CurrentModule = DataEnvelopmentAnalysis
DocTestSetup = quote
using DataEnvelopmentAnalysis
end
When the number of decision-making units is large, traditional DEA models are slow to solve. Khezrimotlagh, Zhu, Cook, and Toloo (2019), propose a framework that reduces the computational time by finding the set of best practices DMUs from a subsample and evaluating the rest of the decision-making units with respect to the best performers.
The proposed framework includes five steps:
- Select a subsample of DMU.
- Find the best practices in the subsample.
- Find the exterior DMUs with respect to the hull of the best practices.
- Identify the set of all efficient DMUs.
- Calculate performance scores as in the traditional DEA model.
This example computes the Big Data radial input-oriented DEA model under variable returns to scale, using random data drawn from a uniform distribution. 500 DMUs with six inputs and four outputs in the interval (10, 20) are generated:
# Generate random data
using DataEnvelopmentAnalysis
using Distributions
using Random
using StableRNGs
rng = StableRNG(1234567)
X = rand(Uniform(10, 20), 500, 6);
Y = rand(Uniform(10, 20), 500, 4);
# Calculate the Big Data DEA Model
deabig = deabigdata(X, Y)
# Get efficiency scores
efficiency(deabig)
deabigdata