A data envelopment analysis (DEA) method can be regarded as a useful management tool to evaluate decision making units (DMUs) using multiple inputs and outputs. In some cases, we face with imprecise inputs and outputs, such as fuzzy or interval data, so the efficiency of DMUs will not be exact. Most researchers have been interested in getting efficiency and ranking DMUs recently. Models of th...

In this research, efficiency of Iranian forest and rangeland decision making unit was assessed using non-parametric method. Inputs and outputs determined by field research and with applying a Delphi method the final input and output variables selected. Forty five wood-producing units (forestry plans) in three northern Iranian province were studied using Data Envelopment Analysis. The efficiency...

In Cambodia, cassava is mostly grown with little or no fertilizer inputs, but the magnitudes of nutrient balances are not known. This study was conducted to assess nutrient balances for cassava cultivation in Kampong Cham province in Northeast Cambodia. Forty five households in four cassava production zones were interviewed in relation to their cultural practices and crop residue management...

Data Envelopment Analysis (DEA) is an eciency measurement tool for evaluation of similar Decision Making Units (DMUs). In DEA, weights are assigned to inputs and outputs and the absolute eciency score is obtained by the ratio of weighted sum of outputs to weighted sum of inputs. In traditional DEA models, this measure is also equivalent with relative eciency score which evaluates DMUs in compar...

The classic overall profit needs precise information of inputs, outputs, inputs and outputs price vectors. In real word, all data are not certain. Therefore, in this case, stochastic and fuzzy methods use for measuring overall profit efficiency. These methods require more information about the data such as probability distribution function or data membership function, which in some cases may no...