| Type: | Package |
| Title: | Ranking of Alternatives with the RAFSI Method |
| Version: | 0.0.2 |
| Description: | Ranking of Alternatives through Functional mapping of criterion sub-intervals into a Single Interval Method is designed to perform multi-criteria decision-making (MCDM), developed by Mališa Žižovic in 2020 (<doi:10.3390/math8061015>). It calculates the final sorted rankings based on a decision matrix where rows represent alternatives and columns represent criteria. The method uses: - A numeric vector of weights for each criterion (the sum of weights must be 1). - A numeric vector of ideal values for each criterion. - A numeric vector of anti-ideal values for each criterion. - Numeric values representing the extent to which the ideal value is preferred over the anti-ideal value, and the extent to which the anti-ideal value is considered worse. The function standardizes the decision matrix, normalizes the data, applies weights, and returns the final sorted rankings. |
| Language: | en-US |
| Depends: | R (≥ 4.2.0) |
| License: | GPL (≥ 3) |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.3.1 |
| Suggests: | knitr, rmarkdown, spelling, testthat (≥ 3.0.0) |
| VignetteBuilder: | knitr |
| Config/testthat/edition: | 3 |
| NeedsCompilation: | no |
| Packaged: | 2024-09-23 18:14:41 UTC; Usuario |
| Author: | Mateus Vanzetta [aut, cre],
Marcos Santos 
[ctb] |
| Maintainer: | Mateus Vanzetta <mateusvanzetta@id.uff.br> |
| Repository: | CRAN |
| Date/Publication: | 2024-09-25 08:10:02 UTC |
Rank Reversal Problem Using a New Multi-Attribute Model - RAFSI Method for Multi-Criteria Decision Making
Description
This function implements the (Ranking of Alternatives Through Functional Mapping of Criterion Sub-Intervals Into a Single Interval) RAFSI method, Rank Reversal Problem Using a New Multi-Attribute Model. More information about the method can be found at https://doi.org/10.3390/math8061015. More information about the implementation at https://github.com/mateusvanzetta/rafsi. used for multi-criteria decision-making problems. It calculates the standardized decision matrix, normalizes the data, applies weights, and returns the final sorted rankings.
Usage
rafsi_method(
dataset,
weights,
criterion_type,
ideal = numeric(),
anti_ideal = numeric(),
n_i,
n_k
)
Arguments
dataset |
A matrix of criterion values where rows represent alternatives and columns represent criteria. |
weights |
A numeric vector representing the weights of each criterion. The sum of the weights must be 1. |
criterion_type |
A character vector indicating the type of each criterion ('max' for maximization, 'min' for minimization). |
ideal |
A numeric vector representing the ideal values for each criterion. |
anti_ideal |
A numeric vector representing the anti-ideal values for each criterion. |
n_i |
A numeric value representing the ratio that shows to what extent the anti-ideal value is worse than the value. |
n_k |
A numeric value representing the ratio that shows to what extent the ideal value is preferred over the anti-ideal value. |
Value
A list containing:
- Standardized_matrix
The matrix after applying the RAFSI transformation, which standardizes the data according to the ideal and anti-ideal values.
- Normalized_matrix
The matrix after normalizing the standardized data, adjusted according to the criteria weights.
- Ranking
A data frame showing the final ranking of the alternatives. The alternatives are sorted in descending order of preference.
#'
Examples
# Define the dataset
dataset <- matrix(c(
180, 165, 160, 170, 185, 167, # Criterion 1
10.5, 9.2, 8.8, 9.5, 10, 8.9, # Criterion 2
15.5, 16.5, 14, 16, 14.5, 15.1, # Criterion 3
160, 131, 125, 135, 143, 140, # Criterion 4
3.7, 5, 4.5, 3.4, 4.3, 4.1 # Criterion 5
), nrow = 6, ncol = 5)
# Set the names of alternatives
rownames(dataset) <- c("A1", "A2", "A3", "A4", "A5", "A6")
# Define the weights and criterion types
weights <- c(0.35, 0.25, 0.15, 0.15, 0.10)
criterion_type <- c('max', 'max', 'min', 'min', 'max')
# Specify ideal and anti-ideal values
ideal <- c(200, 12, 10, 100, 8)
anti_ideal <- c(120, 6, 20, 200, 2)
# Set n_i and n_k values
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 result
print(result)