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Abstract 
In this paper، we deal with fuzzy random variables for inputs and outputs in Data Envelopment Analysis (DEA). These variables are considered as fuzzy random flat LR numbers with known distribution. The problem is to find a method for converting the imprecise chance-constrained DEA model into a crisp one. This can be done by first، defuzzification of imprecise probability by constructing suitable membership function، second، defuzzification of the parameters which has been done using an ?-cut and finally، converting the chance-constrained DEA into a crisp model using the method of Cooper