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编组站静态配流问题需要制定配流方案,明确出发列车的编组内容和车流来源。算法的思路是通过构建网络模型,将静态配流问题转化为固定费用的产销平衡运输问题,并将目标函数转化为求最小虚拟到达列车车辆数。首先设定虚拟到达列车并对其赋初值,把出发列车分为可欠轴与不可欠轴两类,在计算过程中调用学习规则保证出发列车满轴,最后求出虚拟到达列车的最小值,得到配流方案。通过简单的算例验证表明,该算法能够在有效的时间内求解大规模的静态配流问题,为静态配流问题提供一种新的方法。
Marshalling station static distribution problems need to develop a distribution plan, a clear start train grouping content and traffic sources. The idea of the algorithm is to construct a network model, and convert the static distribution problem into a fixed-cost balance of production and sales, and transform the objective function into the minimum number of virtual arriving vehicles. First set the virtual arrival train and its initial value, the departure train can be divided into two kinds of under-and can not be under the shaft, in the calculation process call learning rules to ensure that the starting train full axis, and finally find the virtual arrival train minimum, Get the distribution plan. A simple example shows that the proposed algorithm can solve large-scale static distribution problems in an effective time and provide a new method for static distribution problems.