编组站调度计划随机机会约束规划模型及算法研究

Stochastic Chance Constrained Programming Model and Solution of Marshalling Station Dispatching Plan

运用随机规划方法,研究列车解编时间随机变动情况下编组站阶段计划的优化编制问题,建立了以压缩车辆中时和减少出发列车晚点时间为目标的随机机会约束规划模型。将模型中的随机机会约束转化为相应等价形式,从而将随机规划模型转化为确定性模型,并提出了一种改进遗传算法对之进行求解。该算法基于列车解编顺序对染色体进行编码,并针对问题的特殊性设计了相应的交叉和变异操作。算例表明,设计的改进遗传算法能够在较短时间内收敛至最优解,编组站阶段计划的随机机会约束规划模型能取得可靠性更高的调度计划,为改进编组站的决策质量提供了一条解决的途径与方法。

This paper addresses the problem of optimiszing the marshalling station stage plan with the random break-up and assembly time by stochastic programming methods. A chance-constrained programming model has been developed with the aims to reduce the staying time of cars in the marshalling station and the average delay time of departure trains. The paper changes the chance constraints into their deterministic equivalents so that the stochastic model can be transformed to a deterministic model and presents an improved genetic algorithm to solve the problem. The chromosomes in the algorithm are encoded on the basis of the order of the train breakup and assembly process. The operation of crossover and mutation are also designed for this problem. Experimental results show that the algorithm can converge within a short time and the chance-constrained optimization approach can produce more reliable dispatching plans, which provides a way for improving the decision quality of the computer-aided dispatching plan.