基于频繁模式挖掘的制造型企业决策优化研究  

Research on Decision Optimization of Manufacturing Enterprises Based on Frequent Pattern Mining

作　　者：黄睿[1] 尹诗斯 李胡升 HUANG Rui;YIN Shi-si;LI Hu-sheng(Bussiness School,Chongqing College of Electronic Engineering,Chongqing 401331,China;Certification Centre,China Electronics Standardization Insititute,Beijing 100007,China)

机构地区：[1]重庆电子工程职业学院财经管理学院,重庆401331 [2]中国电子技术标准化研究院认证中心,北京100007

出　　处：《东北财经大学学报》2020年第4期79-89,共11页Journal of Dongbei University of Finance and Economics

基　　金：重庆电子工程职业学院2020年度校级科研项目(第一批)“基于大数据分析的制造业生产运营决策优化研究”(XJSK202007)。

摘　　要：随着射频识别(下简称RFID)技术被广泛应用于制造型企业进行生产数据的采集,生产数据在数据源质量和动态性方面都有所提升。本文针对目前制造型企业在生产计划和控制、物流和供应链方面的管理弱项,以案例研究为基础,探索频繁模式挖掘在制造型企业生产物流场景的应用情况,即基于RFID生产数据构建RFID大数据模型,经过数据清理、压缩和分类,依据不同的科学管理目标进行物流轨迹识别,并最终结合具体的业务场景,对制造型企业的生产计划和控制、物流与和供应链方面的管理决策优化提出建议。With the widespread application of Radio Frequency Identification( RFID) technology in manufacturing enterprises to collect production data, production data has been improved in terms of data source quality and dynamics.This paper focuses on the management weaknesses of manufacturing enterprises in production planning and control, logistics and supply chain;based on case studies, it explores the application of frequent pattern mining in production and logistics scenarios of manufacturing enterprises.Specifically, based on RFID production data to build RFID big data model, after data cleaning, compression and classification, according to different scientific management objectives to carry out logistics track identification.Finally, combining with specific business scenarios, it puts forward suggestions for the production planning and control, logistics and supply chain management decision optimization of manufacturing enterprises.

关 键 词：制造型企业 频繁模式挖掘 物流轨迹 决策优化 RFID大数据模型 

分 类 号：F272.3[经济管理—企业管理] TP311.13[经济管理—国民经济]