سامانه همایش های دانشگاه آزاد اسلامی واحد نجف آباد

صفحه اصلی / International Conference on Artificial Intelligence; City, Industry and Health

An AI-Based Approach for Predicting and Optimizing Production Line Settings with a Focus on OEE

نویسندگان :

Nazila Adabavazeh¹ Mehrdad Nikbakht² Atefeh Amindoust³

1- Department of Industrial Engineering, Na. C., Islamic Azad University, Najafabad, Iran 2- Department of Industrial Engineering, Na. C., Islamic Azad University, Najafabad, Iran 3- Department of Industrial Engineering, Na. C., Islamic Azad University, Najafabad, Iran

کلمات کلیدی :

Artificial Intelligence،Optimization،Production Lines،OEE،Random Search

چکیده :

In today's world, optimizing manufacturing operations and enhancing equipment efficiency are among the primary priorities of industries. Achieving high performance in production lines not only contributes to cost reduction and improved competitiveness but also plays a crucial role in meeting sustainability goals and increasing overall productivity. One of the key indicators for evaluating manufacturing performance is Overall Equipment Effectiveness (OEE), which is widely used to assess the efficiency and productivity of production systems. However, the complexities involved in managing and optimally adjusting various operational parameters—such as speed, temperature, and pressure—highlight the need for innovative and intelligent approaches. This paper introduces an artificial intelligence-based approach for predicting and optimizing production line settings with a focus on OEE. To this end, the Random Search algorithm has been employed as an efficient and straightforward method for finding the best combination of operational parameters. In this study, a range of key parameters was randomly sampled, and the OEE was simulated and calculated for each combination. For validating the proposed approach, the plastic injection industry was selected as the case study. The results of this research demonstrate that even simple methods like Random Search, when properly designed, can have a significant impact on optimizing manufacturing processes and can effectively contribute to the intelligent transformation of production lines.