<ul><li>This paper introduces PROPEL, a framework that combines optimization with supervised and Deep Reinforcement Learning (DRL) for large-scale Supply Chain Planning (SCP) optimization problems.</li><li>PROPEL uses supervised learning to identify variables fixed to zero in the optimal solution, and DRL to select which fixed variables must be relaxed to improve solution quality.</li><li>The framework has been applied to industrial SCP optimizations with millions of variables, leading to significant improvements in solution times and quality.</li><li>The computational results show a 60% reduction in primal integral, an 88% primal gap reduction, and improvement factors of up to 13.57 and 15.92, respectively.</li></ul>

PROPEL: Supervised and Reinforcement Learning for Large-Scale Supply Chain Planning

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