Identifying the specific scale status by evaluating scale efficiency of each DMU (decision making unit) is critical for policy makers to make a correct decision on inefficiency improvement. Although some traditional DEA (Data Envelopment Analysis) models can evaluate different scale efficiencies and identify the specific status of each DMU, they are unable to deal with the evaluation indicators with imprecise observation. Therefore, we propose two different uncertain DEA models with imprecise inputs and outputs to recognize two scale status of increasing returns to scale (IRS) and decreasing returns to scale (DRS), which can better help policy makers make a correct decision on inefficiency improvement without accurate data. Moreover, we analyse the stability of these uncertain DEA models to make effective suggestions for decision makers to further improve the inefficient DMUo or to adjust the inputs and outputs on the premise of remaining the new DMUoefficient.
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