Package {ecpromethee}

EC-PROMETHEE Multi-Criteria Decision Method

Description

Implements ENTROPY, CRITIC, PROMETHEE II and EC-PROMETHEE aggregation.

References

Basilio, M. P.; Pereira, V.; Yigit, F. (2023). New Hybrid EC-Promethee Method with Multiple Iterations of Random Weight Ranges: Applied to the Choice of Policing Strategies. Mathematics, 11(21), 4432. doi:10.3390/math11214432

Calculate CRITIC Criteria Weights

Description

Computes objective criteria weights using Criteria Importance Through Intercriteria Correlation (CRITIC).

Usage

critic_weights(x, directions = "max")

Arguments

Value

A numeric vector of normalized criteria weights.

Examples

x <- matrix(c(1, 2, 3, 2, 3, 4, 4, 4, 5), nrow = 3)
critic_weights(x)

Apply the EC-PROMETHEE Method

Description

EC-PROMETHEE combines ENTROPY and CRITIC objective weights with optional subjective weights. It creates criterion-specific weight ranges, generates random normalized weights inside those ranges, runs PROMETHEE II for each random weight set, and aggregates ordinal positions into a final ranking.

Usage

ec_promethee(
  x,
  subjective_weights = NULL,
  iterations = 1000,
  directions = "max",
  preference = "usual",
  q = 0,
  p = 0,
  s = 1,
  seed = NULL
)

Arguments

Value

A list with final ranking, component weights, weight ranges, random weights and the iteration rank matrix.

Examples

x <- matrix(c(7, 9, 6, 8, 7, 7, 6, 8, 9), nrow = 3, byrow = TRUE)
ec_promethee(x, iterations = 20, seed = 123)$final_ranking

Calculate ENTROPY Criteria Weights

Description

Computes objective criteria weights using the ENTROPY method described in Basilio, Pereira and Yigit (2023).

Usage

entropy_weights(x)

Arguments

Value

A numeric vector of normalized criteria weights.

Examples

x <- matrix(c(1, 2, 3, 2, 3, 4, 4, 4, 5), nrow = 3)
entropy_weights(x)

Policing Strategy Decision Matrix

Description

A 14 by 20 decision matrix using the "Mode" scenario from Basilio, Pereira and Yigit (2023). Rows are policing strategy alternatives and columns are crime-demand criteria.

Usage

policing_mode

Format

A numeric matrix with 14 alternatives and 20 criteria.

Value

A numeric matrix with class matrix. Rows represent policing strategy alternatives, columns represent crime-demand criteria, and the cell values are the criterion scores used by EC-PROMETHEE.

Source

Basilio, M. P.; Pereira, V.; Yigit, F. (2023). doi:10.3390/math11214432

Examples

dim(policing_mode)
ec_promethee(policing_mode, iterations = 5, seed = 123)$final_ranking

Apply PROMETHEE Preference Functions

Description

Applies one of the six standard PROMETHEE preference functions.

Usage

preference_function(d, type = "usual", q = 0, p = 0, s = 1)

Arguments

Value

A numeric vector of preference degrees in [0, 1].

Examples

preference_function(c(-1, 0, 1), type = "usual")
preference_function(c(0, 1, 2), type = "v_shape", p = 2)

Rank Alternatives With PROMETHEE II

Description

Runs PROMETHEE II and returns the complete ranking of alternatives.

Usage

promethee_ii(
  x,
  weights,
  directions = "max",
  preference = "usual",
  q = 0,
  p = 0,
  s = 1
)

Arguments

Value

A data frame with alternatives, positive flow, negative flow, net flow and rank.

Examples

x <- matrix(c(7, 9, 6, 8, 7, 7, 6, 8, 9), nrow = 3, byrow = TRUE)
promethee_ii(x, weights = c(1 / 3, 1 / 3, 1 / 3))

Generate Random Normalized Weights

Description

Generates random normalized weights from EC-PROMETHEE criterion ranges.

Usage

random_weights(lower, upper, iterations, seed = NULL)

Arguments

Value

A numeric matrix with iterations rows and one column per criterion.

Examples

random_weights(c(0.1, 0.2), c(0.4, 0.5), iterations = 3, seed = 1)

Build EC-PROMETHEE Weight Ranges

Description

Creates criterion-specific lower and upper limits from ENTROPY, CRITIC and optional subjective decision-maker weights.

Usage

weight_ranges(entropy, critic, subjective_weights = NULL)

Arguments

Value

A data frame with columns criterion, lower and upper.

Examples

weight_ranges(c(0.4, 0.6), c(0.5, 0.5))