Multi-criteria decision making with linguistic labels: A comparison of two methodologies applied to energy planning

Arayeh Afsordegan; N. Agell; null Cremades  Lázaro; Mònica Sánchez Soler; null Zahedi  Siamak

Multi-criteria decision making with linguistic labels: A comparison of two methodologies applied to energy planning

Arayeh Afsordegan, N. Agell, Cremades Lázaro, Mònica Sánchez Soler, Zahedi Siamak

Esade

Producció científica: Contribució a una conferència › Contribució

Resum

This paper compares two multi-criteria decision making (MCDM) approaches based on linguis-tic label assessment. The first approach consists of a modified fuzzy TOPSIS methodology in-troduced by Kaya and Kahraman in 2011. The second approach, introduced by Agell et al. in 2012, is based on qualitative reasoning techniques for ranking multi-attribute alternatives in group decision-making with linguistic labels. Both approaches are applied to a case of assess-ment and selection of the most suitable types of energy in a geographical area.

Idioma original	Anglès
Estat de la publicació	Publicada - 5 de febr. 2014
Esdeveniment	XVII Congreso Español sobre Tecnologías y Lógica Fuzzy (ESTYLF 2014) - Durada: 5 de febr. 2014 → 7 de febr. 2014

Conferència

Conferència	XVII Congreso Español sobre Tecnologías y Lógica Fuzzy (ESTYLF 2014)
Període	5/02/14 → 7/02/14

Com citar-ho

@conference{465c19cffd71482d809ab913e6cfdaca,

title = "Multi-criteria decision making with linguistic labels: A comparison of two methodologies applied to energy planning",

abstract = "This paper compares two multi-criteria decision making (MCDM) approaches based on linguis-tic label assessment. The first approach consists of a modified fuzzy TOPSIS methodology in-troduced by Kaya and Kahraman in 2011. The second approach, introduced by Agell et al. in 2012, is based on qualitative reasoning techniques for ranking multi-attribute alternatives in group decision-making with linguistic labels. Both approaches are applied to a case of assess-ment and selection of the most suitable types of energy in a geographical area.",

author = "Arayeh Afsordegan and N. Agell and {Cremades L{\'a}zaro} and {S{\'a}nchez Soler}, M{\`o}nica and {Zahedi Siamak}",

year = "2014",

month = feb,

day = "5",

language = "English",

note = "XVII Congreso Espa{\~n}ol sobre Tecnolog{\'i}as y L{\'o}gica Fuzzy (ESTYLF 2014) ; Conference date: 05-02-2014 Through 07-02-2014",

}

TY - CONF

T1 - Multi-criteria decision making with linguistic labels: A comparison of two methodologies applied to energy planning

AU - Afsordegan, Arayeh

AU - Agell, N.

AU - Cremades Lázaro, null

AU - Sánchez Soler, Mònica

AU - Zahedi Siamak, null

PY - 2014/2/5

Y1 - 2014/2/5

N2 - This paper compares two multi-criteria decision making (MCDM) approaches based on linguis-tic label assessment. The first approach consists of a modified fuzzy TOPSIS methodology in-troduced by Kaya and Kahraman in 2011. The second approach, introduced by Agell et al. in 2012, is based on qualitative reasoning techniques for ranking multi-attribute alternatives in group decision-making with linguistic labels. Both approaches are applied to a case of assess-ment and selection of the most suitable types of energy in a geographical area.

AB - This paper compares two multi-criteria decision making (MCDM) approaches based on linguis-tic label assessment. The first approach consists of a modified fuzzy TOPSIS methodology in-troduced by Kaya and Kahraman in 2011. The second approach, introduced by Agell et al. in 2012, is based on qualitative reasoning techniques for ranking multi-attribute alternatives in group decision-making with linguistic labels. Both approaches are applied to a case of assess-ment and selection of the most suitable types of energy in a geographical area.

M3 - Contribution

T2 - XVII Congreso Español sobre Tecnologías y Lógica Fuzzy (ESTYLF 2014)

Y2 - 5 February 2014 through 7 February 2014

ER -

Multi-criteria decision making with linguistic labels: A comparison of two methodologies applied to energy planning

Resum

Conferència

Fingerprint

Com citar-ho