Congestion Calculation only by Solving a Linear Programming Model Through Fuzzy Data

The studies on congestion, in the field of either economic or operations research, have been grown in recent years. Calculating and determining congestion have two important aspects. By reducing the input in the decision-making unit, which has experienced congestion, we can also reduce its expenses. Moreover, congestion can lead to a reduction in outputs. By its obviation, output can be increased, and in turn, it can also increase the profits consequently. In this research, congestion of decision-making units in data envelopment analysis was estimated in the presence of fuzzy data. In most of the problems of the real and practical world, the obtained information is not exact, even they are fuzzy. Here, the interesting point is how to calculate congestion of data if they are fuzzy. At this point, congestion can be calculated through triangular fuzzy data using  cut in a particular interval. The suggested model provides an interval solution, which can be used as a guide in decisions making.