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This study proposes a three-staged approach to data envelopment analysis (DEA) modeling for hospital efficiency. The approach aims to overcome the constraint on the number of inputs/outputs relative to the number of DMUs. Initially, the principal components of all inputs and outputs are determined using principal component analysis (PCA). Next, these principal components enter a factor analysis (FA) process to construct a two-level hierarchy of inputs/outputs and to establish a weighting scheme based on explained variances of components. Finally, a two-level DEA (TLDEA) method is applied to the resultant framework to determine the relative efficiency of hospitals using data from the healthcare context of Iran as an emerging economy. The outcomes of applying the proposed PCA-FA-TLDEA approach are argued to offer a substantial increase in the discriminatory power of classical DEA methods and could incorporate a relatively large set of inputs/outputs already existing in the hospital efficiency literature. As demonstrated in the evaluated hospitals, the PCA-FA-TLDEA methodology improved the discrimination from 0% in the original DEA to 45%. The paper proposes a novel three-stage DEA model by using PCA to extract the principal components from the inputs and outputs; therefore, reducing the number of inputs and outputs and their inter-correlations. Secondly, a hierarchy of inputs and outputs by applying FA to the principal components is constructed. Finally, the TLDEA method to the hierarchy of inputs and outputs is applied to evaluate the performance of public hospitals. [ABSTRACT FROM AUTHOR]

Real-world decision-making problems often consist in considering multiple and antithetic objectives. Therefore, multi-objective decision making (MODM) is a practical framework in implicational areas. In this paper, a fuzzy MODM problem is considered, where all of its parameters are defined fuzzily, and a solution inspired by multi-attribute VIKOR method is proposed. The proposed method tries to find fuzzy efficient solution for a problem by minimizing its combinational distance from an ideal and anti-ideal solution. This method can reveal the efficient frontier of the problem. Applicability of the proposed method is shown in an illustrative example and its application is summarized in an investment problem. Both examples show applicabihty of the proposed method. [ABSTRACT FROM AUTHOR]

Purpose – The purpose of this paper is to extend an approach to solve linear programming problems with grey data and variables, based on a developed multi-objective programming approach. Design/methodology/approach – The proposed approach to generally solve the grey linear programming problems is based on the notion of order relation between interval grey numbers. This notion is applied to cascade the grey objective function to a bi-objective problem based on the objective function of the original problem. The same approach is taken to transform grey constraints to a set of corresponding linear constraints. Finally, the obtained multi-objective model can be solved by any existing methods in the literature. Findings – One of the shortcomings of previous approaches to solve grey linear programming problems was that they required the grey coefficients of objective function to be both side negative or positive. The approach proposed here does not have such a requirement and guarantees the feasibility of solutions. Originality/value – A different approach is developed in the paper that can be used to solve grey linear programming problems in general form. The method relaxes the limitation of existing approaches. [ABSTRACT FROM AUTHOR]