Data-driven production control for complex and dynamic manufacturing systems

被引：63

作者：

Frazzon, Enzo M. ^{[1
]}

Kueck, Mirko ^{[2
]}

Freitag, Michael ^{[2
,3
]}

机构：

[1] Univ Fed Santa Catarina, Ind & Syst Engn Dept, Florianopolis, SC, Brazil

[2] Univ Bremen, BIBA Bremer Inst Prod & Logist GmbH, Bremen, Germany

[3] Univ Bremen, Fac Prod Engn, Bremen, Germany

来源：

CIRP ANNALS-MANUFACTURING TECHNOLOGY | 2018年 / 67卷 / 01期

关键词：

Manufacturing systems; Optimization; Adaptive control; CYBER-PHYSICAL SYSTEMS; OPTIMIZATION; SIMULATION; ALGORITHM; DESIGN; RULES;

D O I：

10.1016/j.cirp.2018.04.033

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Digitalization allows for production control based on the current state of the manufacturing system. Thereof, this paper proposes and applies a data-driven adaptive planning and control approach that uses simulation-based optimization to determine most suitable dispatching rules in real-time under varying conditions. The data integration between the real manufacturing system and the simulation model is implemented through a data-exchange framework. The approach is evaluated in a scenario of a Brazilian manufacturer of mechanical components for the automotive industry, achieving better operational performance compared to the procedure previously applied by the company as well as in comparison to static dispatching rules. (C) 2018 Published by Elsevier Ltd on behalf of CIRP.

引用

页码：515 / 518

页数：4

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