Compartir
Título
An integrated ELECTRE method for selection of rehabilitation center with m-polar fuzzy N-soft information
Autor(es)
Palabras clave
m-polar fuzzy set
Fuzzy N-soft set
ELECTRE I
PROMETHEE
Clasificación UNESCO
12 Matemáticas
Fecha de publicación
2023
Editor
Elsevier
Citación
Akram, M., Sultan, M., & Alcantud, J. C. R. (2023). An integrated ELECTRE method for selection of rehabilitation center with m -polar fuzzy N -soft information. Artificial Intelligence in Medicine, 135, 102449. https://doi.org/10.1016/j.artmed.2022.102449
Resumen
[EN] The primary goal of this research article is to apply ELECTRE I, a fundamental multi-criteria group decisionmaking technique, in an 𝑚-polar fuzzy 𝑁-soft environment. This new methodology helps us to pinpoint
the best alternative(s) in the presence of multi-polar options with 𝑁-graded qualities. Its basic operational
idea entails the comparison between any two alternatives by the assessment of score degrees. Concordance
and discordance indices are then calculated to evaluate the alternatives’ superiority and inferiority. We may
disqualify the incompetent alternatives using concordance and discordance levels. An 𝑚-polar fuzzy 𝑁-soft
dominance matrix can represent the combined effect of concordance and discordance dominance matrices.
The steps of this new multi-criteria group decision making technique are summarized in a flowchart. In order
to demonstrate its authenticity and applicability, we employ a case study involving the establishment of a
rehabilitation facility for drug abusers. A comparison with the 𝑚-polar fuzzy PROMETHEE and 𝑚-polar fuzzy
ELECTRE I methodologies establishes its validity. Finally, we conclude our study of the methodology proposed
in this paper with a critical analysis of its benefits and drawbacks.
URI
ISSN
0933-3657
DOI
10.1016/j.artmed.2022.102449
Versión del editor
Collections
- BORDA. Artículos [35]
Patrocinador
Publicación en abierto financiada por la Universidad de Salamanca como participante en el Acuerdo Transformativo CRUE-CSIC con Elsevier, 2021-2024