inverse model in fuzzy outputs estimation. dea approach

In this paper, we use a selection of validation units (dmus) that have specific properties which are degraded using case analysis and contemporary cover models. with the approach mentioned, we work with models, defines classical coverage and also improves other inefficient units, we enter a decision unit (dmu) and we also increase its value on using thebenchmarking method introduced in this article and assuming that the unit work is fixed or upgraded. we further assume that the problem data is fuzzy and introduce the fuzzy inverse data envelopment analysis model(fidea) and use the α-cut principle and possible programming and fuzzy number ranking to turn the relevant model into a definite programming model. for this we determine the amount of efficiency with data envelopment analysis models (bcc model) and then we estimate the increase in outputs based on the increase in inputs using the model. a numerical example is given at the end to ensure the correctness of the proposed approach.