An Enhanced Data-Driven Algorithm for Shifting Bottleneck Detection
Paper i proceeding, 2021

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.

Shifting bottleneck detection

Production system

Active period method

Throughput bottlenecks

Load balancing

Författare

Christoph Roser

Hochschule Karlsruhe - Technik und Wirtschaft

Mukund Subramaniyan

Chalmers, Industri- och materialvetenskap, Produktionssystem

Anders Skoogh

Chalmers, Industri- och materialvetenskap, Produktionssystem

Björn Johansson

Chalmers, Industri- och materialvetenskap, Produktionssystem

IFIP Advances in Information and Communication Technology

1868-4238 (ISSN) 1868-422x (eISSN)

Vol. 630 683-689

Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems
Nantes, France,

DAIMP - Dataanalys inom underhållsplanering

VINNOVA (2015-06887), 2016-03-01 -- 2019-02-28.

Ämneskategorier

Produktionsteknik, arbetsvetenskap och ergonomi

Programvaruteknik

Signalbehandling

Datavetenskap (datalogi)

Styrkeområden

Produktion

DOI

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

Mer information

Senast uppdaterat

2021-10-13