2.793

2018影响因子

(CJCR)

  • 中文核心
  • EI
  • 中国科技核心
  • Scopus
  • CSCD
  • 英国科学文摘

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于混合蛙跳和遗传规划的跨单元调度方法

贾凌云 李冬妮 田云娜

贾凌云, 李冬妮, 田云娜. 基于混合蛙跳和遗传规划的跨单元调度方法. 自动化学报, 2015, 41(5): 936-948. doi: 10.16383/j.aas.2015.c140455
引用本文: 贾凌云, 李冬妮, 田云娜. 基于混合蛙跳和遗传规划的跨单元调度方法. 自动化学报, 2015, 41(5): 936-948. doi: 10.16383/j.aas.2015.c140455
JIA Ling-Yun, LI Dong-Ni, TIAN Yun-Na. An Intercell Scheduling Approach Using Shuffled Frog Leaping Algorithm and Genetic Programming. ACTA AUTOMATICA SINICA, 2015, 41(5): 936-948. doi: 10.16383/j.aas.2015.c140455
Citation: JIA Ling-Yun, LI Dong-Ni, TIAN Yun-Na. An Intercell Scheduling Approach Using Shuffled Frog Leaping Algorithm and Genetic Programming. ACTA AUTOMATICA SINICA, 2015, 41(5): 936-948. doi: 10.16383/j.aas.2015.c140455

基于混合蛙跳和遗传规划的跨单元调度方法


DOI: 10.16383/j.aas.2015.c140455
详细信息
    作者简介:

    贾凌云 北京理工大学计算机学院硕士研究生. 主要研究方向为演化计算和生产调度. E-mail: lingyun jia@163.com

    通讯作者: 李冬妮 北京理工大学计算机学院副教授. 主要研究方向为智能优化, 企业计算,物流管理等. E-mail: ldn@bit.edu.cn
  • 基金项目:

    国家自然科学基金(71401014),北京市自然科学基金(4122069)资助

An Intercell Scheduling Approach Using Shuffled Frog Leaping Algorithm and Genetic Programming

More Information
  • Fund Project:

    Supported by National Natural Science Foundation of China (71401014) and Natural Science Foundation of Beijing (4122 069)

