Integrating Deep Learning Demand Forecasting with Multi-Objective Optimization for Circular Coffee Supply Chains: A Data-Driven Framework for Cost, Emissions, and Freshness Management
Researchers have developed a novel framework to optimize the complex coffee supply chain by integrating deep learning for demand forecasting with multi-objective optimization. The system uses a CNN-LSTM model to predict demand, achieving a high R^2 score of 0.90, which then informs a mixed-integer linear programming model. This optimization aims to simultaneously minimize costs, reduce carbon emissions, and maximize product freshness within a circular economy model. AI
IMPACT This integrated AI approach could significantly improve efficiency and sustainability in complex agri-food supply chains.