The production possibility set (PPS) is defined as the set of all inputs and outputs of a system in which inputs can produce outputs. In data envelopment analysis (DEA), identification of the strong defining hyperplanes of the empirical production possibility set (PPS) is important, because they can be used for determining rates of change of outputs with change in inputs. Also, efficient hyperp...

Data Envelopment Analysis (DEA) deals with evaluating a set of decision-making units (DMUs) based on nonparametric mathematical approaches. In classical DEA models, the given set of factors of DMUs is divided into two categories, inputs and outputs, while in some practical problems there are some measures whose membership to these categories is unclear. It means these factors treat different in...

This paper considers structural nonparametric random utility models for continuous choice variables. It provides sufficient conditions on random preferences to yield reducedform systems of nonparametric stochastic demand functions that allow global invertibility between demands and random utility components. Invertibility is essential for global identification of structural consumer demand mode...

Demand fluctuations that cause variations in output levels will affect a firm’s technical inefficiency. To assess this demand effect, a demand-truncated production function is developed and an “effectiveness” measure is proposed. Often a firm can adjust some input resources influencing the output level in an attempt to match demand. We proposes a short-run capacity planning method, termed proac...

Derived from the idea of stochastic approximation, recursive algorithms to identify a Hammerstein system are presented. Two of them recover the characteristic of the nonlinear memoryless subsystem while the third one estimates the impulse response of the linear dynamic part. The a priori information about both subsystems is nonparametric. Consistency in quadratic mean is shown and the convergen...

Several methods have been proposed for ranking the decision-making units (DMUs) in data envelopment analysis (DEA) with imprecise data. Some methods have only used the upper bound efficiencies to rank DMUs. However, some other methods have considered both of the lower and upper bound efficiencies to rank DMUs. The current paper shows that these methods did not consider the DEA axioms and may be...

data envelopment analysis (dea) is a method to evaluate the relative efficiency of decision making units (dmus). in this method, the issue has always been to determine a set of weights for each dmu which often caused many problems. since the dea models also have the multi-objective linear programming (molp) problems nature, a rational relationship can be established between molp and dea problem...