  • 摘要: 针对运输能力受限条件下的跨单元问题,提出了一种基于混合蛙跳与遗传规划的超启发式算法.将改进的混合蛙跳算法作为超启发式算法的高层框架,为跨单元调度问题搜索启发式规则,同时利用遗传规划产生可以兼顾多因素的优质规则,用于扩充超启发式算法的规则集.实验表明,提出的算法可以有效地搜索出优异的规则组合,并且通过遗传规划产生的规则可以在很大程度上改善候选规则集,提升算法性能.
  • [1] Rheault M, Drolet J R, Abdulnour G. Dynamic cellular manufacturing system (DCMS). Computers and Industrial Engineering, 1996, 31(1-2): 143-146
    [2] [2] Garza O, Smunt T L. Countering the negative impact of intercell flow in cellular manufacturing. Journal of Operations Management, 1991, 10(1): 92-118
    [3] [3] Khaksar-Haghani F, Kia R, Mahdavi I, Kazemi M. A genetic algorithm for solving a multi-floor layout design model of a cellular manufactur-ing system with alternative process routings and flexible configuration. The International Journal of Advanced Manufacturing Technology, 2013, 66(5-8): 845-865
    [4] [4] Kia R, Baboli A, Javadian N, Tavakkoli-Moghaddam R, Kazemi M, Khorrami J. Solving a group layout design model of a dynamic cellular manufacturing system with alternative process routings, lot splitting and flexible reconfiguration by simulated annealing. Computers and Operations Research, 2012, 39(11): 2642-2658
    [5] [5] Gupta J N D, Schaller J E. Minimizing flow time in a flow-line manufacturing cell with family setup times. Journal of the Operational Research Society, 2006, 57(2): 163-176
    [6] [6] Tsai C H, Li R K. A due-date oriented scheduling heuristic for job shop cellular manufacturing system. International Journal of Industrial Engineering Theory Applications and Practice, 2000, 7(1): 76-88
    [7] [7] Solimanpur M, Elmi A. A tabu search approach for cell scheduling problem with makespan criterion. International Journal of Production Economics, 2013, 141(2): 639-645
    [8] [8] Golmohammadi A, Ghodsi R. Applying an integer Electromagnetism-like algorithm to solve the cellular manufacturing scheduling problem with an integrated approach. In: Proceedings of the 2009 International Conference on Computers and Industrial Engineering. Troyes: IEEE, 2009. 34-39
    [9] [9] Mosbah A B, Dao T M. Optimimization of group scheduling using simulation with the meta-heuristic extended great deluge (EGD) approach. In: Proceedings of the 2010 IEEE International Conference on Industrial Engineering and Engineering Management. Macao: IEEE, 2010. 275-280
    [10] Gholipour K Y, TavakkoliM R, Khorrami A. Solving a multicriteria group scheduling problem for a cellular manufacturing system by scatter search. Journal of the Chinese Institute of Industrial Engineers, 2011, 28(3): 192-205
    [11] Li W L, Murata T. Particle swarm optimization method for rescheduling of job processing against machine breakdowns for nondisruptive cell manufacturing system. In: Proceedings of the 6th International Conference on New Trends in Information Science and Service Science and Data Mining (ISSDM). Taipei, China: IEEE, 2012. 523-528
    [12] Meng X W, Ju Y H, Wang X H, Wang Y, Li D N. An ACO-based approach for intercell scheduling with various types of machines. In: Proceedings of the 25th Chinese Control and Decision Conference (CCDC). Guiyang, China: IEEE, 2013. 1812-1817
