Mission success probability optimization for phased-mission systems with repairable component modules

Mission success probability (MSP) is an important measurement to evaluate the performance of phased-mission systems (PMSs). The PMSs consist of some subsystems in series, and each subsystem is made of some k-out-of-n structures with repairable component modules. Adjusting the reliability and maintainability of components can improve the MSP of repairable PMSs. Therefore, the objective of MSP optimization for PMSs with repairable component modules is to maximize the MSP considering the cost constraints on the adjustment process. The improved optimization algorithm combines the importance measure-based local search method with the ant colony optimization (ACO) algorithm, which is called the importance measure-based ACO (IMACO) algorithm, for solving the MSP optimization. Three numerical experiments are implemented to illustrate the parameter selection and the performance of IMACO algorithm. Comparing with ACO algorithm, the results show that the IMACO algorithm is more effective when the number of component modules is small, while the running time becomes shorter with the increase of the component modules. According to the numerical example of heavy-lift systems with multi-rotor unmanned aerial vehicles (UAVs), the improvement priority of components is analyzed, and the reliability variables of the indispensable components with more missions should be improved at first.