Stochastic Optimization of Maintenance and Operations Schedules Under Unexpected Failures

We develop a stochastic optimization framework for integrated condition-based maintenance and operations scheduling for a fleet of generators with explicit consideration of unexpected failures. Our approach is based on a model that uses condition-based failure scenarios derived from the remaining lifetime distributions of the generators, as well as a chance constraint to ensure a reliable maintenance plan. We derive a deterministic safe approximation of the difficult chance constraint. The large number of failure scenarios is handled by a combination of sample average approximation and an enhanced scenario decomposition algorithm in a distributed framework. We introduce a number of algorithmic improvements by exploiting the polyhedral structure of the problem, utilizing its time decomposability, and an analysis of the transmission line capacities. Finally, we present a case study demonstrating the significant cost savings and computational benefits of the proposed framework.