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在铁路当前的运输组织模式下，编组站阶段计划鲁棒性和列车到达计划兑现率的矛盾十分突出. 为提高阶段计划的鲁棒性，本文运用随机规划方法，研究列车实际到达时刻随机条件下（相对于计划到达时刻）的阶段计划优化编制问题. 以最大化阶段计划在列车到达时刻随机扰动下保持最优的概率为目标，建立阶段计划随机相关机会规划模型. 并设计了随机模拟、禁忌搜索算法相结合的混合智能算法对模型进行求解. 算例结果表明，本文构建的模型能取得鲁棒性较高的阶段计划,能为阶段计划计算机编制提供辅助决策支持.

Stochastic Dependent-Chance Programming Model and Algorithm for Stage Plan of Marshalling Station

Under the current operation mode of railway, the contradiction between the robustness of stage plan and the arrival time of inbound trains becomes increasingly intense. To improve the robustness of the stage plan, this paper addresses the problem of optimizing the marshalling station stage plan with the random arrival time of the inbound trains (compared to the arrival time of inbound trains of schedule) by stochastic programming methods. A dependent-chance programming model is developed with the object function to maximize the probability of keeping the stage plan feasible under the fluctuation of inbound train's arrival time. Then, a hybrid intelligent algorithm based on stochastic simulation and tabu search is presented in the paper. The numerical experiments show that the algorithm can converge within a short time and the dependent-chance programming can produce a more robust stage plan and improves the decisino basis of the computer-aided dispatching plan.