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

Abstract

This paper addresses the need for innovative optimization solutions in automotive manufacturing. Through advanced algorithms, we review existing methods and introduce novel ap- proaches 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 ap- plication, 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.

Keywords

• automotive manufacturing
• dynamic optimization
• hybrid approaches
• machine learning integration
• multi-objective optimization