Stocking strategy for spare parts has a pivotal influence on the productivity and efficiency of industrial plants. Considering the situation of lacking historical statistics causing by uncertainty, this paper proposes a method to optimize spares varieties based on uncertainty theory and uncertain data envelopment analysis (DEA) model. Firstly, a recursive hierarchy structure is established to construct an evaluation system to meet the requirement of avoiding attributes’ redundancy. Then, an uncertain spares optimization model (USOM), which is based on uncertain DEA model, is developed to optimize spares varieties. Furthermore, the uncertainty theory is utilized to convert the USOM into an equivalent deterministic model for simplification. Finally, a numerical example is given to illustrate the performance of this model. The results show that the stocking strategy obtained from the proposed decision model can satisfy the purpose of saving resources and prompting continuous operation.
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