The Newly Improved Particle Swarm Optimization for Vehicle Routine Problem Under the Condition of Stock Shortage

摘要：

The paper analyzed vehicle routine problem under the condition of stock shortage and established corresponding mathematic models based on the actual situation of delivery process. Particle swarm Optimization with dynamically changing inertia weight was introduced to solve the problem. The result of calculation indicated that the algorithm, compared with basic Particle Swarm Optimization, can greatly increase speed of convergence, reduce iterative times and help particle to avoid local optimization so as to achieve global optimization.

关键词： Vehicle Routine Problem Particle Swarm Optimization Dynamic Inertia Weight

作者: Fang Jincheng Zhang Qishan Ruan Xuefeng

作者单位: Management School of Fuzhou University, Fuzhou P.R.China, 350002 ;Fujian University of Technology, F Management School of Fuzhou University, Fuzhou P.R.China, 350002

会议类型: 国际会议

会议名称: 第十四届工业工程与工程管理国际会议(The Proceedings of The 14th International Conference on Industrial Engineering and Engineering Management IE&EM2007)

会议地点: 天津

会议语种:英文

在线出版日期: 2007-10-20（万方平台首次上网日期，不代表论文的发表时间）