Adaptive self-learning distributed and centralized control approaches for smart factories

被引：2

作者：

Antons, Oliver ^{[1
]}

Arlinghaus, Julia C. ^{[2
]}

机构：

[1] Rhein Westfal TH Aachen, Chair Management Sci, Kackertstr 7, D-52072 Aachen, Germany

[2] Otto Guericke Univ Magdeburg, Universitatstspl 2, D-31904 Magdeburg, Germany

来源：

54TH CIRP CONFERENCE ON MANUFACTURING SYSTEMS 2021-TOWARDS DIGITALIZED MANUFACTURING 4.0, CMS 2021 | 2021年 / 104卷

关键词：

distributed control; smart factory; autonomy; decision-making; discrete-event simulation; multi-agent system; cyber-physical system; Industry; 4.0; data analytics; self-learning; MYOPIC BEHAVIOR; SYSTEMS; OPTIMIZATION;

D O I：

10.1016/j.procir.2021.11.266

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The increasing application of cyber-physical systems creates a manufacturing environment in which the technical requirements for distributed control approaches, self-learning systems and analytics of previously untapped data are given. While distributed control approaches are capable to evaluate this information locally and react immediately, centralized approaches react inertly to analyzed machine performance data. In this paper, we study the performance and ability to address the ever increasing challenges in industry of both types of control approaches within an established multi-agent based discrete event simulation. (c) 2021 The Authors. Published by Elsevier B.V.

引用

页码：1577 / 1582

页数：6

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