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In the weapon system development and design process, support vector machine was brought forward to implement the tradeoffs between system performance parameters and cost to effectively control system cost and enhance cost benefit. In current adopted methods, the analysis process of iterative methods is too complex, whereas small perturbation methods adopt excessive approximate means to assure the statistic linear relationship between performance parameters and cost, which is hard to be insured in the practical application. Support vector machine has a favorable adaptability to the modeling of nonlinear system and can disposal the small sample problem in weapon system cost analysis, which can solve some practical difficulties existing in cost analysis. According to the essence of tradeoffs between weapon system performance parameters and cost, this paper discussed the general process of tradeoff analyses and validated the practicability and effectivity of the adopted method through the application analysis on a case. Both theoretical analyses and practical applications show that the system identification model established by support vector machine has the satisfying accuracy and generalization capability, and the optimal performance parameters can be obtained by adopting Davidon least square methods (DLS). The results are satisfying.

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Various missions significantly impact the fleet’s level repair plan. The mission requirement model is established based on the “regular and dual control” repair mode. Considering the fleet’s overall operation, capability decay characteristics, and planner’s expectations and preferences, the fleet’s capability evaluation model is constructed. Furthermore, aiming at the maximum capability value and the minimum number of the mission ships and comprehensively considering constraints such as sailing rate, mission period, repair period, repair cost, capability restoration, and mission capability, the optimization model of the fleet’s level repair plan for diverse missions is established. The particle swarm optimization algorithm based on the hierarchical sequence method is used to solve the model, and the fleet’s level repair plan and mission configuration plan are obtained, which solves the coordination problem of the two types of plans. The results show that, compared with the traditional planning method, this method can fully consider the actual requirements of diverse missions and has better coordination of the relationship between the ship’s use and repair. This method can provide strong technical support for the scientific preparation of level repair plans and the effective completion of combat readiness training missions.

For ship equipment turnover spare parts, if the maintenance interval is too long, the safety and working ability will be reduced; if frequent maintenance is performed, it will cause much waste. Therefore, it is necessary to determine the appropriate maintenance interval for resource optimization. The article analyzes the factors of turnover spare parts maintenance resource optimization. It establishes an equipment parts maintenance time resource optimization model based on maintenance theory and multi-objective decision-making methods, which can ensure the familiar training environment, maintenance type, and update type preventive maintenance mode The satisfaction is the largest, and group decision making and improved genetic algorithm are used to solve the optimal satisfaction. Finally, the effectiveness of the model is verified with examples of ship equipment spare parts.