Condition-based maintenance optimization via stochastic programming with endogenous uncertainty

In this work we address the challenge of integrating production planning and maintenance optimization for a process plant. We consider uncertain predictions of the equipment degradation by adopting a stochastic programming formulation with decision-dependent uncertainty. The probability of the uncertain parameters, in this work the remaining useful time of the plant, depends on the operating conditions of the plant which is modeled by embedding a prognosis model, the Cox model, into the optimization problem. A separation of the variables is suggested to decompose the MINLP formulation via two different primal decomposition algorithms. We provide computational results and compare the performance of the proposed decompositions with the global solver BARON enhanced with a custom branching priority strategy.