«

»

Print this Post

Ghasemi M.R., J. Ignatius, S. Lozano, A. Emrouznejad, A. Hatamimarbini (2015) A fuzzy expected value approach under generalized data envelopment analysis, Knowledge-Based Systems, 89: 148–159.

Ghasemi M.R., J. Ignatius, S. Lozano, A. Emrouznejad, A. Hatamimarbini (2015) A fuzzy expected value approach under generalized data envelopment analysis, Knowledge-Based Systems, 89: 148–159.

Fuzzy data envelopment analysis (DEA) models emerge as another class of DEA models to account for imprecise inputs and outputs for decision making units (DMUs). Although several approaches for solving fuzzy DEA models have been developed, there are some drawbacks, ranging from the inability to provide satisfactory discrimination power to simplistic numerical examples that handles only triangular fuzzy numbers or symmetrical fuzzy numbers. To address these drawbacks, this paper proposes a fuzzy expected generalized DEA model, which can treat fuzzy expected CCR, fuzzy expected BCC, and fuzzy expected FDH models in a unified way that handles both symmetrical and asymmetrical fuzzy numbers. We also considered super-efficiency evaluation problems, which is always feasible and it can be suggested as a way in dealing with infeasibility problems. The proposed method can be perceived as a form of aggregating solutions across a range of ?-levels. In order to illustrate the performance of the proposed method, it is first tested using two established numerical examples and compared with the results obtained from alternative methods. An application of energy dependency among 23 European Union (EU) member countries is further used to validate and describe the efficacy of our approach under asymmetric fuzzy numbers.

[Download1   or   Download2]

goin up
Skip to toolbar