Identification of congestion in the presence of uncontrollable inputs in data envelopment analysis

In traditional data envelopment analysis (DEA), it is assumed that all input is controlled by the manager of units, however, this is not realistic in many applied situations, sometimes there is a situation where the decision maker cannot control some inputs in the measuring efficiency of units, and this causes the desired output to not be produced, and or output is produced at a lower level than expected. It can be stated that in such situations if one or more inputs increases, then this causes the worsening of one or more outputs. Here, the controversial issue is existence of the uncontrollable inputs in the evaluating the efficiency of DMUs, that it may lead to congestion in the units. In the current paper, we propose a new model for identification of congestion in the presence uncontrollable inputs which correctly identifies the effect of the uncontrollable inputs in generating congestion. We show the applicability of the model by using an applied example.