International Journal of Industrial Engineering and Management

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Vol. 15 No. 3 (2024)
Original Research Article

Advancements in Optimization for Automotive Manufacturing: Hybrid Approaches and Machine Learning

Nelson Nainggolan
Mathematics Department, Sam Ratulangi University, Manado, Indonesia
Ebrahim Maghsoudlou
Southern Illinois University Carbondale, School of Computing, Department of Computer Science, Carbondale, IL United States
Belal Mahmoud AlWadi
Al Zaytoonah University of Jordan, Amman, Jordan
Farruh Atamurotov
New Uzbekistan University, Tashkent, Uzbekistan
Mikhail Kosov
Plekhanov Russian University of Economics, Department of State and Municipal Finance, Moscow, Russia
Windhu Putra
Universitas Tanjungpura, Pontianak, Indonesia

Published 2024-09-01

abstract views: 268 // FULL TEXT ARTICLE (PDF): 11


Keywords

  • Automotive Manufacturing,
  • Dynamic Optimization,
  • Hybrid Approaches,
  • Machine Learning Integration,
  • Multi-Objective Optimization

How to Cite

Nainggolan, N., Maghsoudlou, E., AlWadi, B. M., Atamurotov, F., Kosov, M., & Putra, W. (2024). Advancements in Optimization for Automotive Manufacturing: Hybrid Approaches and Machine Learning. International Journal of Industrial Engineering and Management, 15(3), 254–263. https://doi.org/10.24867/IJIEM-2024-3-361

Abstract

This paper addresses the need for innovative optimization solutions in automotive manufacturing. Through advanced algorithms, we review existing methods and introduce novel approaches tailored to this sector. Our literature review identifies gaps and limitations in current methodologies. We define a specific optimization problem within automotive manufacturing, emphasizing its unique challenges. Our key contributions include: (a) Exploring hybrid optimization algorithms, combining genetic algorithms with simulated annealing for a 15% improvement in convergence speed, (b) Integrating machine learning techniques, resulting in a 20% reduction in optimization error compared to static settings, (c) Incorporating multiobjective optimization, achieving a 25% improvement in simultaneous cost and efficiency optimization, and (d) Proposing dynamic optimization algorithms, reducing decision-making latency by 30% during rapid environmental changes. Case studies demonstrate practical application, with quantitative results highlighting the superiority of our approaches over traditional methods. Additionally, the data analysis was conducted using Python, contributing to the robustness and accuracy of our findings.

Article history: Received (March 21, 2024); Revised (April 24, 2024); Accepted (July 3, 2024); Published online (August 23, 2024)

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