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No. 6 (2025)
Marketing

Articles

OPTIMIZATION OF PROCESSES ON THE BASE OF ARTIFICIAL INTELLIGENCE IN INDUSTRY

Published 2025-06-30

Authors
Rixsiboyev Nozimbek Abdurasul oʻgʻli
Affiliation
Independent student of Tashkent International University
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Abstract

This article examines the optimization of production processes using artificial intelligence (AI) technologies in industrial manufacturing. The study utilizes theoretical analysis, a comparative study of international practices, and methods for evaluating the effectiveness of AI -based optimization methods, such as machine learning, deep learning, reinforcement learning, and genetic algorithms. The results demonstrate that AI -based optimization systems can adjust production parameters in real time, efficient ly utilize resources, reduce energy consumption, and stabilize product quality. Using international experience as examples, the article analyzes AI - based optimization methods from leading industrial companies, such as BMW, BASF, Intel, and Bosch, and prese nts their results. The key stages of implementing AI in industrial process optimization, along with technical requirements and expected economic benefits, are substantiated.

Keywords

Artificial intelligence, process optimization, manufacturing, machine learning, reinforcement learning, genetic algorithms, digital twins, real -time optimization, parameter tuning, energy efficiency, Industry 4.0, smart manufacturing, process control, adap tive control

References

  1. Kusiak, A. (2018). Smart manufacturing. International Journal of Production Research, 56(1 -2), 508 -517.
  2. Tao, F., Qi, Q., Liu, A., & Kusiak, A. (2018). Data -driven smart manufacturing. Journal of Manufacturing Systems, 48, 157 -169.
  3. Wuest, T., Weimer, D., Irgens, C., & Thoben, K. D. (2016). Machine learning in manufacturing. Production & Manufacturing Research, 4(1), 23 -45.
  4. Qi, Q., & Tao, F. (2018). Digital twin and big data towards smart manufacturing and industry 4.0. IEEE Access, 6, 3585 -3593.
  5. Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction. MIT Press.
  6. Deb, K. (2014). Multi -objective optimization. Search Methodologies, Springer, 403-449.
  7. Monostori, L. (2014). Cyber -physical production systems: Roots, expectations and R&D challenges. Procedia CIRP, 17, 9 -13.
  8. McKinsey & Company. (2023). AI in manufacturing: Optimization and beyond. McKinsey Analytics.
  9. Deloitte. (2024). Smart Factory Implementation: A Practical Guide. Deloitte Insights.
  10. World Economic Forum. (2023). The Future of Manufacturing Report. WEF Publications.
  11. Tursunov, M. R. (2023). Sanoat jarayonlarini optimallashtirish usullari. — Toshkent: Fan.
  12. Rahimov, A. S. (2022). Sunʼiy intellekt va ishlab chiqarish. — Toshkent: Iqtisodiyot.

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