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Planning, scheduling, and control typically constitute separatedecision-making units within chemical companies. Traditionally, theirintegration is modelled sequentially, but recent efforts prioritize lower-levelfeasibility and optimality, leading to large-scale, potentially multi-level,hierarchical formulations. Data-driven techniques, like optimality surrogatesor derivative-free optimization, become essential in addressing ensuingtractability challenges. We demonstrate a step-by-step workflow to find atractable solution to a tri-level formulation of a multi-site, multi-productplanning-scheduling-control case study. We discuss solutiontractability-accuracy trade-offs and scaling properties for both methods.Despite individual improvements over conventional heuristics, both approachespresent drawbacks. Consequently, we synthesize our findings into a methodologycombining their strengths. Our approach remains agnostic to the level-specificformulations when the linking variables are identified and retains theheuristic sequential solution as fallback option. We advance the field byleveraging parallelization, hyperparameter tuning, and a combination of off-and on-line computation, to find tractable solutions to more accuratemulti-level formulations.

Hierarchical planning-scheduling-control -- Optimality surrogates and derivative-free optimization