Intelligent Decision-Making for Maintenance Assurance throughout the Lifetime of Helicopter Swarms

The utilization of intelligent algorithms for decision-making and optimization in the collaborative maintenance of helicopter swarm addresses the challenge of maximizing the reliability of swarm task execution under limited maintenance resources. Consequently, optimization of maintenance decisions has become a focal point of research both domestically and internationally. The problem of allocating maintenance resources under resource constraints, known as selective maintenance, can naturally be modeled as a Markov decision process. For solving this problem, employing multi-objective optimization algorithms is a logical choice. However, due to the large number of optimization decision variables involved in this practical problem, traditional algorithms may encounter difficulties. Reducing the search space of decision variables can alleviate this challenge. With this in mind, this study proposes a multi-objective optimization algorithm based on fuzzy theory. Finally, we validate the feasibility of the model and intelligent decision-making solutions through an illustrative example.