Observatorio de I+D+i UPM

Memorias de investigación
Research Publications in journals:
Inferring Consensus Weights from Pairwise Comparison Matrices
Year:2007
Research Areas
  • Operative research,
  • Statistics
Information
Abstract
Pairwise comparison is a popular method for establishing the relative importance of n objects. Its main purpose is to get a set of weights (priority vector) associated with the objects. When the information gathered from the decision maker does not verify some rational properties, it is not easy to search the priority vector. Goal programming is a flexible tool for addressing this type of problem. In this paper, we focus on a group decision-making scenario. Thus, we analyze different methodologies for getting a collective priority vector. The first method is to aggregate general pairwise comparison matrices (i.e., matrices without suitable properties) and then get the priority vector from the consensus matrix. The second method proposes to get the collective priority vector by formulating an optimization problem without determining the consensus pairwise comparison matrix beforehand.
International
Si
JCR
Si
Title
ANN OPER RES
ISBN
0254-5330
Impact factor JCR
0,544
Impact info
Volume
154
Journal number
0
From page
123
To page
132
Month
SIN MES
Ranking
Participants
  • Autor: Carlos Romero Lopez (UPM)
  • Autor: Jacinto Gonzalez Pachon (UPM)
Research Group, Departaments and Institutes related
  • Creador: Grupo de Investigación: Economía y Sostenibilidad del Medio Natural
  • Departamento: Inteligencia Artificial
  • Departamento: Economía y Gestión Forestal
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