AI framework blends LSTM and MILP for improved supply chain forecasting and optimization

Researchers have developed a novel Hybrid AI Framework for Demand-Supply Forecasting and Optimization (HAF-DS) to improve supply chain efficiency in volatile industries. This framework integrates a Long Short-Term Memory (LSTM) network for demand prediction with a mixed integer linear programming (MILP) model for operational decisions. Experiments demonstrated that HAF-DS significantly reduced forecasting errors and operational costs, leading to lower inventory costs and fewer stockouts. AI

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IMPACT This hybrid approach could enhance efficiency and reduce costs in supply chains facing demand volatility.

RANK_REASON This is a research paper detailing a new hybrid AI framework for supply chain optimization.

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• LSTM
• MILP
• HAF-DS
• arXiv

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