Modified Directional Distance Function

Based on the data matrix $(X,Y)$, we calculate the modified directional distance function MDDF, (Aparicio et al. 2013), of each observation o by solving $n$ times the following linear programming problem:

\[\begin{aligned} & \underset{\beta^x, \beta^y,\lambda_j }{\mathop{\min }}\,\quad \quad \quad \;\ \beta^x + \beta^y \\ & \text{subject}\ \text{to} \\ & \quad \quad \quad \quad \quad \ \sum_{j=1}^{n}{\lambda_j x_{ij} }\ \le x_{io} - \beta^x {g}_{io}^{-} \qquad i = 1,...,m \\ & \quad \quad \quad \quad \quad \ \sum_{j=1}^{n}{\lambda_j y_{rj} }\ \ge y_{ro} + \beta^y {g}_{ro}^{+} \qquad r = 1,...,s \\ & \quad \quad \quad \quad \quad \ \lambda_j \ge 0 \qquad j = 1,...,n \\ & \quad \quad \quad \quad \quad \ \beta^x \ge 0 \qquad i = 1,...,m \\ & \quad \quad \quad \quad \quad \ \beta^y \ge 0 \qquad r = 1,...,s. \\ \end{aligned}\]

with the following condition when assuming variable returns to scale:

\[\sum\nolimits_{j=1}^{n}\lambda_j=1\]

In this example we compute the modified directional distance function model under variable returns to scale using ones as directions for both inputs and outputs::

using DataEnvelopmentAnalysis

X = [2; 4; 8; 12; 6; 14; 14; 9.412];

Y = [1; 5; 8; 9; 3; 7; 9; 2.353];

deamddfvrs = deamddf(X, Y, Gx = :Ones, Gy = :Ones, rts = :VRS)

Modified DDF DEA Model 
DMUs = 8; Inputs = 1; Outputs = 1
Returns to Scale = VRS
Gx = Ones; Gy = Ones
───────────────────────────────────────────────
   efficiency       βx     βy  slackX1  slackY1
───────────────────────────────────────────────
1     0.0      0.0      0.0        0.0      0.0
2     0.0      0.0      0.0        0.0      0.0
3     0.0      0.0      0.0        0.0      0.0
4     0.0      0.0      0.0        0.0      0.0
5     4.0      2.0      2.0        0.0      0.0
6     7.33333  7.33333  0.0        0.0      0.0
7     2.0      2.0      0.0        0.0      0.0
8     8.059    5.412    2.647      0.0      0.0
───────────────────────────────────────────────

Estimated efficiency scores are returned with the efficiency function:

efficiency(deamddfvrs)

8-element Vector{Float64}:
 0.0
 0.0
 0.0
 0.0
 4.000000000000001
 7.333333333333334
 2.0
 8.059000000000001

Estimated $\beta$ on inputs and outputs are returned with the efficiency function:

efficiency(deamddfvrs, :X)

8-element Vector{Float64}:
 0.0
 0.0
 0.0
 0.0
 1.999999999999999
 7.333333333333334
 2.0
 5.412000000000001

efficiency(deamddfvrs, :Y)

8-element Vector{Float64}:
 0.0
 0.0
 0.0
 0.0
 2.0000000000000018
 0.0
 0.0
 2.6470000000000002

deamddf Function Documentation

deamddf(X, Y; Gx, Gy)