基于改进蚁群算法的车辆路径选择研究

A study on vehicle routing problem based on improved ant colony optimization

给出了考虑软时间窗的物流配送车辆路径选择(VRP)模型,提出了一种改进的蚁群算法来求VRP模型的近似最优解。为了以最少的计算时间得出VRP问题的近似最优解,首先用贪婪算法产生初始蚁群,然后通过蚁群算法的评价、信息素释放、蚂蚁移动、信息素消散、判断收敛的循环过程对初始解进行优化。实践表明,在求解软时间窗物流配送车辆路径选择问题方面,改进蚁群算法具有更好的收敛性。该算法算法是求解VRP问题的较好方案。

The main purpose of this study is to search for one method reducing the cost of terminal logistics by optimizing vehicle routing. This paper translates the distribution problem of multi-centre logistics system into that of single-centre logistic. A mathematical model of logistic vehicle routing problem （VRP） with soft time window is given, and an improved ant colony optimization （ACO） is proposed to get approximate optimal solution of this VRP model. A case study shows that improved ant colony optimization has better convergence on calculation VRP with soft time window. Improved ant colony optimization is a preferable method for VRP.