Predict Then Optimize

Predict-then-optimize (PTO) frameworks integrate machine learning prediction with downstream optimization problems, aiming to improve decision-making by leveraging contextual information to predict parameters before solving the optimization. Current research focuses on developing more efficient and accurate end-to-end training methods, often employing neural networks and novel loss functions designed to directly minimize decision-making regret, rather than relying on separate prediction and optimization stages. This approach holds significant promise for improving the efficiency and effectiveness of decision-making in various applications, from resource allocation and scheduling to personalized recommendations and supply chain management.