Eco-Conscious Master Production Scheduling Under Dual Uncertainty: A Hybrid Approach Using Machine Learning and Stochastic Optimization

<title>Abstract</title> <p>The inherent weaknesses of conventional, deterministic production scheduling have been exposed by the current European energy crisis and the growing integration of renewable sources, which have brought high-frequency volatility and negative price occurrences into day-ahead power markets. This study suggests an integrated, data-driven "predict-and-optimize" methodology to overcome the concurrent uncertainties of severe energy price swings and non-stationary consumer demand. We create a hybrid machine learning pipeline that uses a Long Short-Term Memory (LSTM) deep learning network to capture intricate, non-linear energy pricing dynamics and LightGBM with quantile regression for probabilistic demand forecasting. A Kantorovich distance-based scenario reduction approach is used to optimally condense these continuous prediction distributions after they have been discretized using Monte Carlo sampling. A Two-Stage Stochastic Mixed-Integer Linear Programming (MILP) model is directly fed the resultant scenario tree. The suggested framework exhibits strong economic and environmental resilience after being validated on a multi-product manufacturing plant taking part in the European EPEX SPOT market. The Value of the Stochastic Solution (VSS) is 34.3%, according to computational results, which successfully minimizes predicted operating expenses. The stochastic approach takes advantage of negative power prices by proactively implementing dynamic load shifting and intentional overstocking, while eschewing stringent Just-In-Time (JIT) restrictions. Additionally, the facility's Scope 2 greenhouse gas emissions are naturally reduced by this strictly economic drive of cost reduction. In the end, this study effectively bridges the gap between stochastic mathematical programming and predictive sequence learning, providing supply chain professionals with a scalable solution to manage the current green energy transition.</p>