Optimization of design and maintenance strategy for enhanced availability and life cycle cost efficiency: A combinatorial approach using multi-objective optimization and discrete event simulation

In today’s competitive industrial environment, maximizing the Operational Availability (OA) of complex systems while minimizing their Life Cycle Cost (LCC) is a key challenge. This paper proposes an innovative methodology that jointly integrates design and maintenance strategies to enhance the performance of complex, multi-component, and repairable systems. The proposed approach integrates several parameters: reliability, redundancy, maintainability, logistics, and diagnostics. It is based on a dynamic planning of maintenance operations, guided by the concept of Maintenance-Free Operating Period (MFOP). The proposed approach accounts for multiple interrelated parameters, including reliability, redundancy, maintainability, logistics, and diagnostics. It is structured around a dynamic maintenance planning framework, guided by the Maintenance-Free Operating Period (MFOP) concept to ensure interruption-free operation over a specified duration with a defined confidence level. To achieve these objectives, a hybrid computational approach is developed, combining Monte Carlo simulation (MCS), discrete-event simulation (DES), and the multi-objective evolutionary algorithm (NSGA-II). MCS generates stochastic failure and repair durations, while DES models the system’s dynamic behavior to assess operational availability and associated costs. The methodology generates a set of Pareto-optimal solutions, enabling decision-makers to evaluate balanced trade-offs between OA and LCC, optimized via NSGA-II. The results demonstrate the efficacy of joint design-maintenance optimization in addressing the challenges of complex systems.