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A Decision Support System and Visualisation Tools for AHP-GDM

A Decision Support System and Visualisation Tools for AHP-GDM

Alberto Turón, Juan Aguarón, María Teresa Escobar, José María Moreno-Jiménez
Copyright: © 2019 |Volume: 11 |Issue: 1 |Pages: 19
ISSN: 1941-6296|EISSN: 1941-630X|EISBN13: 9781522565406|DOI: 10.4018/IJDSST.2019010101
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MLA

Turón, Alberto, et al. "A Decision Support System and Visualisation Tools for AHP-GDM." IJDSST vol.11, no.1 2019: pp.1-19. http://doi.org/10.4018/IJDSST.2019010101

APA

Turón, A., Aguarón, J., Escobar, M. T., & Moreno-Jiménez, J. M. (2019). A Decision Support System and Visualisation Tools for AHP-GDM. International Journal of Decision Support System Technology (IJDSST), 11(1), 1-19. http://doi.org/10.4018/IJDSST.2019010101

Chicago

Turón, Alberto, et al. "A Decision Support System and Visualisation Tools for AHP-GDM," International Journal of Decision Support System Technology (IJDSST) 11, no.1: 1-19. http://doi.org/10.4018/IJDSST.2019010101

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Abstract

The Precise Consistency Consensus Matrix (PCCM) is a decisional tool for AHP-Group Decision Making (AHP-GDM). Based on the initial pairwise comparison matrices of the individuals, the PCCM constructs a consensus matrix for the group using the concept of consistency. This paper presents a decision support system (PRIOR-PCCM) that facilitates the construction of the PCCM in the context of AHP-GDM, and the calculus of four indicators that allows comparison of the behaviour of group consensus matrices. PRIOR-PCCM incorporates the possibility of considering different weights for the decision makers and includes a module that permits the extension of the initial PCCM which can achieve the minimum number of non-null entries required for deriving priorities or establishing a complete PCCM matrix. It also includes two cardinal indicators for measuring consistency and compatibility and two ordinal indicators for evaluating the number of violations of consistency and priority. The paper introduces some new visualisation tools that improve comprehension of the process followed for obtaining the PCCM matrix and allow the cognitive exploitation of the results. These original contributions are illustrated with a case study.

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