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进路优化是企业编组站调度的一个重要环节。合理进行进路选择,有利于减少货车在站停留时间,提高作业效率。本文针对企业编组站的作业和站场分布特点,建立了该问题数学模型,提出了一种融合了遗传算法和蚁群算法特点的遗传蚁群算法(GACA)来解决这种大规模组合优化问题,采用遗传算法生成信息素分布,利用蚁群算法求精确解,优势互补。结合实例计算说明了该融合算法是有效可行的。
Route optimization is an important part of marshalling station scheduling. Reasonable choice of approach is conducive to reducing truck stay at the station time and improve operational efficiency. In this paper, according to the operation and station distribution characteristics of marshalling yard, a mathematical model of the problem is established and a Genetic Ant Colony Algorithm (GACA) based on genetic algorithm and ant colony algorithm is proposed to solve the problem of large-scale combinatorial optimization , The use of genetic algorithm to generate pheromone distribution, the use of ant colony algorithm to find the exact solution, the advantages of each other. The calculation example shows that the fusion algorithm is effective and feasible.