考虑多编组站协调分工的枢纽重空车流组织优化

Heavy and empty wagon flow assignment with work division among multimarshalling stations in railway terminal

研究一类考虑多编组站协调分工的枢纽重空车流组织优化问题.基于枢纽内各编组站到达、解体、集结、编组、发车及转站能力限制,以重空车流进站走行、出站走行、转站走行、解体及集结总费用最小化为目标构建数学模型.鉴于传统算法求解大规模线性规划效率低下的缺陷,设计两阶段综合求解策略,根据到发约束组生成列车-编组站匹配方案,利用解集编约束组对匹配方案进行初次调整,再利用转站约束对匹配方案进行二次调整,从而得到列车-编组站初始匹配方案集合.在此基础上,对列车-编组站匹配方案进行编码,设置优选因子并据此从初始匹配方案集合中筛选出优选方案集,给出嵌入替换-自变异-交互更新的异步循环启发式,完成对优选方案集的群体迭代寻优.最后通过设计实验场景对模型的准确性以及所提方法的有效性进行验证.

The heavy and empty wagon flow assignment with work division among multi-marshalling stations in railway terminal is presented. The problem is formulated as a linear programming model which considers some constraints indicating train arrival and departure, train breakup and makeup, wagons accumulation and wagons transshipping between different marshalling stations. The objective function makes minimization of the total cost of heavy and empty wagons entering, leaving, transshipping and accumulation. Using traditional methods for solving the large scale linear programming model is difficult and inefficient. A two-stage comprehensive optimization procedure is given and is abbreviated as two stage comprehensive optimization procedure(TSCOP), and a solving procedure is proposed to match train and marshalling station. The matching scheme is given by the capacity constraints group denoting train arrival and departure, and then the matching scheme is firstly updated by the capacity constraints group denoting train breakup,accumulation and makeup. And then the matching scheme is secondly updated by the capacity constraint denoting wagons transshipping. So the initial matching scheme set can be obtained. Moreover, the matching scheme is coded as a natural number sequence. The selection ratio is given and is adopted to choose a certain number of schemes to set up the filtered scheme set. An asynchronous iteration heuristic with replacing, variation and exchange operation is provided to find the optimal matching scheme from the filtered scheme set. The experimental scenarios are given to test the accuracy of the model and the effectiveness of the proposed heuristic.