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Sensibility analysis of MCDA using prospective in Brazilian energy sector

Carlos Francisco Simões Gomes ( Production Engineering Department, Universidade Federal Fluminense, Niteroi, Brazil)
Helder Gomes Costa ( Production Engineering Department, Universidade Federal Fluminense, Niteroi, Brazil)
Alexandre P. de Barros ( Production Engineering Department, Universidade Federal Fluminense, Niteroi, Brazil)

Journal of Modelling in Management

ISSN: 1746-5664

Article publication date: 14 August 2017

443

Abstract

Purpose

The purpose of this paper is to present a hybrid modelling that combines concepts and techniques for scenario building together with a Multi-criteria Decision Aid (MCDA) outranking approach. The paper presents a case to illustrate the proposed methodology.

Design/methodology/approach

The research method is a qualitative and quantitative mixture and it is presented as a study case. Bibliographic research is used to construct the theoretical framework. There are a number of studies that develop a sensibility analysis in MCDA modelling; however, none of them explore the robustness of the MCDA solution with use of scenarios variation.

Findings

The methodology allows the criteria that must be taken into account, according to the decision makers’ values and preferences. It is interesting to note that, depending on the scenario, different weights were applied for each criterion, and the performances of alternatives under each criterion has changed as well.

Practical implications

This need arises in decision problems that are susceptible to the influence of scenario variation.

Originality/value

This proposal was applied to a real case that has taken into account six alternatives, with a prospective analysis of three scenarios, evaluated by four criteria. The authors use prospective scenarios to choose the criterion weights and alternatives evaluation.

Keywords

Citation

Gomes, C.F.S., Costa, H.G. and de Barros, A.P. (2017), "Sensibility analysis of MCDA using prospective in Brazilian energy sector", Journal of Modelling in Management, Vol. 12 No. 3, pp. 475-497. https://doi.org/10.1108/JM2-01-2016-0005

Publisher

:

Emerald Publishing Limited

Copyright © 2017, Emerald Publishing Limited

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