A method for detecting strong and weak congestion in Data Envelopment Analysis

Congestion is a branch of data envelopment analysis (DEA) that is used for a large part of science, such as engineering, traffic, and economics. Increasing concerns about increasing congestion value can have a negative impact on output, and worrying about The ratio of profit to cost and return on investment in relation to other projects and there is a policy to deal with these problems . The congestion value is important for evaluating the performance and proper allocation of resources, and its determination using DEA has been considered by Researchers. Because, the reduces efficiency and effectiveness. From the management point of view, when one or more inputs increase, one or more outputs are reduced, so that other inputs and outputs remain unaltered, congestion occurs or vice versa. Unlike other common methods, this considered density constraints only to reduce inputs. In this paper, the topic of congestion is studied both with input decreases and with increasing output. Based on new definitions in this study, linear models are presented to identify strong and weak condensation. The proposed models for detecting a strong and poor congestion have this feature that, in the event of a weak and strong congestion, indicate which input indicators will cause the decision-making unit density, how much the density of the unit is, and also if the density is eliminated Which component of the output vector increases and how much it increases.

Keywords:

Data envelopment analysis , Congestion , Efficiency , Inefficiency