期刊文献+

免疫粒子群算法在混流装配线排序中的应用 被引量:12

An Immunity Particle Swarm Sequencing Algorithm for Mixed-model Assembly Lines
在线阅读 下载PDF
导出
摘要 混流装配线上的产品投产排序是影响装配线生产效率的重要因素。建立以最小化装配线总闲置-超载成本为优化目标的装配线排序模型,采用粒子群算法来解决混流装配线的投产排序问题。考虑到基本粒子群算法易陷入局部最优解的问题,引入免疫算法思想对其进行改进,根据抗体亲和性与浓度值的计算,及时进行粒子的替换以维持种群的多样性,防止粒子过早收敛于局部极值。利用某汽车零部件制造企业装配线的数据进行试验计算,并与其他方法相比较,仿真结果说明该方法可以有效、快速地解决装配线排序问题。 Mixed-model assembly line sequencing problem is crucial for the line efficiency.A sequencing model was built on the basis of minimizing the total cost of idletime and overtime.Particle Swarm Optimization algorithm was applied to optimize the subject.The immunity was used to optimize traditional algorithm to avoid the early convergence of particles.The particle was replaced in time to keep the diversity according to the particle affinity and consistency and avoid sinking into local optimum.An instance was optimized by PSO and Immunity PSO respectively,and the results showed that the algorithm is an effective method for sequencing problem of mixed-model assembly lines.
出处 《工业工程与管理》 北大核心 2011年第4期16-20,27,共6页 Industrial Engineering and Management
基金 上海市自然科学基金资助项目(10ZR1431700) "863"高技术研究发展计划资助项目(2008AA04Z105)
关键词 混流装配线 投产排序 粒子群算法 免疫 mixed-model assembly line sequencing particle swarm algorithm immunity
  • 相关文献

参考文献10

二级参考文献26

  • 1杜天军,陈光,刘占辰,雷勇.基于PSO算法的弹道辨识网络及仿真[J].系统仿真学报,2004,16(11):2517-2519. 被引量:8
  • 2金义雄,程浩忠,严健勇,张丽.改进粒子群算法及其在输电网规划中的应用[J].中国电机工程学报,2005,25(4):46-50. 被引量:90
  • 3Ponnambalama S G , Aravindanh P, Raoc M S. Genetic Algorithms for Sequencing Pblems in Mixed Model Assembly Lines[J]. Computers & Industrial Engineering , 2003 (45) : 669-690.
  • 4Xiaobo Z, Ohno K. Algorithms for Sequencing Mixed Models on an Assembly Line in a JIT Production System[J]. Computers and Industry Engineering, 1997, 32(1): 47-56.
  • 5Zhao X B, Zhao Y Z, Ainishet A. A note on Toyota's Goal of Sequencing Mixed Models on an Assembly Line[J]. Computers & Ind. Engineering , 1999(36):57-65.
  • 6Leu Y Y, Matheson L A, Rees L P. Sequencing mixed model assembly lines with genetic algorithms[J]. Computers & Ind,Engineering, 1996,30(4):1027- 1036.
  • 7Kennedy J, Eherhart R. Particle Swarm Optimization. IEEE International Conference on Neural Networks[J]. IEEE Service Center:1942-1948,1995.
  • 8Eberhart R C, Kennedy J. A new Optimizer Using Particle Swarm Theory. Proceedings of the Sixth International Symposium on Micro Machine and Human Science[J]. IEEE Service Center, 1995.
  • 9Salman A, Ahmad I, Al-Madani S. Particle Swarm Optimization for Task Assignment Problem[J]. Microprocessors and Microsystems, 2002 (26):363 - 371.
  • 10Jerald J, Asokan P, Prabaharan G, Saravanan R. Scheduling optimisation of flexible manufacturing systems using particle swarm optimisation algorithm[J]. Advanced Manufacturing Technology, 2004,3.

共引文献208

同被引文献86

  • 1于秀丽,张毕西,李逸帆,李弘.考虑员工学习效应的MOS指派优化研究[J].工业工程,2014,17(1):144-150. 被引量:4
  • 2鞠全勇,朱剑英.基于混合遗传算法的动态车间调度系统的研究[J].中国机械工程,2007,18(1):40-43. 被引量:25
  • 3GLOVER F W. tabu search[Z]. Springer, 1997.
  • 4SARKER B R P H. Designing a mixed-model, open-sta- tion assembly line using mixed-integer programming [J]. The Journal of the Operational Research Society, 2001, 52(5) :545-558.
  • 5BEAN J C. Genctic Algorithms and Random Keys for Sequcncing and Optimization [J]. ORSA Journal on Computing(S0899-1499), 1994,6(2) : 154-160.
  • 6LEU Y, MATHESON L A, REES L P. Sequencing mixed-model assembly lines with genetic algorithms [J]. Computers & Industrial Engineering, 1996, 30 (4) : 1027-1036.
  • 7HYUN C J,KIM Y,KIM Y K.A genetic algorithm for multiple objective sequencing problems in mixed model assembly lines[J].Computers & Operations Research,1998,25(7/8):675-690.
  • 8KARABOGA B D.An idea based on honey bee swarm for numerical optimization[EB/OL].(2010-10-11)[2012-09-13].http://mf.erciyes.edu.tr/abc/pub/tr06_2005.pdf.
  • 9KARABOGA D,BASTURK B.A powerful and efficient algorithm for numerical function optimization:artificial bee colony (ABC) algorithm[J].Journal of Global Optimization,2007,39(3):459-471.
  • 10AKAY B,KARABOGA D.Solving integer programming problems by using artificial bee colony algorithm[J].Lecture Notes in Computer Science,2009,5883:355-364.

引证文献12

二级引证文献64

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部