Joint optimization of selective maintenance decision-making and maintenance personnel allocation under limited resources

Integrating selective maintenance strategies with personnel allocation for equipment groups is essential to meet combat missions' demands and significantly boost overall combat effectiveness. Accordingly, this study aims to maximize the probability of mission completion for equipment groups by developing a joint optimization model under constrained resource conditions. An environmental coefficient is incorporated to represent the dynamic impact of varying combat environments on the degradation states of individual units. Using a nonhomogeneous Markov model, this study calculates the state transition probabilities of units throughout the mission to derive the mission completion probabilities for both the equipment group and the overall combat cycle. To solve this model, an adaptive quantum immune algorithm is applied to the case study. These findings demonstrate that the proposed model and algorithm enhance maintenance decision-making quality and clarify optimization patterns regarding resource efficiency and dynamic personnel allocation. Thus they offer both a theoretical foundation and practical guidance for battlefield maintenance support.