[1]朱杰,张培斯,张询影,等.基于改进蚁群算法的多时间窗车辆路径问题[J].计算机技术与发展,2019,29(01):102-105.[doi:10. 3969 / j. issn. 1673-629X. 2019. 01. 021]
　ZHU Jie,ZHANG Peisi,ZHANG Xunying,et al.Vehicle Ｒouting Problem with Multiple Time Windows Based onImproved Ant Colony Algorithm[J].,2019,29(01):102-105.[doi:10. 3969 / j. issn. 1673-629X. 2019. 01. 021]
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基于改进蚁群算法的多时间窗车辆路径问题()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
29
期数:
2019年01期
页码:
102-105
栏目:
智能、算法、系统工程
出版日期:
2019-01-10

文章信息/Info

Title:
Vehicle Ｒouting Problem with Multiple Time Windows Based onImproved Ant Colony Algorithm
文章编号:
1673－629X( 2019) 01－0102－04
作者:
朱杰;  张培斯;  张询影;  余微微
北京物资学院 信息学院,北京,101149
Author(s):
ZHU Jie; ZHANG Pei－si; ZHANG Xun－ying; YU Wei－wei
School of Information，Beijing Wuzi University，Beijing 101149，China
关键词:
物流运输;  多时间窗;  车辆路径问题;  蚁群算法;  模拟退火
Keywords:
logistics transportation;  multiple time window s;  vehicle routing problem;  ant colony algorithm;  simulated annealing
分类号:
F252; TP18
DOI:
10. 3969 / j. issn. 1673-629X. 2019. 01. 021
文献标志码:
A
摘要:
物流运输成本在物流总成本中占有很大比重,合理安排车辆路线,满足用户需求对企业有重要意义.车辆路径问题是运筹优化领域的热点研究问题,多时间窗车辆路径问题是对车辆路径问题的扩展.文中以总成本最小为目标,建立了多时间窗车辆路径问题的一般数学模型,针对蚁群算法在求解时容易陷入局部最优解和收敛速度慢的问题,改进转移概率公式,采用邻域搜索策略提高解的质量,借鉴模拟退火算法的思想对信息素进行更新,提高算法的寻优能力,加快收敛速度.实验结果表明,改进后的蚁群算法可以有效求得最优解,降低物流运输成本.相比其他算法,改进后的蚁群算法求解精确度高,收敛速度快,在求解多时间窗车辆路径问题上有着较好的性能.
Abstract:
Logistics transportation costs account for a large proportion of the total cost of logistics． It is of great significance for the enterprise to properly arrange the vehicle route and meet the user’s requirements． Vehicle routing problem with multiple time window s ( VＲ- PMTW) is the expansion of the vehicle routing problem，w hich is a hot research issue in the field of optimization． The general mathematical model of VＲPMTW is established with a minimum cost． In view of the problem that the ant colony algorithm is easy to fall into the local optimal solution with slow convergence speed w hen solving，the transfer probability formula is improved and the quality of the solution is enhanced by the neighborhood search strategy． The simulated annealing algorithm is referred to update the pheromone，so as to improve the optimization and speed up the convergence． The experiment show s that the improved ant colony algorithm can obtain the optimal solution effectively and reduce the cost of logistics transportation． Compared with other algorithms，it has high accuracy，fast convergence speed and great performance in solving VＲPMTW．
更新日期/Last Update: 2019-01-10