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结合当前国内外对高速铁路动车组运用的相关研究,给出了动车组周转这一特大型组合优化问题的数学描述并建立神经网络模型,提出了基于分层聚类思想与模拟退火算法相结合的解决方案,降低了算法的时间复杂度,达到了减少车底在车站的停留时间,提高了车底利用效率,为优化我国在建和拟建的高速铁路、城际客运专线的动车组周转及计算机自动编制车底运用计划提供理论支持,并结合实际客运专线运用计算机模拟进行检算,证实了算法的可行性、实用性。
Combined with the current domestic and foreign research on the application of EMU for high-speed railway, the mathematical description of the EMU optimization problem is given and the neural network model is established. Based on the idea of hierarchical clustering and simulated annealing Solution to reduce the time complexity of the algorithm to reduce the time spent in the car at the station and improve the utilization efficiency of the bottom of the car, in order to optimize the construction of our country and the proposed high-speed railway, intercity passenger train EMU turnover And computer automatic preparation of vehicle under the plan to provide theoretical support, combined with the actual use of passenger dedicated passenger line simulation of computer simulation, confirmed the feasibility of the algorithm.