Causal Forest-Guided Partitioning for Robust Operation Optimization: A Case Study on Draw Ratios Allocation

The draw ratios allocation critically determines the final performance of carbon fiber. However, the stochastic nature in component fluxes and concentrations variations introduces uncertainty into draw ratios, causing frequent parameter fluctuations. Therefore, we propose a robust operation optimization framework that formulates a biobjective model to minimize linear density and maximize strength. Within this framework, a causal forest-guided partitioning for robust operation optimization algorithm is developed to solve the model. Specifically, the algorithm uses perturbations on individual decision variables to form treatment groups from an initial control population, enabling causal forests to infer the overall causal effects of each decision variable on the objectives. Next, the computed mean conditional average treatment effects are clustered via K-means to partition the variables by their robustness relevance. Then, a tailored optimization strategy, augmented by an external archiving mechanism, is performed on each group to efficiently search for the robust Pareto optimal set. Finally, adaptive utopian point-based decision making is used to determine the optimal setpoint. The proposed framework is validated on benchmark problems and in simulation study, achieves a 29.93% reduction in linear density and 99.41% increase in strength, thereby confirming its practical applicability.