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...

in models of data envelopment analysis (dea), an optimal set of weights is generally assumed to represent the assessed decision making unit (dmu) in the best light in comparison to all the other dmus, and so there is an optimal set of weights corresponding to each dmu. the present paper, proposes a three stage method to determine one common set of weights for decision making units. then, we use...

in this paper, presenting two simple methods for ranking of efficient dmus in dea models that included to add one virtual dmu as ideal dmu and is using the additive model. note that, we use an ideal point just for comparing efficient dmus with. although these methods are simple, they have ability for ranking all efficient dmus, extreme points and the others, also they are capable of ranking the...

for better guiding a system, senior managers should have accurate information. using data envelopment analysis (dea) help managers in this objective. thus, many investigations have been made in order to find the most productive scale size (mpss) for the evaluating decision making units (dmus). in this paper we consider this case where there exist subsets of input and output variables to be inte...

in some situations the producers desire to maximize total profit of decision making units (dmus) while the inputs and outputs prices of dmus change from one time period to another. in this paper, the researchers develop productivity index when producers are going to maximize total profit when the price of inputs and outputs are known.the proposed method uses all price information about inputs a...

in the classical data envelopment analysis (dea) models, inputs and outputs are assumed as known variables, and these models cannot deal with unknown amounts of variables directly. in recent years, there are few researches on handling missing data. this paper suggests a new interval based approach to apply missing data, which is the modified version of kousmanen (2009) approach. first, the prop...

abstract. data envelopment analysis (dea) is now widely utilized as a method for assessing relative efficiency of a set of decision making units (dmus). along as evaluating relative efficiency dea models are capable of to be improved for finding benchmark for inefficient units which can help managers for better making decision. thus it is important to obtain accurate results from dea models. in...

this research proposes a methodology for ranking decision making units byusing a goal programming model.we suggest a two phases procedure. in phase1, by using some dea problems for each pair of units, we construct a pairwisecomparison matrix. then this matrix is utilized to rank the units via the goalprogramming model.