考虑机内测试诊断的维修任务模型及算法

机内测试设备（Built—in Test Equipment，BITE）在复杂系统中的应用越来越多，由于BITE诊断存在不确定性，就可能导致无效的维修．采用一种考虑BITE诊断不确定性的维修任务选择模型及其求解算法，以在一定置信水平下获得最佳的维修方案．首先，给出了考虑BITE诊断不确定性的维修任务选择问题的假设条件，建立了非线性的、离散的随机机会约束规划模型．其次，设计了求解随机机会约束规划模型的一种基于随机模拟的粒子群算法，包括粒子的表示、适应度函数、更新公式、算法框架等．最后，给出了具体实例，证明了模型与算法的有效性．该模型适用于管理人员在考虑BITE诊断不确定性的情况下做出合理的维修任务选择决策．

Selective Maintenance Model Considering Diagnostics of Built-in Test Equipment and Its Algorithm

Built-in Test Equipment （BITE） is applied more and more to complex systems, but due to uncertainty in diagnostics, ineffective maintenance is inevitable. In order to get an optimal maintenance plan under BITE diagnostic uncertainty and resource constraint, a selective maintenance model considering diagnostic uncertainty of BITE is established,and a resolving algorithm is put forward. Firstly,this paper expounds the model assumption of selective maintenance considering BITE diagnostic uncertainty, and establishes a discrete, nonlinear, chance constrained programming model. Secondly,it designs a simulation based particle swarm optimization algorithm to resolve the model ,and it includes particle representation, fitness function, update methods and algorithm framework. Thirdly, it provides an example to prove the validity and feasibility of the model and resolving algorithm. The model suits managers making feasible decision under BITE diagnostic uncertainty.