Joint Multimission Selective Maintenance and Inventory Optimization for Multicomponent Systems Considering Stochastic Dependency

Studies on maintenance and inventory optimization have been frequently combined to cut the total operation and maintenance costs of multicomponent systems. Most existing studies assume that components are stochastically independent and only collaborate on inventory management-related resources. In practice, stochastic dependencies exist in most complex systems, and limited maintenance time becomes a crucial resource shared by all components during multimission selective maintenance (SM). Neglecting these features reduces the practicality of policies. To address this limitation, we investigate joint multimission SM and inventory optimization for systems considering stochastic dependency among components. First, an extended factor analysis model incorporating the effects of working conditions is proposed, based on which diverse and dependent degradation processes of components under multiple missions can be well characterized. Then, the sequential optimization of joint multimission SM and inventory policies, which consider information about component degradation states, available resources, and mission profiles simultaneously, is developed using a continuous-state Markov decision process. Decision variables are optimized by an efficient reinforcement learning algorithm. Conclusively, the superiority of the proposed method is illustrated using a numerical example of a photovoltaic system.