## 3 Mission and Reliability Driven Fleet Selective Maintenance Planning Two-Stage Method
### Algorithm Design
#### 3.2.2 A Simulated Annealing Algorithm Used to Solve Maintenance Scheduling
The specific displacement rule is to randomly exchange the maintenance order of two components;  
Step 4: After each displacement, calculate the total maintenance time corresponding to the new solution according to the Equations (26)-(29), and then perform the acceptance judgment according to the admission rules described in Equation (30);  
Step 5: After the temperature gradually drops to the preset value of 0.1, jump out of the annealing cycle;  
Step 6: Repeat the above steps several times to explore the algorithm performance under different initial conditions;  
Step 7: Find the minimum total maintenance time and output the corresponding maintenance schedule. Process of Selective Maintenance Planning for Equipment Cluster Based on Two-Stage Algorithm Driven by Task and Reliability  
According to the solution framework shown in Section 3.1, this paper establishes a two-stage algorithm-based selective maintenance planning method process for equipment clusters driven by tasks and reliability, which mainly includes the following three steps:  
Step 1: Analyze the structure of the equipment system to determine the reliability indicators of the components in the system and the maintenance hours and costs required for different maintenance strategies. The virtual age model is used to describe the effects of maintenance activities on the health state of components in each equipment system of the mission profile and to determine the survival probability of the components in the mission. Step 2: According to the logical relationship of the components in the equipment system, the mission reliability of the equipment system is obtained, the mission satisfied capability of the fleet is determined by combining the survival probability of the components in the task, and a multiobjective selective maintenance model of the fleet is established with the goal of maximizing the mission satisfied capability and minimizing the maintenance cost. Step 3: Taking the maintenance cost and the mission satisfied capability of the fleet in the mission profile as objectives, the multiobjective selective maintenance model is solved by using the multiobjective sparrow search algorithm, and a series of nondominated maintenance strategy solution sets are obtained. With the maintenance time as the target, the SA algorithm is used to optimize the solution set of the maintenance strategy and obtain the maintenance schedule with the shortest maintenance time in the next mission. In conclusion, the selective maintenance planning algorithm flow of the two-stage equipment group is shown in Figure 4.  
In this section, several fleets composed of certain vehicle equipment systems are taken as examples, and the selective maintenance planning method of the fleet based on the two-stage algorithm proposed in this chapter is verified and analyzed. Assume that the mission requires that all subsystems work properly and that subsystems fail independently of each other. As the actual structure of the equipment system is complex and there are many components, for the sake of simplicity, each subsystem can be regarded as a structure composed of several key components in parallel, and the system reliability block diagram is shown in Figure 5.