We evaluate the overall efficiency of DMUo (o=1,..., n) tak- ing ({kt}, {s− t }, {s+ t }, {sgood t }, {sbad t }, {sfree t }) as variables, in the follow- ing three orientations, i.e. input-, output- and non-orientations. The input-oriented models deal mainly with reduction of input-related factors while producing at least the observed output-related factor levels. In our DSBM models, we maximize relative slacks in inputs and undesirable (bad) links. In the output-oriented models, we at- tempt to maximize output-related factors while using no more than theobservedamountofanyinput-relatedfactors.InourDSBMmod- els,wemaximizerelativeslacksinoutputsanddesirable(good)links. The non-oriented models aim to reduce input-related factors and to enlarge output-related factors simultaneously. Thus, the models in this class unify the input-oriented and output-oriented models in a single framework. These models should be used properly depending on respective research and managerial purposes. The differences in orientations affect the objective function of models as follows.
We evaluate the overall efficiency of DMUo (o=1,..., n) tak- ing ({kt}, {s− t }, {s+ t }, {sgood t }, {sbad t }, {sfree t }) as variables, in the follow- ing three orientations, i.e. input-, output- and non-orientations. The input-oriented models deal mainly with reduction of input-related factors while producing at least the observed output-related factor levels. In our DSBM models, we maximize relative slacks in inputs and undesirable (bad) links. In the output-oriented models, we at- tempt to maximize output-related factors while using no more than theobservedamountofanyinput-relatedfactors.InourDSBMmod- els,wemaximizerelativeslacksinoutputsanddesirable(good)links. The non-oriented models aim to reduce input-related factors and to enlarge output-related factors simultaneously. Thus, the models in this class unify the input-oriented and output-oriented models in a single framework. These models should be used properly depending on respective research and managerial purposes. The differences in orientations affect the objective function of models as follows.
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We evaluate the overall efficiency of DMUo (o=1,..., n) tak- ing ({kt}, {s− t }, {s+ t }, {sgood t }, {sbad t }, {sfree t }) as variables, in the follow- ing three orientations, i.e. input-, output- and non-orientations. The input-oriented models deal mainly with reduction of input-related factors while producing at least the observed output-related factor levels. In our DSBM models, we maximize relative slacks in inputs and undesirable (bad) links. In the output-oriented models, we at- tempt to maximize output-related factors while using no more than theobservedamountofanyinput-relatedfactors.InourDSBMmod- els,wemaximizerelativeslacksinoutputsanddesirable(good)links. The non-oriented models aim to reduce input-related factors and to enlarge output-related factors simultaneously. Thus, the models in this class unify the input-oriented and output-oriented models in a single framework. These models should be used properly depending on respective research and managerial purposes. The differences in orientations affect the objective function of models as follows.
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