O. Gorobchenko, V. Matsiuk, H. Holub, D. Zaika, I. Gritsuk
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引用次数: 0
摘要
在对有前途的机车进行研究时,我们面对的是一个复杂的事件--"选择实施机车"。为有效解决这一问题,建议对这一事件进行分解。因此,这项工作的目的是根据新牵引机车车辆各种方案的客观标准,开发一种评估过程建模方法。通过将层次结构转化为人工神经网络,开发出了 Saaty 方法。该网络在分析成对比较矩阵时自动进行训练,输出结果是一个通用标准--机车 R 的评级,其值从 0(最差指标)到 1 不等。取而代之的是,对牵引车辆进行评估的最重要标准的比较矩阵。所开发的方法能够支持机车停车场的各种运营策略。这在编制二级标准比较矩阵阶段就已实现。根据铁路面临的任务,还可以调整一种标准对另一种标准的偏好程度。这为拟议方法的使用提供了更大的灵活性。
The theoretical basis of the choice of new locomotives for Ukraine in the post-war period
In the case of the research of promising locomotives, we are dealing with a complex event – "choosing a locomotive for implementation". To effectively solve this problem, it is suggested to decompose this event. Therefore, the purpose of this work is to develop a methodology for modeling the evaluation process according to objective criteria of various options of new traction rolling stock. The Saaty method has been developed by transforming the hierarchy into an artificial neural network. The training of this network occurs automatically when analyzing the matrices of pairwise comparisons, and at the output we have a generalized criterion – the rating of the locomotive R, the value of which varies from 0 (the worst indicator) to 1. This allowed, unlike the existing approach, not to compare locomotives by compiling a matrix of comparisons at the last stage. Instead, a matrix of comparisons of the most important criteria by which traction rolling stock is evaluated has been compiled. The developed method has the ability to support various strategies for the operation of the locomotive park. This is implemented at the stage of drawing up the second-level criteria comparison matrix. Depending on the tasks facing the railways, it is also possible to adjust the degree of preference of one criterion over another. This provides even greater flexibility in using the proposed method.