An Enhanced Data-Driven Algorithm for Shifting Bottleneck Detection

被引：4

作者：

Roser, Christoph ^{[1
]}

Subramaniyan, Mukund ^{[2
]}

Skoogh, Anders ^{[2
]}

Johansson, Bjorn ^{[2
]}

机构：

[1] Karlsruhe Univ Appl Sci, Moltkestr 30, Karlsruhe, Germany

[2] Chalmers Univ Technol, Dept Ind & Mat Sci, S-41296 Gothenburg, Sweden

来源：

ADVANCES IN PRODUCTION MANAGEMENT SYSTEMS: ARTIFICIAL INTELLIGENCE FOR SUSTAINABLE AND RESILIENT PRODUCTION SYSTEMS, APMS 2021, PT I | 2021年 / 630卷

关键词：

Shifting bottleneck detection; Active period method; Load balancing; Throughput bottlenecks; Production system;

D O I：

10.1007/978-3-030-85874-2_74

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Bottleneck detection is vital for improving production capacity or reducing production time. Many different methods exist, although only a few of them can detect shifting bottlenecks. The active period method is based on the longest uninterrupted active time of a process, but the analytical algorithm is difficult to program requiring different self-iterating loops. Hence a simpler matrix-based algorithm was developed. This paper presents an improvement over the original algorithm with respect to accuracy.

引用

页码：683 / 689

页数：7

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