基于量子蚁群算法的随机需求的动态车辆路径问题

Dynamic Vehicle Routing Problem with Stochastic Demand based on Quantum Ant Colony Algorithm

针对随机需求的动态车辆路径问题,以最小化成本和最大化客户满意度为目标,采用两阶段建模,把动态车辆路径问题转换为静态车辆路径问题,将量子理论与蚁群算法结合并加以改进,用量子Hε门代替传统的量子旋转门实现对蚁群的更新.用Matlab7. 0软件实现数据仿真,验证了本文改进的量子蚁群算法是求解该问题有效的方法之一.

Aiming at the problem of dynamic vehicle routing with stochastic demand,two-stage modeling is adopted to minimize the cost and maximize customer satisfaction. The dynamic vehicle routing problem is transformed into a static vehicle routing problem. The quantum theory is combined with the ant colony algorithm,and Quantum Gate is used instead of the traditional quantum revolving door to update the ant colony. Matlab is employed to simulate the data,which proves that the improved quantum ant colony algorithm is one of the effective methods to solve this problem.