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Assessing operational complexity of manufacturing systems based on algorithmic complexity of key performance indicator time-series

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  • Bugra Alkan
  • Seth Bullock

Abstract

This article presents an approach to the assessment of operational manufacturing systems complexity based on the irregularities hidden in manufacturing key performance indicator time-series by employing three complementary algorithmic complexity measures: Kolmogorov complexity, Kolmogorov complexity spectrum’s highest value and overall Kolmogorov complexity. A series of computer simulations derived from discrete manufacturing systems are used to investigate the measures’ potentiality. The results showed that the presented measures can be used in quantitatively identifying operational system complexity, thereby supporting operational shop-floor decision-making activities.

Suggested Citation

  • Bugra Alkan & Seth Bullock, 2021. "Assessing operational complexity of manufacturing systems based on algorithmic complexity of key performance indicator time-series," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 72(10), pages 2241-2255, October.
    • Handle: RePEc:taf:tjorxx:v:72:y:2021:i:10:p:2241-2255
    DOI: 10.1080/01605682.2020.1779622
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