编组站动态配流改进树状模型及其蚁群算法
首发时间:2009-03-23
摘要:编组站动态配流所要解决的核心问题是从众多的解体方案中选择对出发列车有利的方案。由于问题的复杂性,目前仍然缺乏高效算法。蚁群算法作为一种基于种群的并行进化算法,易于与其它启发式方法相结合,能够利用正反馈原理发现满意解。通过改进编组站动态配流树状模型,可将不相容方案纳入解体方案的搜索过程。根据方案树的改进构造规则,设计了求解编组站动态配流问题的蚁群算法。同时,蚁群算法在计算精度上具有优越性,是解决较大规模动态配流问题的有效途径,可为编组站阶段计划自动优化编制提供决策基础。
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Improving Tree Model and Ant Colony Optimization Algorithms for Dynamic Wagon-Flow Allocating in Marshalling Station
Abstract:How to select the favorable scheme out of numerous break-up schemes is the core of the dynamic wagon-flow allocating problem in marshalling station. Because of the complexity, this problem still lacks the high-efficient algorithm at present. The ant colony optimization algorithm is a parallel evolutionary algorithm based on population, easy to combine with other heuristic methods, can utilize the positive feedback principle to find satisfactory solution. The improving tree model for dynamic wagon-flow allocating, can include the incompatible scheme in the search course of the break-up schemes. According to the improving construct rule of the scheme tree, ant colony optimization algorithm has been designed to solve the dynamic wagon-flow allocating problem in marshalling station. Meanwhile, ant colony optimization algorithm has superiority in calculating precision, is the effective way to solve the more massive dynamic wagon-flow allocating problem, and can offer the decision foundation for working out stage plans automatically.
Keywords: marshalling station dynamic wagon-flow allocating improving tree model ant colony optimization algorithm
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