Development of optimization models for automotive production processes: Taguchi and neuro-fuzzy methods at Renault

EUREKA Physics and Engineering, Год журнала: 2025, Номер 3, С. 143 - 151

Опубликована: Май 30, 2025

This research investigates the development and application of advanced optimization techniques mathematical modeling to enhance automotive production processes, using Renault's operations in Morocco as a case study. The study addresses inefficiencies within supply chain by integrating two powerful methodologies: Taguchi method neuro-fuzzy models. approach identifies optimal process parameters, improving consistency, reducing variability, enhancing resource utilization. Simultaneously, model provides adaptability, effectively managing dynamic often unpredictable nature modern chains. combined use these leads significant outcomes, including better efficiency, considerable cost reductions, stronger adherence environmental sustainability principles. Furthermore, hybrid helps minimizing enabling more precise decision-making strategic planning. Beyond emphasizes adaptability efficacy integrated methods, making them highly valuable for broader industrial policy-making applications. findings highlight critical importance leveraging innovative, data-driven strategies shape sustainable, efficient, resilient systems. underscores relevance methods supporting evolution addressing global challenges, reinforcing their role fostering eco-friendly cost-effective solutions across industries. compelling blueprint industry drive lasting improvements foster systemic transformation

Язык: Английский

Recovery methods and application prospects of transition metals from bulk solid waste generated in the phosphorus chemical and vanadium titanium industries

Separation and Purification Technology, Год журнала: 2025, Номер unknown, С. 133836 - 133836

Опубликована: Июнь 1, 2025

Язык: Английский