomings of the standard immune algorithm,the diversity of population is kept by population decomposition in order to improve the ability of global search. A standard test case with 6 workpieces and 10 machines is simulated,the optimized results of makespan obtained by genetic algorithm,simulated annealing algorithm and immune algorithm are 47 s,48 s and 50 s respectively,and the result obtained by this improved algorithm is 45 s; the probability of getting the optimum solution of this improved algorithm is 75%.

参考文献/References:

[1] 高亮,张国辉,王晓娟. 柔性作业车间调度智能算法及其应用[M]. 武汉:华中科技大学出版社,2012.
[2]张超勇,董星,王晓娟,等. 基于改进非支配排序遗传算法的多目标柔性作业车间调度[J]. 机械工程学报,2010,46(11):156-164.
Zhang Chaoyong,Dong Xing,Wang Xiaojuan,et al. Improved NSGA-Ⅱ for the multi-objective flexible job-shop scheduling problem[J]. Journal of Mechanical Engineering,2010,46(11):156-164.
[3]Roshanaeia V,Naderia B,Jolaib F,et al. A variable neighborhood search for job shop scheduling with set-up times to minimize makespan[J]. Future Generation Computer Systems,2009,25(6):654-661.
[4]Qiao Lihong,Lv Shengping. An improved genetic algorithm for integrated process planning and scheduling[J]. The International Journal of Advanced Manufacturing Technology,2012,58(5):727-740.
[5]Pezzella F,Morganti G,Ciaschetti G. A genetic algorithm for the flexible job-shop scheduling problem[J]. Computers & Operations Research,2008,35(10):3202-3212.
[6]信宁宁,黄宗南. 基于最短处理时间疫苗的免疫遗传算法优FJSP问题[J]. 机械设计与研究,2013,29(3):53-55.
Xin Ningning,Huang Zongnan. Optimal FJSP problem by using immune genetic algorithm based on the shortest processing time vaccine[J]. Machine Design & Research,2013,29(3):53-55.
[7]左益,公茂果,曾久琳,等. 混合多目标算法用于柔性作业车间调度问题[J]. 计算机科学,2015,42(9):220-225.
Zuo Yi,Gong Maoguo,Zeng Jiulin,et al. Hybrid multi-objective algorithm for flexible job shop scheduling problem[J]. Computer Science,2015,42(9):220-225.
[8]马佳,石刚. 基于改进人工免疫算法的柔性车间调度问题[J]. 计算机仿真,2014,31(12):375-379.
Ma Jia,Shi Gang. Flexible job shop scheduling problem based on improved artificial immune algorithm[J]. Computer Simulation,2014,31(12):375-379.
[9]汤洪涛,丁彬楚,李修琳,等. 基于改进免疫遗传算法的混合车间调度研究[J]. 中国机械工程,2014,25(9):1189-1194.
Tang Hongtao,Ding Binchu,Li Xiulin,et al. Improved immune genetic algorithm for mixed-model scheduling problem[J]. China Mechnical Engineering,2014,25(9):1189-1194.
[10]庞留勇,曹炬,张燕. 基于动态疫苗库的免疫遗传算法解决车间调度问题[J]. 计算机工程与科学,2010,32(2):124-127.
Pang Liuyong,Cao Ju,Zhang Yan,et al. Solving the workshop scheduling problem using the immune genetic algorithm based on dynamic vaccine pools[J]. Computer Engineering & Science,2010,32(2):124-127.
[11]彭建刚,刘明周,张铭鑫,等. 基于改进非支配排序的云模型进化多目标柔性作业车间调度[J]. 机械工程学报,2014,50(12):198-205.
Peng Jiangang,Liu Mingzhou,Zhang Mingxin,et al. Cloud model evolutionary multi-objective flexible job-shop scheduling based on improved non-dominated sorting[J]. Journal of Mechanical Engineering,2014,50(12):198-205.
[12]赵诗奎,方水良. 基于工序编码和邻域搜索策略的遗传算法优化作业车间调度[J]. 机械工程学报,2013,49(16):160-169.
Zhao Shikui,Fang Shuiliang. Operation-based encoding and neighborhood search genetic algorithm for job shop scheduling optimization[J]. Journal of Mechanical Engineering,2013,49(16):160-169.
[13]刘琼,张超勇,饶运清,等. 改进遗传算法解决柔性作业车间调度问题[J]. 工业工程与管理,2009,14(2):59-66.
Liu Qiong,Zhang Chaoyong,Rao Yunqing,et al. Flexible job-shop scheduling problem using improved genetic algorithm[J]. Industrial Engineering and Management,2009,14(2):59-66.
[14]赵小强,何浩. 一种求解柔性作业车间调度问题的改进DRSGA[J]. 南京理工大学学报,2016,40(3):297-302.
Zhao Xiaoqiang,He Hao. Improved DRSGA for flexible job shop scheduling[J]. Journal of Nanjing University of Science and Technology,2016,40(3):297-302.
[15]Li Xinyu,Gao Liang. An effective hybrid genetic algorithm and tabu search for flexible job shop scheduling problem[J]. International Journal of Production Economics,2016,174:93-110.
[16]Zhang Guohui,Gao Liang,Shi Yang. An effective genetic algorithm for the flexible job-shop scheduling problem[J]. Expert Systems with Applications,2011,38(4):3563-3573.
[17]黄席樾,向长城,殷礼胜. 现代智能算法理论及应用[M]. 北京:科学出版社,2009.
[18]杨晓梅,曾建潮. 遗传算法求解柔性Job-shop调度问题[J]. 控制与决策,2004,19(10):1197-1200.
Yang Xiaomei,Zeng Jianchao. Genetic algorithm for flexible job-shop scheduling problem[J]. Control and Decision,2004,19(10):1197-1200.
[19]Nasr N,Elsayed E A. Job shop scheduling with alternative machine[J]. International Journal of Production Research,1990,28(9):1595-1609.
[20]李平,唐秋华,夏绪辉. 基于双层遗传编码的柔性作业车间自适应重调度研究[J]. 中国机械工程,2013,24(16):2195-2201.
Li Ping,Tang Qiuhua,Xia Xuhui. Adaptive rescheduling of flexible job shop based on bilevel genetic coding[J]. China Mechnical Engineering,2013,24(16):2195-2201.
[21]郝倩. 基于改进的混合免疫算法的车间调度问题研究[D]. 大连:大连交通大学软件学院,2014.

相似文献/References:

[1]王 雷,蔡劲草,石 鑫.基于改进遗传算法的多目标柔性作业车间节能调度问题[J].南京理工大学学报(自然科学版),2017,41(04):494.[doi:10.14177/j.cnki.32-1397n.2017.41.04.015]
 Wang Lei,Cai Jingcao,Shi Xin.Multi-objective flexible job shop energy-saving scheduling problem based on improved genetic algorithm[J].Journal of Nanjing University of Science and Technology,2017,41(03):494.[doi:10.14177/j.cnki.32-1397n.2017.41.04.015]

备注/Memo

备注/Memo:
收稿日期:2017-05-23 修回日期:2017-07-19
基金项目:安徽省自然科学基金(1708085ME129); 安徽省科技攻关项目(1604a0902183)
作者简介:王雷(1982-),男,博士,副教授,主要研究方向:智能优化算法、作业车间调度,E-mail:wangdalei2000@126.com。
引文格式:王雷,邹新. 基于改进免疫克隆选择算法的柔性作业车间调度[J]. 南京理工大学学报,2018,42(3):345-351.
投稿网址:http://zrxuebao.njust.edu.cn
更新日期/Last Update: 2018-06-30