Selective maintenance optimization for fuzzy multi-state systems

This paper addresses a selective maintenance optimization problem for a fuzzy multi-state system composed of fuzzy multi-state elements. Due to insufficient data and unpredictable external working conditions, both the performance capacity and states transition intensities of multi-state elements ca nnot be known precisely, but are represented by fuzzy numbers. Additionally, both the durations of a break and a succeeding mission are also treated as fuzzy values. To maximize the fuzzy probability of a system successfully completing a succeeding mission, a selective maintenance model is proposed to identify an optimal subset of maintenance activities to be performed on some elements in the system. A solution algorithm containing three rules to eliminate inferior solutions and narrow down elements' states combinations is proposed to resolve the new selective maintenance model in a computationally efficient manner. An illustrative example of an archibald is presented to demonstrate the effectiveness of the proposed model.