Non discretionary factors and imprecise data in

Previous article in issue. Under an Elsevier user license open archive Abstract The objective of the present paper is to propose a novel pair of data envelopment analysis DEA models for measurement of relative efficiencies of decision-making units DMUs in the presence of non-discretionary factors and imprecise data.

On the other hand, in traditional DEA, the efficiency of each DMU is measured relative to the efficiency frontier and is called the best relative efficiency or optimistic efficiency. Compared to traditional DEA, the proposed interval DEA approach measures the efficiency of each DMU relative to the inefficiency frontier, also called the input frontier, and is called the worst relative efficiency or pessimistic efficiency.

The pair of proposed interval DEA models takes into account the crisp, ordinal, and interval data, as well as non-discretionary factors, simultaneously for measurement of relative efficiencies of DMUs.

Previous article in issue.

Two numeric examples will be provided to illustrate the applicability of the interval DEA models. The pair of proposed interval DEA models takes into account the crisp, ordinal, and interval data, as well as non-discretionary factors, simultaneously for measurement of relative efficiencies of DMUs.

Two numeric examples will be provided to illustrate the applicability of the interval DEA models. Compared to traditional DEA, the proposed interval DEA approach measures the efficiency of each DMU relative to the inefficiency frontier, also called the input frontier, and is called the worst relative efficiency or pessimistic efficiency.

Under an Elsevier user license open archive Abstract The objective of the present paper is to propose a novel pair of data envelopment analysis DEA models for measurement of relative efficiencies of decision-making units DMUs in the presence of non-discretionary factors and imprecise data.

On the other hand, in traditional DEA, the efficiency of each DMU is measured relative to the efficiency frontier and is called the best relative efficiency or optimistic efficiency.The current data envelopment analysis (DEA) literature on non-discretionary inputs ignores the possibility that efficiency may be correlated with the non-discretionary factors.

This paper extends the literature by analyzing the effects that such correlation has. Discretionary models for evaluating the efficiency of suppliers assume that all criteria are discretionary, that is, controlled by the management of each supplier and varied at its discretion.

The objective of this paper is to propose a new pair of nondiscretionary factors-imprecise data envelopment analysis (NF-IDEA) models for selecting.

Liu et al. () for selecting the best suppliers considered distance and supply variety as non-discretionary input and non-discretionary output, respectively. Farzipoor Saen (b) proposed a new DEA model for selecting the best suppliers in the presence of.

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Measurement of the worst practice of decision-making units in the presence of non-discretionary factors and imprecise data Azizi, H.; Ganjeh Ajirlu, H. Improved data envelopment analysis models for evaluating interval efficiencies of decision-making units. Non-discretionary factors and imprecise data in DEA Obviously the above model is nonlinear since output/input levels are also variables whose exact value are.

Motivated by those points, the objective of this paper is to propose a model for selecting media in the presence of both flexible factors and imprecise data. This paper depicts the media selection process through a DEA model, while allowing for the incorporation of both flexible factors and imprecise data.

This paper proceeds as follows.

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Non discretionary factors and imprecise data in
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