Sequential multi-objective maintenance optimization with dynamic importance ranking: A case study of subsea transportation system

Abstract

Significant failure dependence exists among components of engineering systems, leading to rapid failure propagation and state changes of components. Such compounding dynamics render conventional maintenance strategies prone to inefficiency like over-maintenance, under-maintenance, or maintenance delays. To address the issue of dynamics, this paper proposes a dynamic importance ranking-driven sequential multi-objective maintenance optimization (SMOMO) model. Using a rolling time window approach, the integrated importance of components is dynamically evaluated and ranked based on their real-time states within each window to guide maintenance decision-makings. The window then rolls forward, updating the calculation of long-term expected system cost and availability, based on which a Multi-objective Particle Swarm Optimization (MOPSO) algorithm optimizes maintenance scheduling for cost minimization and availability maximization across the rolling horizon. The proposed methodology is demonstrated and validated using a case study of subsea transportation system. Results illustrate the necessity of dynamic importance evaluation for capturing evolving system status and the effectiveness of the proposed model in optimizing maintenance schedules.