摘要
针对当前供应链过程中物流状态信息存在获取精度较低的问题,提出供应链智慧监控与预警模型,通过构建供应链布控元节点模型,分析供应链在运作过程中流经的环节,并根据相应环节构建物流监控环境,实现物料流转的可视化,利用数据挖掘技术,挖掘供应链在不同维度层面下的运行数据,获取可视化物流环境下物料信息反馈数据,结合超限异常算法进行物料状态诊断,并引入蚁群算法,通过蚁群寻优方法提升状态诊断速度,完成供应链的监控与预警。经仿真实验结果可知,该文方法的预警相对误差较低,在高噪声环境下系统运行较为稳定,具有鲁棒性。
Aiming at the problem of low precision of obtaining logistics state information in current supply chain process,this paper puts forward intelligent monitoring and early warning model of supply chain.Through the construction of supply chain control element node model,analysis of supply chain in the process of operation,and according to the corresponding links to build logistics monitoring environment,material flow visualization.Using data mining technology,mining the operation data of supply chain at different dimension level,obtaining the feedback data of material information in visual logistics environment,combining with the algorithm of over-limit anomaly to diagnose the material state.An ant colony algorithm is introduced to improve the speed of state diagnosis by ant colony optimization,and to monitor and warn the supply chain.The simulation results show that the relative error of this method is low,the system is stable and robust in high noise environment.
作者
杨洲
宫博仁
李国源
汤强
YANG Zhou;GONG Boren;LI Guoyuan;TANG Qiang(State Grid Tianjin Binhai Electric Power Supply Company,Tianjin 300450,China)
出处
《电子设计工程》
2021年第3期56-60,65,共6页
Electronic Design Engineering
基金
天津省国网公司重大科技项目(052200GKE9826)。
关键词
能源互联网
供应链物流
智慧监控
实时预警
遗传算法
energy internet
supply chain logistics
intelligent monitoring
real-time early warning
genetic algorithm
