Generalized DEA Model for Multiple Criteria Decision
8.6 Comparison Between GDEA and DEA Models
Now, we compare the efficiency in basic DEA models and GDEA model for the data in Taylor et al. (1997). We have data from 13 Mexican commercial banks over two years (1990–1991) from Taylor’s group. As shown in Table 8.7, each bank has the total income as the single output. Total income is the sum of a bank’s inter-est and non-interinter-est income. Total deposits and total non-interinter-est expense are the two inputs used to generate the output. Interest income includes interest earned from loan activities. Total non-interest income includes dividends, fees, and other non-interest revenue. The total deposits input variable includes the bank’s inter-est paying deposit liabilities. Total non-interinter-est expense includes personnel and administrative costs, commissions paid, banking support fund contributions and other non-interest operating costs. Thus, we evaluate the efficiency for each bank with the annual data, that is, considerα-efficiency corresponding to several values
164YeBoonYunetal.
Table 8.7. Input and output values for 13 Mexican banks, 1990–1991 (billions of nominal pesos).
1990 1991
Int. income Int. income
Non-int. plus non-int. Non-int. plus non-int.
Bank Deposits expense income Deposits expense income
(1) Banamex 35,313.90 2,500.88 14,247.10 57,510.90 3,670.33 15,764.60
(2) Bancomer 34,504.60 2,994.70 12,682.10 59,965.00 3,872.40 15,877.00
(3) Serfin 30,558.20 1,746.50 11,766.40 46,987.20 2,709.20 12,694.10
(4) Intermac 7,603.53 1,011.40 3,422.40 13,458.00 1,165.20 4,212.20
(5) Cremi 1,977.18 1,628.80 2,889.10 5,108.97 760.60 2,102.70
(6) Bancreser 2,405.00 140.70 1,050.50 3,314.32 190.80 1,681.10
(7) MercNort 2,146.06 338.30 1,320.10 3,714.72 463.30 1,377.40
(8) BCH 2,944.00 260.8 1,410.00 3,728.00 402.90 1,794.10
(9) Confia 1,962.34 266.60 1,568.00 3,324.43 364.90 1,944.40
(10) Bancen 1,815.73 196.70 946.20 2,544.96 242.70 848.80
(11) Promex 1,908.23 251.30 1,162.80 3,080.00 320.40 1,251.40
(12) Banoro 1,372.78 169.60 598.20 2,799.00 224.40 810.50
(13) Banorie 488.17 71.90 340.80 680.88 86.80 373.00
Source: Taylor et al. (1997).
Generalized DEA Model for Multiple Criteria Decision Making 165 α = 0.1, 0.5, 1, 10, 15 (only 1991) and103. Table 8.8 represents the results of analyses under the basic DEA models and the GDEA model.
As shown in the tables, the GDEA model withα = 0.1provides FDH effi-ciency. It means that there is no change in α-efficient DMUs for smallerαthan 0.1. In addition, the GDEA model withα= 10yields BCC efficiency in Table 8.8, whileα= 15does in Table 8.9. Also, there is no change inα-efficiency of DMUs, even if taking greaterαthan10or15.
Moreover, CCR efficiency can be figured by takingαsufficiently large in the GDEA model and adding the constraint xToν = yToµ. This operation shows that the number of efficient DMUs decreases as a parameter α increases in general.
Particularly, note theα-efficiency forα= 0.5andα= 1. This represents an inter-mediate efficiency between FDH-efficiency and BCC-efficiency. In practice, there are decision-making problems which cannot correspond to a special value judg-ment such as “ratio value efficiency”2 in the CCR model, “sum value efficiency”3 in the BCC model, and so on. In contrast to the existing DEA models, the GDEA model can incorporate various value judgments of DMs by changing a parameter α, and then several kinds of efficiency of the basic DEA models can be measured in a unified way on the basis of the GDEA model. Furthermore, the relationships among efficiencies for these models become transparent by considering GDEA.
8.7 Conclusions
In this paper, we suggested the GDEA model based on parametric domination struc-ture, and defined α-efficiency in the GDEA model. In addition, we investigated theoretical properties of relationships between the GDEA model and existing DEA models, specifically, the CCR model, the BCC model, and the FDH model. It was then proved that the GDEA model makes it possible to evaluate efficiencies of sev-eral DEA models in a unified way, and to incorporate various preference structures of DMs. Through a numerical example, it has been shown that the mutual relations among all decision-making units can be grasped by varying α in GDEA model.
