• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

计算机工程与科学

• 论文 • 上一篇    下一篇

面向产业链云服务平台的分布式备件库存协同控制方法与软件工具研究

吕瑞1,2,孙林夫1,2   

  1. (1.西南交通大学制造业产业链协同与信息化支撑技术四川省重点实验室,四川  成都 610031;
    2.西南交通大学四川省现代服务科技工程技术研究中心,四川 成都 610031)
  • 收稿日期:2016-10-30 修回日期:2016-03-09 出版日期:2017-10-25 发布日期:2017-10-25
  • 基金资助:

    中央财政服务业发展专项资金(2015059901010);四川省科技支撑计划(2014GZ0142)

An overall scheme and a software tool of coordination and control
of accessories stock for cloud service platform of industrial chian

Lv Rui1,2,SUN Lin-fu1,2   

  1. (1.Sichuan Province Key Laboratory of Manufacturing Industry Chain Collaboration and Information Supply Technology,
    Southwest Jiaotong University,Chengdu 610031;
    2.Sichuan Province Research Center for Modern Service Science and Technology Engineering Technique,
    Southwest Jiaotong University,Chengdu 610031,China)
     
  • Received:2016-10-30 Revised:2016-03-09 Online:2017-10-25 Published:2017-10-25

摘要:

针对制造业产业链协同服务平台的备件业务协作需求,提出跨节点的库存协同解决方案并建立近期需求预测计算模型。结合分布式节点企业的历史交易数据、库存数据的实时采集与处理应用,保障库存控制方案的实效性。采用MapReduce框架对模型参数计算过程进行优化,提高运算速度。基于遗传算法获取模型计算最优解,并将模型计算结果推送至下游经销商企业群,由反馈信息控制订单的动态生成。并将该模式应用在汽车产业链云服务平台,压缩了产业链响应时间。
 

关键词: 备件, 产业链, 库存协同, 遗传算法

Abstract:

To meet the spare parts collaborative needs of manufacturing industry chain collaborative platform, we propose a cross-enterprise inventory control solution and establish a near-term demand forecasting model. In order to ensure the effectiveness of the inventory control program, we dynamically extract historical transaction data of each node and real-time inventory data, and obtain the optimal solution based on the genetic algorithm. We use the MapReduce framework to calculate model parameters and improve the processing speed. By pushing the results to downstream service providers, the feedback information can control the dynamically generated orders. The application in the automotive industry chain cloud services platform demonstrates that the response time of the industry chain is compressed.

Key words: spare parts, industrial chain, inventory cooperative, genetic algorithms