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The RAFSI method is a multi-criteria decision-making technique
developed by Malisa Zizovic in 2020. The rafsi package
implements this method in R, allowing users to evaluate alternatives
based on weighted criteria and generate a final ranking of
alternatives.
In this example, we will use the RAFSI method to evaluate a set of alternatives based on specific criteria and calculate their final rankings.
library(rafsi)
# Define the dataset (rows: alternatives, columns: criteria)
dataset <- matrix(c(
180, 165, 160, 170, 185, 167, # Criterion 1: Higher is better
10.5, 9.2, 8.8, 9.5, 10, 8.9, # Criterion 2: Lower is better
15.5, 16.5, 14, 16, 14.5, 15.1, # Criterion 3: Lower is better
160, 131, 125, 135, 143, 140, # Criterion 4: Higher is better
3.7, 5, 4.5, 3.4, 4.3, 4.1 # Criterion 5: Higher is better
), nrow = 6, ncol = 5, byrow = TRUE)
# Set names for the alternatives (A1 to A6)
rownames(dataset) <- c("A1", "A2", "A3", "A4", "A5", "A6")
# Define the weights for each criterion
weights <- c(0.35, 0.25, 0.15, 0.15, 0.10)
# Define the type of each criterion: 'max' for benefit, 'min' for cost
criterion_type <- c('max', 'min', 'min', 'max', 'max')
# Define the ideal values (best-case scenario) for each criterion
ideal <- c(200, 6, 10, 200, 8)
# Define the anti-ideal values (worst-case scenario) for each criterion
anti_ideal <- c(120, 12, 20, 100, 2)
# Number of criteria (n_i) and number of alternatives (n_k)
n_i <- 1
n_k <- 6
# Apply the RAFSI method
result <- rafsi_method(dataset, weights, criterion_type, ideal, anti_ideal, n_i, n_k)
# View the results
print(result)
#> $Standardized_matrix
#> [,1] [,2] [,3] [,4] [,5]
#> A1 4.7500 133.500000 76.00 4.500 153.500000
#> A2 3.9375 4.750000 0.60 -3.560 7.250000
#> A3 -5.8750 3.416667 3.75 -3.175 11.000000
#> A4 -5.5000 8.083333 3.55 4.000 108.500000
#> A5 1.3125 108.500000 67.50 3.000 2.416667
#> A6 -6.1875 -0.250000 -2.30 -3.785 2.750000
#>
#> $Normalized_matrix
#> [,1] [,2] [,3] [,4] [,5]
#> A1 0.6785714 0.006420546 0.01127820 0.6428571 21.9285714
#> A2 0.5625000 0.180451128 1.42857143 -0.5085714 1.0357143
#> A3 -0.8392857 0.250871080 0.22857143 -0.4535714 1.5714286
#> A4 -0.7857143 0.106038292 0.24144869 0.5714286 15.5000000
#> A5 0.1875000 0.007899934 0.01269841 0.4285714 0.3452381
#> A6 -0.8839286 -3.428571429 -0.37267081 -0.5407143 0.3928571
#>
#> $Ranking
#> Alternative Ranking
#> A1 A1 2.5300826
#> A4 A4 1.4234412
#> A2 A2 0.4835592
#> A5 A5 0.1683143
#> A3 A3 -0.1076394
#> A6 A6 -1.2642399Interpret the Results
The result provides the ranking of the alternatives based on their performance across all criteria, considering the weights and whether each criterion is treated as a benefit or a cost.
For more detailed information on the RAFSI method, you can refer to the original paper: https://doi.org/10.3390/math8061015.
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They may not be fully stable and should be used with caution. We make no claims about them.
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