期刊文献+
任意字段
题名或关键词
题名
关键词
文摘
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息

基于微粒群技术的多分拣区产品存储的算法设计

Optimization Algorithm Design of the Forward Reserve Problem for Multiple Forward Areas Based on Particle Swarm Techonology
下载PDF
导出
摘要 由于多分拣区产品存储优化决策是一类典型的NP难问题,求解较为困难,尤其当问题规模增大后,应用精确算法求解时间代价较高。考虑到微粒群技术在求解大规模组合优化问题的优势,本论文针对多分拣区产品存储优化设计了微粒群算法,并用算例验证了该算法的有效性。 Forward-reserve problem（FRP） in a multiple forward area can not easily be solved since it is a NP-hard problem. Especially, for a large sized problem it is very time-consuming to solve by using the exact algorithm. Since particle swarm optimization （PSO）shows great efficiency when solving a big scale optimization problem, the paper proposed PSO for FRP in multiple forward area and a numerical example showed the effectiveness of the proposed algorithm.
作者 陈彦如 杨璐 魏朝恒 曾东红
机构地区 西南交通大学经济与管理学院 西南交通大学交通运输与物流学院
出处 《交通运输工程与信息学报》 2015年第3期16-21,共6页 Journal of Transportation Engineering and Information
基金 四川省科技支撑计划项目(2012GZ0063) 中央高校基本科研业务费专项资金资助(2682013CX074)
关键词 多分拣区 产品存储优化 微粒群 Multiple forward areas forward-reserve problem particle swarm optimization
分类号 F253.4 [经济管理—国民经济]
  • 相关文献

参考文献6

  • 1Heragu S S, Du L, Mantel R J, et al. Mathematical model for warehouse design and product allocation[J]. International Journal of Production Research, 2005, 43(2): 327-338.
  • 2Van den Berg J P, Sharp G P, Gademann A N, et al. Forward-reserve allocation in a warehouse with unit-load replenishments[J]. European Journal of Operational Research, 1998, 111 (1): 98-113.
  • 3Frazelle E H, Hackman S T, Passy U, et al. The forward-reserve problem[M]. John Wiley & Sons, Inc., 1994.
  • 4Gu J, Goetschalckx M, MeGinnis L F. Solving the forward-reserve allocation problem in warehouse order picking systems[J]. Journal of the Operational Research Society, 2010, 61(6): 1013-1021.
  • 5谢晓锋,张文俊,杨之廉.微粒群算法综述[J].控制与决策,2003,18(2):129-134. 被引量：421
  • 6陈彦如,单翠,蒋阳升,等.多分拣区FRP的GA&SS算法设计[J/OL].重庆交通大学学报(自然科学版).20141205.1507.013.htmldetail/50.1190.

二级参考文献34

  • 1[31]Eberhart R, Hu Xiaohui. Human tremor analysis using particle swarm optimization[A]. Proc of the Congress on Evolutionary Computation[C].Washington,1999.1927-1930.
  • 2[32]Yoshida H, Kawata K, Fukuyama Y, et al. A particle swarm optimization for reactive power and voltage control considering voltage security assessment[J]. Trans of the Institute of Electrical Engineers ofJapan,1999,119-B(12):1462-1469.
  • 3[33]Eberhart R, Shi Yuhui. Tracking and optimizing dynamic systems with particle swarms[A]. Proc IEEE Int Conf on Evolutionary Computation[C].Hawaii,2001.94-100.
  • 4[34]Prigogine I. Order through Fluctuation: Self-organization and Social System[M]. London: Addison-Wesley,1976.
  • 5[1]Kennedy J, Eberhart R. Particle swarm optimization[A]. Proc IEEE Int Conf on Neural Networks[C].Perth,1995.1942-1948.
  • 6[2]Eberhart R, Kennedy J. A new optimizer using particle swarm theory[A]. Proc 6th Int Symposium on Micro Machine and Human Science[C].Nagoya,1995.39-43.
  • 7[3]Millonas M M. Swarms Phase Transition and Collective Intelligence[M]. MA: Addison Wesley, 1994.
  • 8[4]Wilson E O. Sociobiology: The New Synthesis[M]. MA: Belknap Press,1975.
  • 9[5]Shi Yuhui, Eberhart R. A modified particle swarm optimizer[A]. Proc IEEE Int Conf on Evolutionary Computation[C].Anchorage,1998.69-73.
  • 10[6]Kennedy J. The particle swarm: Social adaptation of knowledge[A]. Proc IEEE Int Conf on Evolutionary Computation[C].Indiamapolis,1997.303-308.

共引文献420

交通运输工程与信息学报
2015年 第3期

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