An Enhanced Data-Driven Algorithm for Shifting Bottleneck Detection - Artificial Intelligence for Sustainable and Resilient Production Systems PART IV, IFIP WG 5.7 International Conference, APMS 2021 Access content directly
Conference Papers Year : 2021

An Enhanced Data-Driven Algorithm for Shifting Bottleneck Detection

Abstract

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.
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hal-04030347 , version 1 (15-03-2023)

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Christoph Roser, Mukund Subramaniyan, Anders Skoogh, Björn Johansson. An Enhanced Data-Driven Algorithm for Shifting Bottleneck Detection. IFIP International Conference on Advances in Production Management Systems (APMS), Sep 2021, Nantes, France. pp.683-689, ⟨10.1007/978-3-030-85874-2_74⟩. ⟨hal-04030347⟩
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