| Type: | Package |
| Title: | PROMETHEE I, II, and III Methods |
| Version: | 0.1.0 |
| Author: | Miguel Angelo Lellis Moreira [aut, cre], Marcos dos Santos [aut], Carlos Francisco Simoes Gomes [aut] |
| Maintainer: | Miguel Angelo Lellis Moreira <miguellellis@hotmail.com> |
| Description: | The PROMETHEE method is a multi-criteria decision-making method addressing with outranking problems. The method establishes a preference structure between the alternatives, having a preference function for each criterion. IN this context, three variants of the method is carried out: PROMETHEE I (Partial Outranking), PROMETHEE II (Total Outranking), and PROMETHEE III (Outranking by Intervals). |
| License: | GPL-3 |
| Imports: | ggplot2,cowplot |
| Encoding: | UTF-8 |
| LazyData: | true |
| NeedsCompilation: | no |
| Packaged: | 2020-12-17 16:56:58 UTC; migue |
| Repository: | CRAN |
| Date/Publication: | 2020-12-21 10:30:03 UTC |
PROMETHEE I, II, and III Methods
Description
The PROMETHEE method is a multti-criteria decision-making method addressing with outranking problems. The method establishes a preference structure between the alternatives, having a preference function for each criterion. IN this context, three variants of the method is carried out: PROMETHEE I (Partial pre-ordering), PROMETHEE II (Total pre-ordering), and PROMETHEE III (pre-ordering by inervals).
Usage
promethee123(alternatives, criteria, decision_matrix, min_max,
normalization_function, q_indifference, p_preference, s_curve_change, criteria_weights)
Arguments
alternatives |
The names respective to set of alternatives in evaluation |
criteria |
The names respective to set of criteria in evaluation |
decision_matrix |
A matrix where rows correspond to the criteria and columns correspond to alternatives, there is inputed the performance of alternatives in each criterion |
min_max |
A vector with objectives, minimize or maximize, to each criteria. |
normalization_function |
Numerical description relative to each type of normalization function to each criterion |
q_indifference |
Indifference threshold |
p_preference |
Preference threshold |
s_curve_change |
Threshold of changing in the curve |
criteria_weights |
Numerical representation of the respective importance for each criterion |
Details
- For normalization function we have six types: [ 1 ] for USUAL (0 or 1) — [ 2 ] for U-SHAPE (0 or 1) q [ 3 ] for V-SHAPE (x/p or 1) p [ 4 ] for LEVEL (0, 0.5 or 1) q , p [ 5 ] for V-SHAPE I (0, (x-q)/(p-q) or 1) q , p [ 6 ] for GAUSSIAN (0 or 1-e^(-x^2/2*s^2)) s ———————————- q = indifference parameter p = preference parameter s = parameter to indicate change in the preference curve
- The input of thresholds depends of the type of preference function used;
- The sum of weights must be 1;
Value
- Performance in each criterion;
- Global Index of Importance;
- Importance Flows (Positive, Negative, and Net);
- Preference relations in PROMETHEE I;
- Total Outranking in PROMETHEE II;
- Preference relations in PROMETHEE III;
- Graphical representations of PROMETHEE I, II, and III.
Author(s)
Miguel Angelo Lellis Moreira miguellellis@hotmail.com, Marcos dos Santos marcosdossantos_doutorado_uff@yahoo.com.br, Carlos Francisco Simoes Gomes cfsg1@bol.com.br
References
BRANS, Jean-Pierre; DE SMET, Yves. PROMETHEE methods. In: Multiple criteria decision analysis. Springer, New York, NY, 2016. p. 187-219. DOI: 10.1007/978-1-4939-3094-4_6. <https://link.springer.com/chapter/10.1007/978-1-4939-3094-4_6>
Examples
alternatives <- c("SARP", "ORAC", "TOTS", "MICRO", "IBRP")
criteria <- c("Price", "Complexity", "Security", "Performance")
decision_matrix <- matrix(c(15, 29, 38, 24, 25.5,
7.5, 9, 8.5, 8, 7,
1, 2, 4, 3, 3,
50, 110, 90, 75, 85),
ncol = length(alternatives), nrow = length(criteria), byrow = TRUE)
min_max <- c("min", "min", "max", "max")
normalization_function <- c( 5 , 5 , 5 , 5 )
q_indifference <- c(2, 0.5 , 1 , 10)
p_preference <- c(5 , 1 , 2 , 20)
s_curve_change <- c("","","","")
criteria_weights <- c(0.2 , 0.2 , 0.3 , 0.3)
promethee123(alternatives, criteria, decision_matrix, min_max, normalization_function,
q_indifference, p_preference, s_curve_change, criteria_weights)