Aiming at the maintenance task prediction problem of armored forces, the macro model and micro model are established to analyze the constraint conditions, and the equipment maintenance task prediction model is established in order to meet the motor hours echelon storage. Under the condition of meeting the balance of annual motor hours payments, the motor hours consumed by equipment are allocated according to the annual training tasks, and a hybrid optimization algorithm of improved particle swarm optimization is designed to solve the model, and a case study is carried out on a few vehicles in a certain army. The simulation results show that the model can effectively solve the problem of equipment maintenance task prediction, and a provide reference value for troops to make the maintenance plans.

The characteristics of military equipment maintenance work are analyzed. According to the actual needs of the army, the optimization objective is designed, and a multiobjective flexible maintenance process optimization model is built based on the maintenance business organization process. Combining the advantages of NSGA-II algorithm and the simulated annealing algorithm, this paper proposes a novel improved HNSGSA algorithm, of which algorithm flow is detailed. In accordance with the requirements of the optimization model, this paper also specifically designs the coding methods of the process sequence, the equipment selection and the process scheduling, and the corresponding cross mutation method. The feasibility of the built model is verified by the actual data of maintenance business. And, the superiority, accuracy, and effectiveness of the proposed algorithm are further validated by the comparison with the NSGA-II algorithm and the simulated annealing algorithm, providing a scientific reference for the army to carry out equipment maintenance.