A Novel Multi-stage Stochastic Formulation with Decision-dependent Probabilities for Condition-based Maintenance Optimization

摘要

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.