فهرست مطالب s. kordrostami

In this research, two-stage networks are considered in which there are common resources between both stages. Also, there are links between the first and second stages that are undesirable for the first stage. Two models are rendered, the first is a model for calculating the efficiency of the mentioned two-stage processes under the non-convex technology and the second model is an inverse model for calculating output values through input changes. The changes are such that efficiency values remain constant. Actually, at first, a non-radial network free disposal hull (FDH) model is planned to evaluate the entire and stage efficiencies of two-stage processes with shared resources. The introduced framework is a mixed integer nonlinear programming plan and is computed using a heuristic approach. Then, an inverse two-stage FDH approach is presented to determine outputs related to two stages for perturbing inputs of each stage while the efficiency values remain unchanged. To clarify the proposed models, an application from the literature is used.

Regular NDEA models for calculating the efficiency of DMUs with two-stage construction requires the data set to be deterministic. In the real world, however, there are many observations that have stochastic behavior. This paper proposes a network DEA with two-stage structures models by the attention to stochastic data. The stochastic two-stage network DEA models are formulated based on the P-models of chance constrained programming and leader-follower concepts.

The appropriate choice of input-output weights is necessary to have a successful DEA model. Generally, if the number of DMUs i.e., n, is less than number of inputs and outputs i.e., m+s, then many of DMUs are introduced as efficient then the discrimination between DMUs is not possible. Besides, DEA models are free to choose the best weights. For resolving the problems that are resulted from freedom of weights, some constraints are set on the input-output weights. Symmetric weight constraints are a kind of weight constrains. In this paper, we represent a new model based on a multi-criterion data envelopment analysis (MCDEA) are developed to moderate the homogeneity of weights distribution by using symmetric weight constrains. Consequently, we show that the improvement of the dispersal of unrealistic input-output weights and the increasing discrimination power for our suggested models. Finally, as an application of the new model, we use this model to evaluate and ranking guilan selected hospitals.

Because of the piecewise linear nature of the data envelopment analysis (DEA) frontier, the optimal multipliers of the DEA models may not be unique. Choosing weights from alternative optimal solutions of dual multiplier models is one of the most frequently studied subjects in the context of DEA. In this paper, the authors have been inspired by the idea of Cooper et al. (2011) to propose a linear programming problem in which a specific decision making unit chooses the optimal solution of a linear program as the profile of weights for use in the cross-efficiency calculation. The approach proposed in this paper to determine input/output weights, prohibit the large differences in weights in cross efficiency evaluation. A real case on Chinese cities and special economic zones is given to illustrate the applicability of the proposed approach.