期刊文献+

一种基于改进蚁群算法的混合型调度算法 被引量:4

The Algorithm of Hybrid Pocess Attempering Based on the Improved Ant Colony Algorithm
下载PDF
导出
摘要 针对混合型制造业车间生产调度及时性、合理性、科学性及应用结合性上的不足,提出了一种基于改进蚁群算法的混合型调度算法,结合混合型生产的特点,首先给出了混合型生产调度问题细化模型,然后对传统生产调度模型中的蚁群算法进行了改进,最后通过在具备混合型生产特点的汽车玻璃制造企业测试应用后,验证了该算法的可行性及有效性。 It deals with an algorithm of hybrid process attempering based on the improved ant colony algorithm. Based on the deficiency of the scheduling algorithm for the hybrid process in workshop,. it provides an improved ant schedule algorithm. After analysis of a detailed data model for hybrid process attempering,it improves the original algorithm of ant scheduling algorithm. Finally this algorithm is used in a company which produces automotive glass to test the validity of the algorithm. Test results prove the validity and the feasibility of this algorithm.
出处 《中国制造业信息化(学术版)》 2010年第7期8-13,17,共7页
基金 广州市科委重点科技攻关项目(2006Z1-D3021)
关键词 蚁群算法 混合型 调度算法 柔性 Ant Colony Algorithm Hybrid process Scheduling Algorithm Flexible
  • 相关文献

参考文献12

二级参考文献77

共引文献116

同被引文献33

  • 1李垒,王章豹.自主创新:从制造业大国走向制造业强国的必由之路[J].内蒙古科技与经济,2007(4):8-11. 被引量:5
  • 2吴大为,陆涛栋,刘晓冰,孟永胜.求解作业车间调度问题的并行模拟退火算法[J].计算机集成制造系统,2005,11(6):847-850. 被引量:20
  • 3Deb K. Multi--objective Optimization Using Evolu tionary Algorithms[M]. New York: John Wiley &Sons, 2001.
  • 4Kennedy J, Eberhart R. Particle Swarm Optimiza tion[C]//IEEE Int. Conf. on Neural Networks Piscataway, 1995 : 1942-1948.
  • 5Laumanns M, Thiele L, Deb K, et al. Combining Convergence and Diversity in Evolutionary Multi-- objective Optimization[J]. Evolutionary Computa- tion, 2002, 10(3): 263-282.
  • 6Tan K C, Lee T H, Khor E F. Evolutionary Al- gorithms with Dynamic Population Size and Local Exploration for Multi--objective Optimization[J]. IEEE Trans. on Evolutionary computation, 2001, 5(6) : 565-588.
  • 7Zitzler E, Thiele L. Multiobjective Evolutionary Algorithm: a Comparative Case Study and the Strength Pareto Approach[J]. IEEE Transactions on Evolutionary Compution, 1999,3(4) :252-271.
  • 8Benabid R, Boudour M, Abido M. Optimal Loca- tion and Setting of SVC and TCSC Devices Using Non dominated Sorting Particle Swarm Optimi- zation[J]. Electric Power Systems Research, 2009,12(79) :1668-1677.
  • 9蒋程涛,邵世煌.基于适配粒子群的多目标优化方法[J].计算机工程,2007,33(21):175-178. 被引量:8
  • 10Kennedy J,Eberhart RC.Particle swarm optimization[].Proceedings of the IEEE International Joint Conference on Neural Networks.1995

引证文献4

二级引证文献7

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