Research on the task sequencing of battlefield equipment emergency repair for synthetic brigades based on GWO-BP algorithm

Addressing the limitations of traditional battlefield equipment repair task scheduling models, such as the lack of adaptive learning capabilities and the subjective and experience-based determination of indicator weights, this study proposes a repair task scheduling model based on the GWO (Grey Wolf Optimization) algorithm combined with a BP neural network. First, a task scheduling indicator system comprising 11 indicators is constructed from three dimensions: repair tasks, repair teams, and battlefield environments; Second, the GWO algorithm is used to optimize the weights and thresholds of the BP neural network, avoiding getting stuck in local optima; finally, the network is trained using synthetic brigade exercise data to obtain the optimal model. Experimental results show that the GWO-BP model significantly reduces prediction errors compared to the BP model, enabling precise prioritization of repair tasks and providing an objective and efficient solution for battlefield equipment repair decision-making in synthetic brigades.