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基于改进蚁群算法的双向物流路径优化 被引量:8

Logistics Routing Optimization Based on Improved Ant Colony Algorithm
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摘要 针对物流路径优化已有算法运算过程复杂、精度不高、过早收敛等问题,对蚁群算法进行了改进,以解决物流路径优化问题.为了消除蚁群算法的易停滞、收敛慢等问题,从蚂蚁转移策略、信息素更新方式以及遗传算法的融合等方面对算法进行了改进.针对双向物流的路径优化问题,通过增加启发函数、设计转移策略等方面来改进蚁群算法,使得算法能更好地考虑综合因素来进行搜索,能够更全面、更准确地找到合适的下一节点,从而得到更优的路线. Due to computation complexity, lower accuracy and premature convergence of the conventional algorithm, the ant colony algorithm was improved to solve the routing problem o[ logistics. In order to eliminate the problems that ant colony algorithm is easy to be stagnant and its convergence is slow, the algorithm was improved including the following points such as the transfer strategies of the ants, the pheromone update method and the integration of genetic algorithm . To solve the path optimization o{ simultaneous delivery and pickup, the heuristic function was added and the transfer strategies were designed, which made the algorithm conduct search by considering a combination of factors reasonably, and could find the next right point more comprehensively and more accurately, then, a better path could be gotten.
出处 《东北大学学报(自然科学版)》 EI CAS CSCD 北大核心 2012年第9期1240-1243,1252,共5页 Journal of Northeastern University(Natural Science)
基金 国家自然科学基金资助项目(61101121)
关键词 物流路径优化 蚁群算法 启发函数 转移策略 双向物流路径 logistics routing optimization ant colony algorithm heuristic function transferstrategy vehicle routing with simultaneous delivery and pickup
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  • 1Filipec M, Skrlec D, Krajear S. Darwin meets computers: new approach to multiple depot capacitated vehicle routing problem[A]. Proceedings of of the International Conference on Systems,Man and ,Cybernetics [ C ]. Orlando, USA: Pergamon Press,1997.421 - 426.
  • 2Filipec M, Skrlec D, Krajcar S. Genetic algorithm approach for multiple depot capacitated vehicle routing problem solving with heuristic improvements built-in [ J ]. International Journal of Modeling and Simulation, 2000,20(4) :320 -328.
  • 3Skok M, Skrlcc D, Krajcar S. The genetic algorithm method for multiple depot capacitated vehicle routing problem solving [A].The Fourth International Conference on Knowledge-based Intelligent Engineering Systems & Allied Technologies [C]. Brighton,UK:UK Press,2000. 520 -526.
  • 4Powell M J D. Variable metric methods for constrained optimization [ J]. Mathematical Programming; the State of the Art, 1983,29(29) :288 -311.
  • 5Sulhil J L, Xiang Y Y, Yuan Z Y. Multiple vehicle routing with time windows using genetic algorithms [ A ]. IEEE Congress on Evolutionary Computation [ C]. Piscataway, NJ, USA: IEEE,1999. 1804-1808.
  • 6Syswerda G. Schedule Optimization Using Genetic Algorithms[Z]. New York : Van Nostrand Reinhold, 1991. 332 - 349.
  • 7Whifley D. The genitor algorithm AND selection pressure: why rank-based allocation of reproductive trials is best [ A]. Proceedings of the Third International Conference on Genetic Algorithms and Their Application [ C ]. Morgan , USA : Morgan Kaufmann Publishers, 1989. 116 - 121.
  • 8Haghaui A L, Jung S A. Dynamic vehicle routing problem with time-dependent travel times [J]. Computers & Operations Re-search, 2004,31 (4) :653 -681.
  • 9Krajcar S. Mgorithms for Interactive Optimal Planning of Distribution Networks [ D ]. Ann Arbor: University of Aagre Press, 1988.
  • 10Laporte G,Gendreau M,Potvin J Y.Classical and modem heuristics for the vehicle routing problem[J].Intel Trans in Operation Research,2000,7:285-300.

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