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| 求解带时间窗的动态车辆路径问题的改进蚁群算法 |
| Improved Ant Colony Optimization Algorithm for Solving the Dynamic Vehicle Routing Problem with Time Windows |
| 投稿时间：2018-03-08 修订日期：2018-04-04 |
| DOI： |
| 中文关键词: 动态车辆路径问题 时间窗 改进蚁群算法 交通拥堵因子 全局最优解 |
| 英文关键词: dynamic vehicle routing problem time windows improved ant colony optimization algorithm traffic congestion factor global optimal solution |
| 基金项目:宝鸡市科技计划项目(16RKX1-24)；宝鸡文理学院校级重点项目(ZK16027) |
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| 中文摘要: |
| 车辆路径问题作为组合优化中的一类典型问题,其模型、算法及应用被人们广泛关注和研究。本文在建立双目标带时间窗的动态车辆路径问题数学模型的基础上,设计了一种求解该问题的改进蚁群算法。该算法首先对所有顾客进行区域划分；其次通过在传统蚁群算法中引入交通拥堵因子,提高了计算效率；再将挥发因子取为服从 上均匀分布的随机变量,使算法能更稳定的收敛到全局最优解。最后的数值实例验证了文中模型和改进蚁群算法的有效性和优越性。 |
| 英文摘要: |
| As a classical problem in combinatorial optimization, the vehicle routing problem has raised the attention of researcher in different fields to study its mathematical model, algorithm and application. Based on the bi-objective mathematical model of dynamic vehicle routing problem with time windows, an improved ant colony optimization(IACO) algorithm is designed to solve the dynamic vehicle routing problem with time windows. Firstly, the IACO algorithm divides all customers into corresponding areas. Secondly, by introducing traffic congestion factor into traditional ant colony optimization algorithm, the computational efficiency of the IACO algorithm is improved. And then, the IACO algorithm can search the global optimal solution more stably, by taking the evaporation factor for random variables with (0,1) uniform distribution. Finally, numerical example is given to illustrate the efficiency and the superiority of the IACO algorithm as well as the established mathematical model. |
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