Mixed input and output orientations of Data Envelopment Analysis with Linear Fractional Programming and Least Distance Measures
Data Envelopment Analysis (DEA) is an optimization technique to evaluate the efficiency of Decision- Making Units (DMU’s) together with multiple inputs and multiple outputs on the strength of weighted input and output ratios, where as Linear fractional programming is used to obtain DEA frontier. The...
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| Veröffentlicht in: | Statistics, optimization & information computing optimization & information computing, 2016, Vol.4 (4), p.326 |
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| creator | Dar, Qaiser Farooq Padi, Tirupathi Rao Tali, Arif Muhammad |
| description | Data Envelopment Analysis (DEA) is an optimization technique to evaluate the efficiency of Decision- Making Units (DMU’s) together with multiple inputs and multiple outputs on the strength of weighted input and output ratios, where as Linear fractional programming is used to obtain DEA frontier. The efficiency scores of DMU obtained through either input orientation or output orientation DEA model will provide only local optimum solution. However, the mixed orientation of input and output variables will provide the global optimal solution for getting the efficient DMUs in DEA. This study has proposed the relationships of a mixed orientation of input and output variables using fractional linear programming along with Least-Distance Measure (LDM). Both constant returns to scale (CRS) and variable returns to scale (VRS) are considered for the comparative study. |
| doi_str_mv | 10.19139/soic.v4i4.225 |
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| subjects | Comparative studies Data analysis Data envelopment analysis Efficiency Linear programming Mathematical programming Optimization Orientation |
| title | Mixed input and output orientations of Data Envelopment Analysis with Linear Fractional Programming and Least Distance Measures |
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