    [13] Pajoutan M, Golmohammadi A, Seifbarghy M. CMS scheduling problem considering material handling and routing flexibility. The International Journal of Advanced Manufacturing Technology, 2014, 72(5-8): 881-893
    [14] Li Dong-Ni, Xiao Guang-Xue, Wang Yan, Tang Jia-Fu. An intercell scheduling approach considering flexible processing routes. Acta Automatica Sinica, 2012, 38(6): 969-975(李冬妮, 肖广雪, 王妍, 唐加福. 一种柔性路径下的跨单元调度方法. 自动化学报, 2012, 38(6): 969-975)
    [15] Li D N, Wang Y, Xiao G X, Tang J F. Dynamic parts scheduling in multiple job shop cells considering intercell moves and flexible routes. Computer and Operations Research, 2013, 40(5):1207-1223
    [16] Eusuff M M, Lansey K E. Optimization of water distribution network design using the shuffled frog leaping algorithm. Journal of Water Resources Planning and Management, 2003, 129(3): 210-225
    [17] Xu Y, Wang L, Wang S Y, Liu M. An effective shuffled frog-leaping algorithm for solving the hybrid flow-shop scheduling problem with identical parallel machines. Engineering Optimization, 2013, 45(12): 1409-1430
    [18] Luke S, Panait L, Balan G, Paus S, Skolicki Z, Kicinger R, Popovici E, Sullivan K, Harrison J, Bassett J, Hubley R, Desai A, Chircop A, Compton J, Haddon W, Donnelly S, Jamil B, Zelibor J, Kangas E, Abidi F, Mooers H, O'Beirne J, Talukder K A, McDermott J. ECJ: a java-based evolutionary computation research system. [Online], available: http: //cs.gmu.edu/ eclab/projects/ecj/, June 5, 2014
    [19] Koza J R. Genetic programming as a means for programming computers by natural selection. Statistics and Computing, 1994, 4(2): 87-112
    [20] Robilliard D, Marion-Poty V, Fonlupt C. Genetic programming on graphics processing units. Genetic Programming and Evolvable Machines, 2009, 10(4): 447-471
    [21] Park S C, Raman N, Shaw M J. Adaptive scheduling in dynamic flexible manufacturing systems: a dynamic rule selection approach. IEEE Transactions on Robotics and Automation, 1997, 13(4): 486-502
    [22] Barman S. Simple priority rule combinations: an approach to improve both flow time and tardiness. International Journal of Production Research, 1997, 35(10): 2857-2870
    [23] Laforge R L, Barman S. The performance of simple priority rule combinations in a flow dominant shop. Production and Inventory-Management Journal, 1989, 30(3): 1-4
    [24] Sarper H, Henry M C. Combinatorial evaluation of six dispatchingrules in a dynamic two-machine flow shop. Omega, 1996, 24(1): 73-81
    [25] Li D N, Meng X W, Liang Q Q, Zhao J Q. A heuristic-search genetic algorithm for multi-stage hybrid flow shop scheduling with single processing machines and batch processing machines. Journal of Intelligent Manufacturing, DOI: 10. 1007/s10845-014-0874-y
    [26] Yang T, Kuo Y, Cho C. A genetic algorithms simulation approachfor the multi-attribute combinatorial dispatching decision problem. European Journal of Operational Research, 2007, 176(3): 1859-1873
    [27] Ebrahimi J, Hosseinian S H, Gharehpetian G B. Unit commitment problem solution using shuffled frog leaping algorithm. IEEE Transactions on Power Systems, 2011, 26(2): 573-581
    [28] Rahimi-Vahed A, Mirzaei A H. A hybrid multi-objective shuffled frog-leaping algorithm for a mixed-model assembly line sequencing problem. Computers and Industrial Engineering, 2007, 53(4): 642-666
