Data envelopment analysis (DEA), as a useful management and decision tool, has been widely used since it was first invented by Charnes et al. in 1978. On the one hand, the DEA models need accurate inputs and outputs data. On the other hand, in many situations, inputs and outputs are volatile and complex so that they are difficult to measure in an accurate way. The conflict leads to the researches of uncertain DEA models. This paper will consider DEA in uncertain environment, thus producing a new model based on uncertain measure. Due to the complexity of the new uncertain DEA model, an equivalent deterministic model is presented. Finally, a numerical example is presented to illustrate the effectiveness of the uncertain DEA model.

Epistemic uncertainty exists in system availability evaluation due to the lack of data and information. To address it, this article proposes a series of definitions of the uncertainty theory-based availability, called belief availability, expanding the scope of belief reliability by introducing uncertainty-measured logistics and maintenance into belief reliability. Based on an uncertain alternating renewal process, we construct a belief availability model for repairable systems subject to the epistemic uncertainty. From the model, we derive formulas of several belief availability metrics, including belief availability (inherent, achieved, and operational), delay time ratio, maintenance time ratio, and belief failure frequency. We find an interesting property that the states order or the initial state in the model will not influence these metrics. A case study about the oxygen generation system (OGS) on the international space station was conducted to analyze the impact of its working, logistics, and maintenance time on the OGS belief operational availability. The results show a potential application in belief availability tradeoff between the OGS's availability-related parameters. In addition, we compared the proposed availability with probability one based on the time distributions of the OGS states, illustrating our method can effectively reduce the deviation of availability evaluation with insufficient data.