Prognostic-based Maintenance Optimization in Complex Systems with Resource Limitation Constraints

This paper is concerned with prognostic information for maintenance optimization in complex systems. At each stage of such a system, we consider redundant components used as backup to guarantee the system's availability. The Remaining Useful Life (RUL/prognostic information) of components is used to evaluate each component's redundancy. We address RUL-based maintenance optimization under resource limitation to ensure the availability of the system such that production demands can be satisfied in a given maintenance planning horizon. We propose a mixed-integer linear programming approach to minimize the overall cost. Our numerical results on test instances show the efficiency of the proposed approach to attain optimal solutions.