基于Pareto蚁群算法的拆卸线平衡多目标优化

被引:57
作者
丁力平
谭建荣
冯毅雄
高一聪
机构
[1] 浙江大学CAD&CG国家重点实验室
关键词
拆卸线平衡; 多目标优化; 蚁群算法; Pareto解集; 小生境技术;
D O I
10.13196/j.cims.2009.07.160.dinglp.005
中图分类号
TP391.7 [机器辅助技术];
学科分类号
摘要
为提高产品拆卸效率,针对拆卸线平衡问题建立了数学模型。该模型以最小拆卸线闲置率、负荷均衡和最小拆卸成本为优化目标。结合拆卸线平衡问题的具体特点,提出了一种改进的基于Pareto解集的多目标蚁群优化算法。算法采用小生境技术,引导蚂蚁搜索到分布良好的Pareto最优解集,并以被支配度和分散度为个体评价规则。实验测试结果表明了该算法的可行性。最后,结合企业生产实际,给出了所提模型与算法的具体应用。
引用
收藏
页码:1406 / 1413+1429 +1429
页数:9
相关论文
共 12 条
  • [1] Ant colony optimization formulti-objective flow shop scheduling problem. Yagmahan B,et al. Computers and Industrial Engineering . 2008
  • [2] A taxonomy and an empirical analysis of multiple objective ant colony optimization algorithms for the bi-criteria TSP. C Garcia-Martinez,0 Cordon,F Herrera. European Journal of Operational Research . 2007
  • [3] Beam-ACOapplied to assembly line balancing. BLUMC,BAUTISTAJ,PEREIRAJ. Proceedings of the5th Interna-tional Workshop-ANTS2006 . 2006
  • [4] Niching mechanisms in evolutionary computations. KOWALCZUK Z,BI ALASZEWSKI T. International Journal of Applied Mathematics and Computer Science . 2006
  • [5] Uninformed and probabilistic distributed agent combinatorial searches for the unary NP-complete dis-assembly line balancing problem. MCGOVERN S M. Proceedings of SPIE the International Society for Optical Engineering . 2005
  • [6] Disassembly sequencing problem:a case study of a cell phone. GUPTA S M,EVREN E,MCGOVERN S M. Pro-ceedings of the2004SPIE-The International Conference on Environmentally Conscious Manufacturing . 2004
  • [7] Com-plications in disassembly line balancing. GUNGOR A,GUPTA S M,POCHAMPALLY K,et al. Proceedings of SPIE the International Society for Optical Engineering . 2001
  • [8] Disas-sembly line balancing with li mited supply and subassembly a-vailability. ALTEKI N F T,KANDILLER L,OZDEMIREL N E. Proceedings of SPIE the International Society for Optical Engineering . 2004
  • [9] 2-Opt heuristic for the dis-assembly line balancing problem. MCGOVERN S M,GUPTAS M. Proceedings of SPIE the International Society for Optical Engineering . 2004
  • [10] A fast and elitist multi-objective genetic algorithm:NSGA-Ⅱ. Kalyanmoy Deb,Amrit Pratap,Sameer Agarwal,et al. IEEE Transactions on Evolutionary Computation . 2002