求解带时间窗动态车辆路径问题的改进蚁群算法

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孙小军,介科伟.求解带时间窗动态车辆路径问题的改进蚁群算法[J].,2018,58(5):539-546

求解带时间窗动态车辆路径问题的改进蚁群算法

Improved ant colony optimization algorithm for solving dynamic vehicle routing problem with time windows

DOI：10.7511/dllgxb201805015

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:宝鸡市科技计划资助项目(16RKX1-24)；宝鸡文理学院校级重点项目(ZK16027).

作者	单位
孙小军,介科伟

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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．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(0,1)上均匀分布的随机变量，使算法能更稳定地收敛到全局最优解．最后的数值实例验证了所建数学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 established 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, all customers are divided into corresponding areas. Secondly, by introducing traffic congestion factor into traditional ant colony optimization algorithm, the computational efficiency is improved. And then, by taking the evaporation factor for random variables with (0,1) uniform distribution, the algorithm can search the global optimal solution more stably. 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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求解带时间窗动态车辆路径问题的改进蚁群算法

本站小编 Free考研考试/2024-01-16

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