<![CDATA[This research focuses on enhancing supply chain network design through the integration of hybrid artificial intelligence (AI) and real-time data for dynamic optimization. The objective is to develop a mathematical formulation and model that minimize costs while meeting demand and capacity requirements. The research proposes the integration of hybrid AI techniques, such as machine learning and optimization algorithms, with real-time data to enable data-driven decision-making and adaptability to changing market conditions. The implementation involves collecting and processing real-time data from various sources and utilizing AI algorithms to optimize facility locations, transportation routes, and inventory allocation. A numerical example demonstrates the application of the model, showcasing cost savings and improved customer service. However, the research has limitations, including simplified assumptions, data quality concerns, scalability challenges, and the limited scope of considered factors. Despite these limitations, the findings highlight the potential benefits of integrating hybrid AI and real-time data in supply chain network design, offering insights for practitioners and future research directions.]]>
Copyrights © 2019
