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Combined with the engineering requirements for the optimal allocation of the current ammunition equipment spare parts, a spare part optimization model based on genetic algorithm was established. With the combination of good readiness and cost of ammunition equipment, the use of genetic algorithm had the characteristics of fast convergence, strong global optimization, and simple programming. The model was solved and the spare parts of ammunition equipment were optimally configured. Finally, an example of ammunition equipment was analyzed. The results show that the genetic algorithm can effectively solve the optimization problem of ammunition equipment spare parts.

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Failure mode effects and criticality analysis (FMECA) is a commonly adopted approach to defining, assessing, and reducing possible failures in designs, systems, processes, products, and services. Traditional FMECA ranks the failure modes of products based on a risk priority number (RPN), which is obtained by multiplying the risk elements. Conventional FMECA has the shortcomings of badly handling unknown information and unreasonably assessing RPNs. To deal with these issues, an advanced FMECA method based on intuitionistic 2-tuple linguistic variables (I2LVs) and the triangular fuzzy analytic hierarchy process (TFAHP) is proposed. In this method, the fuzzy evaluation of risk elements given by different FMECA members is represented by I2LVs, which can efficiently handle unknown information. The TFAHP method is adopted to assess the weights of risky elements and rank the risk priorities of different failure modes. Finally, an application case of an insulated-gate bipolar transistor is used to verify the effectiveness and robustness of the proposed method.