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  • 标题:Manufacturing-Uber: Intelligent Operator Assignment in a Connected Factory
  • 本地全文:下载
  • 作者:Noel P. Greis ; Monica L. Nogueira ; Tony Schmitz
  • 期刊名称:IFAC PapersOnLine
  • 印刷版ISSN:2405-8963
  • 出版年度:2019
  • 卷号:52
  • 期号:13
  • 页码:2734-2739
  • DOI:10.1016/j.ifacol.2019.11.621
  • 语种:English
  • 出版社:Elsevier
  • 摘要:This paper introduces the Manufacturing-Uber concept for dynamic assignment of operators in the Connected Factory. In traditional non-IoT machining environments it is common to assign an operator to a (small) number of machines, clustered in close proximity within a cell. In contrast to “fixed” assignment within a cell, the Manufacturing-Uber approach leverages the connectivity of the IoT environment to allow on-demand “floating” operator assignment across cells. An intelligent assignment engine determines and assigns the operator to achieve best system performance. Results show that Manufacturing-Uber outperforms fixed assignment with respect to reduction in required operators, increased machine up-time and more parts completed.
  • 关键词:KeywordsConnected FactoryIntelligent Manufacturing SystemsCognitive SystemsOperator AssignmentIndustry 4.0
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