مدل بهینه سازی چند هدفه غیر خطی برنامه ریزی تولید بر اساس منطق فازی و یادگیری ماشین

نوع مقاله : مقاله پژوهشی

نویسندگان

1 استادیار، گروه مهندسی صنایع، واحد تهران مرکزی، دانشگاه آزاد اسلامی، تهران، ایران

2 گروه مهندسی صنایع، واحدساری، دانشگاه آزاد اسلامی، ساری، ایران

10.22091/jemsc.2024.11186.1197

چکیده

این تحقیق به معرفی یک مدل بهینه‌سازی چندهدفه غیرخطی می‌پردازد که برای بهینه‌سازی هم‌زمان سود و رضایت مشتری در سیستم‌های تولیدی طراحی شده است. مسأله مورد بررسی شامل بهینه‌سازی در شرایط پیچیده و نامطمئن تولید است که با محدودیت‌های منابع و زمان مواجه است. مدل پیشنهادی با به‌کارگیری توابع هدف غیرخطی و تحلیل دقیق شرایط عملیاتی، راه‌حل‌های بهینه‌ای را برای مدیران ارائه می‌دهد. این منطق فازی با الگوریتم‌های یادگیری ماشین نظیر شبکه‌های عصبی و یادگیری تقویتی ترکیب شده است تا مدلی هوشمند و انعطاف‌پذیر ایجاد شود که به‌طور مؤثری با تغییرات ناگهانی در محیط‌های پویا سازگار می‌شود. این مدل از ترکیب الگوریتم‌های ژنتیک مرتب سازی غیر مسلط چهارم (NSGA-IV ) و شبکه انتخاب متغیر (VSN) در یک چارچوب ترکیبی بهره می‌برد و رویکردی پیشرفته و چندوجهی برای حل مسائل پیچیده بهینه‌سازی چندهدفه ارائه می‌کند. نتایج پارتو-بهینه حاصل از این مدل نشان‌دهنده عملکرد کارآمد و بهینه آن است. مدل پیشنهادی می‌تواند به‌عنوان منبعی عملی و راهبردی برای مدیران و تصمیم‌گیران در بهینه‌سازی تولید و ارتقاء رضایت مشتری در شرایط نامطمئن و پویا مورد استفاده قرار گیرد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Non-linear multi-objective optimization model of production planning based on fuzzy logic and machine learning

نویسندگان [English]

  • zahra Saeidi Mobarakeh 1
  • hossein amoozadkhalili 2
1 Department of Industrial Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran
2 Department of Industrial Engineering, Sari Branch, Islamic Azad University, Sari, Iran
چکیده [English]

This research introduces a nonlinear multi-objective optimization model that is designed to simultaneously optimize profit and customer satisfaction in production systems. The investigated problem includes optimization in complex and uncertain conditions of production, which is faced with resource and time limitations. The proposed model provides optimal solutions for managers by using non-linear objective functions and detailed analysis of operating conditions. This fuzzy logic is combined with machine learning algorithms such as neural networks and reinforcement learning to create an intelligent and flexible model that effectively adapts to sudden changes in dynamic environments. This model uses the combination of non-dominant fourth sorting genetic algorithms (NSGA-IV) and variable selection network (VSN) in a hybrid framework and provides an advanced and multi-faceted approach to solving complex multi-objective optimization problems. Pareto-optimal results obtained from this model indicate its efficient and optimal performance. The proposed model can be used as a practical and strategic source for managers and decision makers in optimizing production and improving customer satisfaction in uncertain and dynamic conditions.

کلیدواژه‌ها [English]

  • Multi-objective optimization
  • fuzzy logic
  • machine learning
  • hybrid multi-objective meta-heuristic algorithm

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