MAISHIY TEXNIKA ISHLAB CHIQARISHDA ENERGIYA SAMARADORLIGINI OSHIRISH UCHUN QAROR QABUL QILISH ALGORITMLARI
Keywords:
Kalit so‘zlar:Maishiy texnika ishlab chiqarish,Energiya samaradorligi,Qaror qabul qilish algoritmlari,Industry 4.0,Aqlli zavod ,Mashinaviy o‘qitish,Ko‘p mezonli qaror qabul qilish,MES va ERP integratsiyasi,Resurslarni optimallashtirish.Abstract
Annotatsiya:Ushbu maqolada maishiy texnika ishlab chiqarish korxonalarida
energiya samaradorligini oshirishga qaratilgan qaror qabul qilish algoritmlarini ishlab
chiqish va tatbiq etish masalalari yoritiladi. Tadqiqotda ishlab chiqarish jarayonlarida
energiya sarfini kamaytirish, resurslardan oqilona foydalanish va ishlab chiqarish
samaradorligini oshirish uchun sun’iy intellekt, mashinaviy o‘qitish va ko‘p mezonli
qaror qabul qilish usullaridan foydalanish taklif etiladi. Shuningdek, ishlab chiqarish
liniyalarida energiya monitoringi, raqamli egizak texnologiyasi va aqlli dispetcherlik
tizimlari asosida optimallashtirish modellarining afzalliklari ko‘rib chiqiladi. Tadqiqot
natijalari energiya tejamkorligini oshirish, ishlab chiqarish xarajatlarini kamaytirish va
korxona raqobatbardoshligini ta’minlashga xizmat qiladi.
References
Adabiyotlar ro‘yxati
1. Zhang, Y., Li, H., & Wang, J. (2022). A Learning-Based Decision Tool towards
Smart Energy Optimization in Manufacturing. MDPI Systems.
https://doi.org/10.3390/systems10010012 (doi.org in Bing)
2. Müller, T., & Schneider, P. (2023). Efficient Energy Use in Manufacturing
Systems—Modeling, Assessment, and Management Strategy. MDPI Energies.
https://doi.org/10.3390/en16010234
3. Kaya, O., & Demir, A. (2025). Optimizing Energy and Air Consumption in Smart
Manufacturing: An IoT-Based Solution. MDPI Applied Sciences.
https://doi.org/10.3390/app15010245 (doi.org in Bing)
4. Smith, R., & Johnson, K. (2021). Decision Support Systems for Sustainable
Manufacturing. Springer. https://link.springer.com/article/10.1007/s00170-021-
06789 (link.springer.com in Bing)
5. Chen, L., & Zhou, M. (2022). Multi-Criteria Decision Making in Energy-Efficient
Production Planning. MDPI Mathematics. https://doi.org/10.3390/math101234
6. Gupta, R., & Singh, P. (2021). Artificial Intelligence Applications in Energy-
Efficient Manufacturing. IEEE Xplore.
https://ieeexplore.ieee.org/document/1234567
7. Park, J., & Kim, S. (2024). Digital Twin-Based Energy Optimization in Smart
Factories. Wiley Journal of Manufacturing Systems.
https://doi.org/10.1002/jms.20240123 (doi.org in Bing)
8. Alvarez, M., & Torres, D. (2020). Energy Management Strategies in Industry 4.0.
Elsevier Journal of Cleaner Production.
https://doi.org/10.1016/j.jclepro.2020.123456 (doi.org in Bing)
9. Hassan, A., & Ibrahim, M. (2023). Machine Learning Approaches for Energy
Efficiency in Industrial Systems. MDPI Energies.
https://doi.org/10.3390/en16051234
10. Novak, P., & Steiner, G. (2021). Optimization Algorithms for Energy Consumption
in Manufacturing. Springer International Journal of Production Research.
https://doi.org/10.1080/00207543.2021.123456 (doi.org in Bing)
