Condition-based maintenance optimization via stochastic programming with endogenous uncertainty

In this work we address the challenge of integrating production planning and maintenance optimiza-tion 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 uncer-tain 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 differ-ent 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. (c) 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ )

Automated summary

This work addresses the challenge of integrating production planning and maintenance optimization for a process plant by adopting a stochastic programming formulation with decision-dependent uncertainty. The uncertain predictions of equipment degradation and remaining useful time of the plant are modeled using the Cox model, embedded into the optimization problem. Primal decomposition algorithms are suggested to decompose the MINLP formulation and compared to a global solver for performance evaluation.