  In chemical and manufacturing processes, unit failures due to equipment
degradation can lead to process downtime and significant costs. In this
context, finding an optimal maintenance strategy to ensure good unit health
while avoiding excessive expensive maintenance activities is highly relevant.
We propose a practical approach for the integrated optimization of production
and maintenance capable of incorporating uncertain sensor data regarding
equipment degradation. To this end, we integrate data-driven stochastic
degradation models from Condition-based Maintenance into a process level
mixed-integer optimization problem using Robust Optimization. We reduce
computational expense by utilizing both analytical and data-based
approximations and optimize the Robust optimization parameters using Bayesian
Optimization. We apply our framework to five instances of the
State-Task-Network and demonstrate that it can efficiently compromise between
equipment availability and cost of maintenance.
