Data Envelopment Analysis (DEA) is a mathematical technique to assess the performance of Decision Making Units (DMUs) with similar inputs and outputs. The traditional DEA models disregard the internal structure of units and have a “black box” view. Thus, to
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Data Envelopment Analysis (DEA) is a mathematical technique to assess the performance of Decision Making Units (DMUs) with similar inputs and outputs. The traditional DEA models disregard the internal structure of units and have a “black box” view. Thus, to evaluate the structures with more than one stage, the network DEA (NDEA) models expanded. On the other hand, the dynamic optimization models have been presented to eliminate the limitations of static models in optimization. In the article, for the first time, a systematic approach is used to present a dynamic NDEA with constant inputs and undesirable outputs. First, we used an axiomatic approach in DEA with undesirable output and presented an NDEA model with undesirable output. Then, we extended the proposed approach and presented a dynamic NDEA with undesirable output and a constant input. Afterward, we applied this model to evaluate hospitals’ performance in an experimental study to estimate the efficiency of their dynamic network.
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