data envelopment analysis (dea) is a nonparametric technique that includes models to evaluate the relative efficiency of decision making units (dmus). it has the ability to separate efficient units and inefficient units. one of the applications of this mathematical technique is evaluating performance of supply chain. according to such as the inefficiency factors of one dmu, is existence congest...

Traditional DEA models do not deal with imprecise data and assume that the data for all inputs and outputs are known exactly. Inverse DEA models can be used to estimate inputs for a DMU when some or all outputs and efficiency level of this DMU are increased or preserved. this paper studies the inverse DEA for fuzzy data. This paper proposes generalized inverse DEA in fuzzy data envelopment anal...

The inverse Data Envelopment Analysis (InvDEA) is an exciting and significant topic in the DEA area. Also, uncertain data various real-life applications can degrade efficiency results. current work addresses InvDEA presence of stochastic data. Under maintaining score, inputs/outputs-estimation problem investigated when some or all its outputs/inputs increase. A novel optimality concept for mult...

this paper proposes a new approach for determining efficient dmus in dea models using inverse optimi-zation and without solving any lps. it is shown that how a two-phase algorithm can be applied to detect effi-cient dmus. it is important to compare computational performance of solving the simultaneous linear equa-tions with that of the lp, when computational issues and complexity analysis are a...

This paper presents a novel approach to achieving the goals of data envelopment analysis in the process of reconstruction and integration of decision-making units by using multiple objective linear programming. In this regard, first, we review inverse data envelopment analysis models for data reconstruction and integration. We present a model with multi-objective linear programming structure in...

in this paper, we show that inverse data envelopment analysis (dea) models can be used to estimate output with fuzzy data for a decision making unit (dmu) when some or all inputs are increased and deficiency level of the unit remains unchanged.

for efficiency evaluation of some of the decision making units that have uncertain information, rough data envelopment analysis technique is used, which is derived from rough set theorem and data envelopment analysis (dea). in some situations rough data alter nonradially. to this end, this paper proposes additive rough–dea model and illustrates the proposed model by a numerical example.