针对航空票务公司免费接送顾客去机场路径优化的问题, 文章研究了更贴近实 际的关于单时间窗约束下的接送机场服务, 同时考虑了接送过程中的碳排放, 构建出相应 的优化模型, 提出利用蚁群算法来解决该问题, 并采用改进的蚁群算法加以求解.在初始选择路 径上的改进, 有效解决路径选择上容易陷入局部最优的缺点;根据当前节点到目标点和起点的距离, 重新设计启发式函数, 驱使车辆尽量沿着起点和目标点之间的最短路行进;依据 实时路径长度, 动态调整挥发系数, 精炼搜索空间, 提高收敛性能.最后通过参数校验和实例计算验证, 得出了适用于此问题的蚁群算法的参数优化组合;以及顾客点位置在三种不同类型分布下时, 使用改进后的蚁群算法都能更好的求出问题的最优解, 表明改进后的蚁群算法是解决航空票务公司免费接送顾客去机场服务路径优化问题的一个更有效的求解算法.

For the problem of free pick-up and drop-off of customers to the airport route for air ticketing companies. In this paper, we study the transport of customers to the airport with the actual single time window constraint, and consider the total carbon emission in the transport process. At the same time, we construct the corresponding optimization model, and improve the traditional ant colony algorithm. The algorithm cannot select the initial path randomly and improve the goodness of the initial solution, so that the better solution can be found more quickly. At the same time, the heuristic function is redesigned according to the distance from the current node to the target point and the starting point. Try to travel along the shortest path between the starting point and the target point. Finally, according to the length of the real-time path, the volatility is dynamically adjusted to refine the search space and improve the convergence performance. Finally, validated by checking and calculation of the parameters, obtained the ant colony algorithm is applicable to the problem of optimum combination of parameters, and customer point location based on the distribution of the three different types, the improved ant colony algorithm can better find out the optimal solution of the problem, shows that the improved ant colony algorithm is a more efficient algorithm to solve air ticketing company free pick-up service from the customer to the airport route optimization problem.