摘要
在动车段对动车组的检修作业过程中,检修车间所需零件或工具的物流配送时效性直接决定了检修作业的效率,而目前动车段内采用的配送方式均为点对点单一路径配送。通过对动车段检修物流配送问题进行分析并建立数学模型,采用遗传算法与模拟退火算法相结合的改进算法对模型进行求解,并将算法应用于动车组管理信息系统中。广州动车段在广东地区的实验数据结果表明,改进的遗传算法针对动车段在路径优化方面较为有效地提高了动车段检修物流配送效率,确保了段内动车组的及时检修,进而保障了段配属动车组的安全运用。
In the process of repairing the EMU,the efficiency of the logistics and distribution of the parts or tools needed in the maintenance workshop directly determines the efficiency of the maintenance work.However,the distribution method used in EMU depot is point to point single path distribution.Through the analysis of the logistics distribution problems of EMU depot,this article established a mathematical model,used an improved algorithm based on genetic algorithm and simulated annealing algorithm to solve the model,and applied the improved algorithm to the EMU information management system(MIS).In the aspect of routing optimization for EMU depot,experimental results from Guangzhou EMU depots in Guangdong province indicated that the improved genetic algorithm could effectively improve the efficiency of logistics distribution,ensure the timely maintenance in EMU depot,guarantee the safe operation of depot attachment of EMU.
作者
程凯
张惟皎
CHENG Kai;ZHANG Weijiao(China Academy of Railway Sciences, Beijing 100081, China;Institute of Computing Technologies, China Academy of Railway Sciences, Beijing 100081, China)
出处
《铁路计算机应用》
2017年第5期38-42,共5页
Railway Computer Application
基金
中国铁路总公司科技研究开发计划(2016X-B)
中国铁道科学研究院科研项目(2016YJ102)
关键词
动车段
物流
遗传算法
车辆路径问题
EMU depot
logistics
genetic algorithm(GA)
vehicle routing problem(VRP)