Furthermore, we proposed the GDEADmodel based on production possibility as a dual approach to GDEA, and definedαD-efficiency in the GDEAD model. Also, we clarified the relations between the GDEAD model and existing DEA dual mod-els, and interpreted the meaning of an optimal value to the problem (GDEAD). As a result, it is possible to make a quantitative analysis for inefficiency on the basis of surplus of inputs and slack of outputs. Moreover, through an illustrative example, it
2We named the CCR-efficiency ratio value efficiency, because the ratio of the weighted sum of outputs to the weighted sum of inputs is maximized by the CCR model (see Yun et al., 2000).
3We named the BCC-efficiency sum value efficiency, because the difference of the weighted sum of outputs and the weighted sum of inputs is maximized by the BCC model (see Yun et al., 2000).
166YeBoonYunetal.
Table 8.8. DEA Mexican bank analysis, 13 banks, 1990. Output is total interest and non-interest income; inputs are total deposits and non-interest expense.
CCR BCC GDEA
θ Class θ RTS α= 103 α= 10 α= 1 α= 0.5 α= 0.1
Bank (xToν=yToµ)
(1) Banamex 0.816 NE 1.000 D –123.46 0.00 0.00 0.00 0.00
(2) Bancomer 0.646 NE 0.890 – –744.67 –7,282.88 –358.41 0.00 0.00
(3) Serfin 0.902 NE 1.000 D –11.88 0.00 0.00 0.00 0.00
(4) Intermac 0.573 NE 0.809 – –285.50 –1,648.99 0.00 0.00 0.00
(5) Cremi 1.000 E 1.000 C 0.00 0.00 0.00 0.00 0.00
(6) Bancreser 1.000 E 1.000 C 0.00 0.00 0.00 0.00 0.00
(7) MercNort 0.750 NE 0.757 – –126.73 –1,078.91 –149.92 –102.55 –19.69
(8) BCH 0.829 NE 0.837 – –70.89 –390.60 –11.27 –0.08 0.00
(9) Confia 1.000 E 1.000 C 0.00 0.00 0.00 0.00 0.00
(10) Bancen 0.778 NE 0.803 – –94.29 –390.09 –8.06 0.00 0.00
(11) Promex 0.782 NE 0.797 – –79.50 –506.79 –29.08 –6.76 0.00
(12) Banoro 0.588 NE 0.644 – –299.20 –606.52 –12.81 0.00 0.00
(13) Banorie 0.862 NE 1.000 I –58.55 0.00 0.00 0.00 0.00
C = constant returns to scale; D = decreasing returns to scale (RTS); E = efficient; I = increasing returns to scale; NE = not efficient.
GeneralizedDEAModelforMultipleCriteriaDecisionMaking167
Table 8.9. DEA Mexican bank analysis, 13 banks, 1991. Output is total interest and non-interest income; inputs are total deposits and non-interest expense.
CCR BCC GDEA
θ Class θ RTS α= 103 α= 15 α= 10 α= 1 α= 0.5 α= 0.1
Bank (xToν=yToµ)
(1) Banamex 0.531 NE 1.000 D –181.32 0.00 0.00 0.00 0.00 0.00
(2) Bancomer 0.511 NE 1.000 D –281.95 0.00 0.00 0.00 0.00 0.00
(3) Serfin 0.532 NE 1.000 D –136.52 0.00 0.00 0.00 0.00 0.00
(4)Intermac 0.569 NE 0.908 – –257.11 –717.26 0.00 0.00 0.00 0.00
(5) Cremi 0.704 NE 0.772 – –282.58 –3,134.25 –1,957.76 0.00 0.00 0.00
(6) Bancreser 1.000 E 1.000 C 0.00 0.00 0.00 0.00 0.00 0.00
(7) MercNort 0.634 NE 0.638 – –284.80 –4,371.50 –2,999.54 –385.14 –212.60 –42.31
(8) BCH 0.826 NE 0.828 – –112.8 –1,481.79 –982.50 –99.34 –60.03 –15.61
(9) Confia 1.000 E 1.000 C 0.00 0.00 0.00 0.00 0.00 0.00
(10) Bancen 0.592 NE 0.612 – –253.70 –1,621.77 –1,075.07 –50.54 0.00 0.00
(11) Promex 0.705 NE 0.715 – –191.64 –2,262.34 –1,504.08 –74.49 0.00 0.00
(12) Banoro 0.535 NE 0.554 – –295.19 –1,410.08 –934.00 –80.67 –5.37 0.00
(13) Banorie 0.937 NE 1.000 I –73.42 0.00 0.00 0.00 0.00 0.00
C = constant returns to scale; D = decreasing returns to scale (RTS); E = efficient; I = increasing returns to scale; NE = not efficient.
168 Ye Boon Yun et al.
has been shown that GDEADcan reveal domination relations among all decision-making units. It is expected from the results obtained in this study that GDEA will be useful for evaluating the efficiency of complex management systems in business, industry, and social problems.
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