Multi-stage stochastic optimization with decision-dependent probabilities for maintenance
A novel multi-stage stochastic formulation with decision-dependent probabilities for condition-based maintenance optimization. Originally presented at 30th European Symposium on Computer Aided Process Engineering (ESCAPE30). E. Leo, S. Engell
Abstract:
The challenge addressed in this work is the integrated production planning and condition-based maintenance optimization for a process plant. We take into account uncertain information of the predicted equipment degradation adopting a stochastic programming formulation. To adjust the likelihood of the failure scenarios, we embed a prognosis model, the Cox model, into the optimization problem. We propose here a novel endogenous uncertainty formulation where the decisions at one point in time have an impact on the probability of the uncertainty. We provide computational results implementing a custom branching within the global solver BARON and decomposing the problem via the Benders algorithm.