    [29] Eusuff M, Lansey K, Pasha F. Shuffled frog-leaping algorithm: a memetic metaheuristic for discrete optimization. Engineering Optimization, 2006, 38(2): 129-154
    [30] Teekeng W, Thammano A. A combination of shuffled frog leaping and fuzzy logic for flexible job-shop scheduling problems. Procedia Computer Science, 2011, 6: 69-75
    [31] Luo X H, Ye Y, Li X. Solving TSP with shuffled frog-leaping algorithm. In: Proceedings of the 8th International Conference on Intelligent Systems Design and Applications. Washington, D.C., USA: IEEE, 2008. 228-232
    [32] Glover F, Laguna M, Mart R. Fundamentals of scatter search and path relinking. Control and Cybernetics, 2000, 39(3): 653-684
  • [1] 田云娜, 李冬妮, 刘兆赫, 郑丹. 一种基于动态决策块的超启发式跨单元调度方法[J]. 自动化学报, 2016, 42(4): 524-534. doi: 10.16383/j.aas.2016.c150402
    [2] 刘兆赫, 李冬妮, 王乐衡, 田云娜. 考虑运输能力限制的跨单元调度方法[J]. 自动化学报, 2015, 41(5): 885-898. doi: 10.16383/j.aas.2015.c140498
    [3] 顾鑫, 王士同, 许敏. 基于多源的跨领域数据分类快速新算法[J]. 自动化学报, 2014, 40(3): 531-547. doi: 10.3724/SP.J.1004.2014.00531
    [4] 张浩杰, 龚建伟, 姜岩, 熊光明, 陈慧岩. 基于变维度状态空间的增量启发式路径规划方法研究[J]. 自动化学报, 2013, 39(10): 1602-1610. doi: 10.3724/SP.J.1004.2013.01602
    [5] 徐汉川, 徐晓飞. 考虑资源置信度的跨企业项目鲁棒性调度算法[J]. 自动化学报, 2013, 39(12): 2176-2185. doi: 10.3724/SP.J.1004.2013.02176
    [6] 李冬妮, 肖广雪, 王妍, 唐加福. 一种柔性路径下的跨单元调度方法[J]. 自动化学报, 2012, 38(6): 969-975. doi: 10.3724/SP.J.1004.2012.00969
    [7] 江贺, 邱铁, 胡燕, 李明楚, 罗钟铉. 启发式算法设计中的骨架分析与应用[J]. 自动化学报, 2011, 37(3): 257-269. doi: 10.3724/SP.J.1004.2011.00257
    [8] 张长胜, 孙吉贵, 杨轻云, 郑黎辉. 一种求解车间调度的混合算法[J]. 自动化学报, 2009, 35(3): 332-336. doi: 10.3724/SP.J.1004.2009.00332
    [9] 任子武, 伞冶, 陈俊风. 全局数值寻优的一种混合遗传算法[J]. 自动化学报, 2007, 33(1): 91-96. doi: 10.1360/aas-007-0091
    [10] 张德富, 陈胜达, 刘艳娟. 求解矩形Packing问题的基于遗传算法的启发式递归策略[J]. 自动化学报, 2007, 33(9): 911-916. doi: 10.1360/aas-007-0911
    [11] 杨建军, 刘扬, 魏立新, 战 红. 多源注水系统泵站优化调度的双重编码混合遗传算法[J]. 自动化学报, 2006, 32(1): 154-160.
    [12] 黄敏, 杨红梅, 王兴伟. 基于遗传算法和模糊综合评价的虚拟企业风险规划[J]. 自动化学报, 2004, 30(3): 449-454.
    [13] 唐立新, 吴亚萍. 混合流水车间调度的遗传下降算法[J]. 自动化学报, 2002, 28(4): 637-641.
    [14] 薛雷, 郝跃. 基于Petri网的启发式生产调度[J]. 自动化学报, 2002, 28(5): 827-831.
    [15] 刘民, 吴澄. 解决并行多机提前/拖后调度问题的混合遗传算法方法[J]. 自动化学报, 2000, 26(2): 258-262.
    [16] 孙树栋, 林茂. 基于遗传算法的多移动机器人协调路径规划[J]. 自动化学报, 2000, 26(5): 672-676.
    [17] 唐立新, 杨自厚. 热轧实施计划中最优倒垛问题的整数规划模型及遗传算法[J]. 自动化学报, 2000, 26(4): 461-469.
    [18] 唐加福, 汪定伟, 高振, 王瑾. 面向非线性规划问题的混合式遗传算法[J]. 自动化学报, 2000, 26(3): 401-404.
    [19] 张元亮, 郑南宁, 代颖. 基于遗传算法的混合分形编码[J]. 自动化学报, 1999, 25(1): 142-144.
    [20] 何敏, 吕勇哉. 基于混合知识表达模型的启发式优化控制策略及其应用[J]. 自动化学报, 1992, 18(3): 371-375.
  • 加载中
计量
  • 文章访问数:  639
  • HTML全文浏览量:  6
  • PDF下载量:  1369
  • 被引次数: 0
出版历程
  • 收稿日期:  2014-06-23
  • 修回日期:  2014-11-17
  • 刊出日期:  2015-05-20

基于混合蛙跳和遗传规划的跨单元调度方法

doi: 10.16383/j.aas.2015.c140455
    作者简介:

    贾凌云 北京理工大学计算机学院硕士研究生. 主要研究方向为演化计算和生产调度. E-mail: lingyun jia@163.com

    通讯作者: 李冬妮 北京理工大学计算机学院副教授. 主要研究方向为智能优化, 企业计算,物流管理等. E-mail: ldn@bit.edu.cn
基金项目:

国家自然科学基金(71401014),北京市自然科学基金(4122069)资助

摘要: 针对运输能力受限条件下的跨单元问题,提出了一种基于混合蛙跳与遗传规划的超启发式算法.将改进的混合蛙跳算法作为超启发式算法的高层框架,为跨单元调度问题搜索启发式规则,同时利用遗传规划产生可以兼顾多因素的优质规则,用于扩充超启发式算法的规则集.实验表明,提出的算法可以有效地搜索出优异的规则组合,并且通过遗传规划产生的规则可以在很大程度上改善候选规则集,提升算法性能.

English Abstract

参考文献 (32)

目录

    /

    返回文章
    返回