中文摘要：

以同一支车流不可拆散、编组去向容量、车站最大编组去向数量、车站改编能力作为约束条件,以车流走行和改编总成本最小作为目标函数,构建车流改编方案优化模型;在此基础上考虑日均车流量的波动,构造车流改编方案随机优化模型;设计基于随机模拟的混合模拟退火算法;以具有10个节点的网络为例进行验证计算。结果说明：随机优化模型可以获得鲁棒性较强的车流改编方案,该方案虽然不能保证在所有情景下都为最优,但是在绝大多数情景下都是＂较优＂解。此外,车流改编方案的总成本在车流量随机波动的情况下变化相对平稳,在可容忍的范围之内。可见采用给出的随机优化模型获得的车流改编方案具有更高的可靠性,对车流量变化的敏感度更低。

英文摘要：

Under the constraint conditions of the nonbreakup of same wagon flow,the capacity of formation direction,the maximum number of formation direction at station and the reorganization capacity of station,the optimization model for wagon flow reorganization was constructed with the wagon flow running and the minimum cost of reorganization as the target function.Based on it,a stochastic optimization model for the reorganization plan of wagon flow was formulated,taking the fluctuation of the daily average wagon flow into consideration. A hybrid simulated annealing algorithm based on stochastic simulation was designed and tested by an example with 10 nodes. The result shows that the stochastic optimization model can obtain a more robust plan for wagon flow reorganization. This plan is not always the best plan in all scenarios, but it can be a near-optimal one in most scenarios. In addition, the change of the total cost for the wagon flow reorganization plan is relatively stable under the stochastic fluctuation of wagon flow,which is within the tolerable range. In conclusion, the given stochastic optimization model can obtain a more reliable wagon flow reorganization plan which is not so sensitive to the fluctuation of the wagon flow.