基于Vague集的铁路车流径路优化

Optimization of Car Flow Routing based on Vague Sets

为实现车流径路时间消耗与距离消耗的双重优化,求得更合理径路,针对以往径路优化模型没有考虑径路走行时间的问题,将车辆在车流径路上的运行时间分为在途时间与在站时间,并结合信息化手段对在站时间的历史数据进行统计分析来确定在站时间参数,建立以车辆在路网上运行的路径最短和时间最短为目标的多目标优化模型,采用?K?短路算法求出满足约束的合理径路集,并引入Vague?集来对合理径路集进行评价,从而得到满意解,最后通过算例验证了模型的有效性与算法的可行性。

Railway car flow routing optimization should take account of path distance as well as path realization time so as to mirror the real process of train operation. To overcome the shortage of lack of path realization time factor in previous optimization models, travelling time of freight car in car flow routing is divided into on-way time and on-station time, and IT technology methods are used to calculate on-station parameters based on historical data of on-station time so that a multiobjective optimization model is established with objectives of shortest path and shortest time; and reasonable path sets is obtained and estimated by Vague sets and algorithm of K-shortest path. Finally, the validity of the model and the feasibility of the algorithm are verified by computational example.