###### Abstract  
This paper studies the problem of planning and scheduling in selective maintenance tasks of mission requirements and the health condition of the fleet. In order to deal with the problems of high maintenance cost and long time consumption in maintenance systems, a two-stage fleet maintenance optimization method is proposed. Firstly, a selective maintenance model of fleets based on age reduction is established to maximize the probability of completing the next mission and minimize the maintenance cost. Secondly, a multiobjective sparrow search algorithm is designed to solve the maintenance planning problem in the first stage, and a nondominated solution set of maintenance strategies satisfying the mission constraint is obtained. In the second stage, the simulated annealing algorithm is used to schedule the maintenance task and obtain the minimum maintenance hours required by the maintenance strategy. An example analysis of a vehicle fleet is launched to prove the effectiveness of this method. In a word, this method not only meets the mission requirements but also achieves the purpose of reducing maintenance cost and maintenance hours, which can provide reference for other types of equipment maintenance. maintenance planning: multiobjective optimization; sparrow search algorithm; fleet-level maintenance; simulated annealing algorithm +
Footnote †: journal: Applied  
0000-0001-9999-22000-0000-0000-0000-0000-0000-0000-0000-0000-0000  
strategy adjusts the baseline scheduling in a reasonable computational time and avoids the unnecessary scheduling. [PERSON] et al. [7, 8] managed to reduce the waiting time, makespan and electricity consumption in complex and customized manufacturing by proposing a self-adaptive collaboration control method. By monitoring the health condition of each key component in complex equipment in real time, judging whether the system has a symptom of failure based on the understanding of failing mechanism and evaluating the approximate occurrence time of system failure, the maintenance team can make flexible maintenance arrangements according to the health status of the system and make adequate preparations before maintenance tasks. Scheduled maintenance and other maintenance strategies do not consider the dynamic change in equipment health condition, and some key components cannot be properly maintained, resulting in a waste of maintenance resources and mission risks. Therefore, the condition-based maintenance strategy based on real-time monitoring and evaluation of equipment health condition has been gradually focused on [9, 10]. [PERSON] et al. [11] studied the condition-based maintenance strategy for industrial facilities consisting of multiple units working in parallel. [PERSON] et al. [12] constructed a condition-based maintenance model based on piecewise deterministic Markov process and considered the imperfect maintenance of units with natural degradation. According to the degree of repair, maintenance strategies can be divided into perfect maintenance, minimal maintenance, imperfect maintenance, etc. [13] Among them, the imperfect maintenance cannot make the system as good as a new one, but its health condition can be rendered better than before. [PERSON] et al. [14] proposed a two-stage preventive maintenance policy that considered imperfect maintenance, and the defect will finally be removed by replacement in the second stage. [PERSON] et al. [15] established a fleet-level selective imperfect maintenance model and proposed a maintenance strategy optimization method based on evolutionary competition game theory. [PERSON] et al. [16] simultaneously considered three maintenance behaviors: no maintenance, imperfect maintenance and replacement. They proposed an imperfect model by introducing the degradation ratio that follows the beta distribution and used the MDP method to solve the optimal maintenance problem.