摘要
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。
Routing Optimization plays an important part in scheduling operation in marshalling yards of enterprises.The detention time of wagons can be reduced by selecting reasonable routes,consequently,it can make the operation more efficient.Aiming at the characteristic of operation and distribution in the marshalling yards,this paper have established a mathematical model for this issue,and a combination of genetic algorithm and ant colony algorithm which is called GACA is presented to resolve the large-scale combinatorial optimization problem.It adopts the genetic algorithm to generate pheromone to distribute,and then makes use of the ant colony algorithm to find accurate solutions,so the advantages of those two algorithms are integrated.The example shows that the integrated algorithm is feasible and effective.
出处
《微计算机信息》
2010年第34期243-244,220,共3页
Control & Automation
关键词
编组站调度
进路优化
遗传蚁群算法
Marshalling Yards Scheduling
Routing Optimization
Genetic and Ant Colony Algorithm
