A Hybrid Particle Swarm Optimization Algorithm for Vehicle Routing Problem with Stochastic Travel Time

被引：0
|
作者：
Shao, Ze-jun [1 ]
Gao, Shu-ping [1 ]
Wang, Sha-sha [1 ]
机构：
[1] Xidian Univ, Dept Math, Xian 710071, Shaanxi, Peoples R China
来源：
FUZZY INFORMATION AND ENGINEERING, VOL 1 | 2009年 / 54卷
关键词：
Particle swarm optimization; chance-constrained programming model; stochastic vehicle; neighbor operator;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Vehicle Routing Problem with stochastic travel time (VRPST) is of crucial importance in today's industries, especially in logistics distribution. This paper presents a hybrid particle swarm optimization algorithm (HPSO) to solve the problem. A chance-constraint model considering capacity of vehicle is founded. The VRPST was changed into a quasi - continuous problem by designing a real number coding. Constrained terms were processed by the penalty function. Cooperating with dynamic neighborhood and the weight value of variable inertia, the proposed HPSO can find the global optimum. The results axe compared with those by both standard particle swarm optimization (SPSO) and improved genetic algorithm (IGA). The illustrations indicate that HPSO can improve success rate of searching best route and is effective for VRPST.
引用
收藏
页码：566 / 574
页数：9
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