编组站配流的协调优化算法  

Optimization Algorithm for Wagon-Flow Allocation in Marshalling Station

作　　者：薛锋[1] 王慈光[1] 张展杰[1,2] 

机构地区：[1]西南交通大学交通运输学院,四川成都610031 [2]成都铁路局贵阳南站,贵州贵阳550003

出　　处：《西南交通大学学报》2010年第6期932-937,共6页Journal of Southwest Jiaotong University

基　　金：国家自然科学基金资助项目(60776824);中央高校基本科研业务费专项资金资助项目(SWJTU09BR134);西南交通大学青年教师科研起步项目的资助(2009Q040)

摘　　要：为了提高编组站动态配流与静态配流协调优化算法的收敛速度,根据编组站解体方案树的构造规则,用解体序号矩阵进行解体方案编码,限制解的生成空间,避免了不必要的搜索.结合遗传算法与蚁群算法(genetic and ant algorithm,GAAA)的优势和配流问题的特点,设计了以GAAA为基础的协调优化算法.用遗传算法求出若干组优化解体方案,并生成初始信息素分布,用静态配流蚁群算法筛选出最优解体方案,在此基础上生成配流方案.实例表明:对阶段到发列车数不超过25列的编组站配流问题,本文算法均能在30 s内收敛到最优解或满意解.To improve the convergence performance of optimization algorithms for static and dynamic wagon-flow allocation,a genetic-ant algorithm was proposed,in which unnecessary search was avoided by limiting the solution space and coding schemes with their sequence number matrix following the rules of scheme tree in a marshalling station.An optimization algorithm based on GAAA（genetic and ant algorithm） was designed,which takes the characteristic of wagon-flow allocation problems into consideration and makes use of advantages of genetic and ant algorithms.It uses a genetic algorithm to obtain optimized break-up schemes and generate initial pheromones,and an ant algorithm to select the most optimum break-up scheme to produce a wagon-flow allocation scheme.Results of examples show that the proposed algorithm converges within 30 s for a wagon-flow allocation problem,in which the number of arrival and departure trains does not exceed 25 during an operation period.

关 键 词：编组站 配流 遗传算法 蚁群算法 优化 

分 类 号：U292.16[交通运输工程—交通运输规划与管理]