This paper proposes a novel hybrid approach combining Random Forest machine learning techniques with Genetic Algorithms to solve the order acceptance and scheduling problem with past-sequence-dependent setup times. The Random Forest component helps predict optimal scheduling patterns, while the Genetic Algorithm optimizes the overall solution. Experimental results demonstrate superior performance compared to traditional methods in terms of computational efficiency and solution quality.
