An integrated constrained fuzzy stochastic analytic hierarchy process method with application to the choice problem
File(s)Sitorus et al 2019 _spiral.pdf (3.13 MB)
Accepted version
Author(s)
Fernando, Fernando
Cilliers, Jan
Brito Parada, Pablo Rafael
Type
Journal Article
Abstract
The ability of the analytical hierarchy process (AHP) when applied to the choice problem in the context of group decision making under uncertainty has been often criticised. AHP is not able to fully capture the various opinions and the uncertainty associated with the lack of information. This work develops an integrated constrained fuzzy stochastic analytic hierarchy process (IC-FSAHP) method in order to deal with the aforementioned drawbacks. IC-FSAHP combines two existing fuzzy AHP (FAHP) methods and further extends its applicability by implementing stochastic simulations. A case study has been conducted in order to assess the ability of IC-FSAHP; the results showed that IC-FSAHP is able to capture the uncertainty and multiple DMs' opinions. This paper also discusses the effect that the number of DMs has in enhancing rank discrimination. Besides, the possibility of the occurrence of rank reversal because of the use of IC-FSAHP has been analysed. The results showed that the ranking of alternatives was preserved throughout the changes in the number of alternatives, however, rank reversal occurred in the case of changes in judgements scales. By comparing the U-uncertainty in fuzzy global priorities obtained using IC-FSAHP to that obtained using an existing FSAHP method, we show that our method is capable of minimising the risk of losing important knowledge during the computations. We also discuss how IC-FSAHP can decrease the uncertainty and increase the reliability of the decisions by means of robust computations.
Date Issued
2019-12-30
Date Acceptance
2019-07-16
Citation
Expert Systems with Applications, 2019, 138, pp.1-25
ISSN
0957-4174
Publisher
Elsevier
Start Page
1
End Page
25
Journal / Book Title
Expert Systems with Applications
Volume
138
Copyright Statement
© 2019 Elsevier Ltd. All rights reserved. This manuscript is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence http://creativecommons.org/licenses/by-nc-nd/4.0/
Sponsor
EC‘s Framework Programme for Research and Innovation Horizon 2020
Identifier
https://www.sciencedirect.com/science/article/pii/S095741741930524X
Grant Number
637077
Subjects
01 Mathematical Sciences
08 Information and Computing Sciences
Artificial Intelligence & Image Processing
Publication Status
Published online
Date Publish Online
2019-07-19