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Research on Coordinated Development of a Railway Freight Collection and Distribution System Based on an “Entropy-TOPSIS Coupling Development Degree Model” Integrated with Machine Learning

机译:基于“熵 - TOPSIS耦合开发模型”的铁路货运收集系统协调发展研究与机器学习集成

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In recent years, with the gradual networking of high-speed railways in China, the existing railway transportation capacity has been released. In order to improve transportation capacity, railway freight transportation enterprises companies have gradually shifted the transportation of goods from dedicated freight lines to passenger-cargo lines. In terms of the organization form of collection and distribution, China has a complete research system for heavy-haul railway collection and distribution, but the research on the integration of collection and distribution of the ordinary-speed railway freight has not been completed. This paper combines the theories of the integration of collection and distribution theory, coordination theory, and coupling theory and incorporates the machine learning fuzzy mathematics to construct an “Entropy-TOPSIS Coupling Development Degree Model” for dynamic intelligent quantitative analysis of the synergy of railway freight collection and distribution systems. Finally, we take the Tongchuan Depot of “China Railway Xi’an Group Co., Ltd.” as a research object to construct a target system and use the intelligent information acquisition system to collect basic data. The analysis results show that through the coordinated control of the freight collection and distribution system, the coordination between the subsystems of the integrated freight collection and distribution system is increased by 5.94%, which verifies the feasibility of the model in the quantitative improvement of the integration of collection and distribution system. It provides a new method for the research of integrated development of railway freight collection and distribution.
机译:近年来,随着中国高速铁路的逐步网络,现有的铁路运输能力已被释放。为了提高运输能力,铁路货运企业公司逐步转移到专门的货物货物运输到乘客货车。在组织的收集和分配形式方面,中国拥有重载铁路收集和分销的完整研究体系,但普通速度铁路货运收集和分销集成的研究尚未完成。本文结合了集合和分配理论,协调理论和耦合理论的整合理论,并融入了机器学习模糊数学,构建了一种“熵-Topsis耦合开发度模型”,用于铁路货运协同作用的动态智能定量分析收集和分配系统。最后,我们乘坐“中国铁路西安集团有限公司”的铜川仓库作为构建目标系统并使用智能信息采集系统来收集基本数据的研究对象。分析结果表明,通过对货运收集系统的协调控制,综合运费收集系统的子系统之间的协调增加了5.94%,验证了模型的可行性,以定量改进整合的数量改进集合与分配系统。它为铁路货运收集和分销的综合发展提供了一种新方法。

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