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In chemical and manufacturing processes, unit failures due to equipmentdegradation can lead to process downtime and significant costs. In thiscontext, finding an optimal maintenance strategy to ensure good unit healthwhile avoiding excessive expensive maintenance activities is highly relevant.We propose a practical approach for the integrated optimization of productionand maintenance capable of incorporating uncertain sensor data regardingequipment degradation. To this end, we integrate data-driven stochasticdegradation models from Condition-based Maintenance into a process levelmixed-integer optimization problem using Robust Optimization. We reducecomputational expense by utilizing both analytical and data-basedapproximations and optimize the Robust optimization parameters using BayesianOptimization. We apply our framework to five instances of theState-Task-Network and demonstrate that it can efficiently compromise betweenequipment availability and cost of maintenance.

Data-driven optimization of processes with degrading equipment