Leveraging AI Techniques to Innovate Decision Theory Models for Enhanced Decision-Making in Complex Systems

This article explores the application of artificial intelligence techniques to enhance decision-making in complex systems, specifically within Iran's petrochemical industry. We present a comprehensive decision support model that integrates operational data, financial metrics, market demand forecasts, and regulatory compliance information. Through systematic data collection, transformation, and analysis, our model optimizes production scheduling while minimizing costs and ensuring adherence to environmental standards. By employing linear programming and predictive analytics, we demonstrate how stakeholders can make informed decisions that align production capabilities with market dynamics. This research highlights the critical role of AI technologies in providing scalable solutions to the multifaceted challenges of modern industrial operations, ultimately fostering sustainable practices and improved economic outcomes. The findings underscore the potential for advanced decision theory models to drive innovation and resilience in complex systems, paving the way for future advancements in data-driven decision-making strategies.

کلیدواژه ها:

Decision Theory ، Model ، Complex System ، Artificial Intelligence