Machine learning-driven approach for large scale decision making with the analytic hierarchy process

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dc.creatorMarcos Antonio Alves
dc.creatorIvan Reinaldo Meneghini
dc.creatorAntónio Gaspar-Cunha
dc.creatorFrederico Gadelha Guimarães
dc.date.accessioned2025-02-19T18:34:10Z
dc.date.accessioned2025-09-08T23:50:01Z
dc.date.available2025-02-19T18:34:10Z
dc.date.issued2023-01-26
dc.format.mimetypepdf
dc.identifier.doihttps://doi.org/10.3390/math11030627
dc.identifier.issn2227-7390
dc.identifier.urihttps://hdl.handle.net/1843/80231
dc.languageeng
dc.publisherUniversidade Federal de Minas Gerais
dc.relation.ispartofMathematics
dc.rightsAcesso Aberto
dc.subjectEngenharia elétrica
dc.subjectMáquinas
dc.subjectAlgorítmos computacionais
dc.subjectAprendizagem baseada em problemas
dc.subject.otherScalable decision making
dc.subject.otherPairwise matrices
dc.subject.otherMulti-attribute decision methods
dc.subject.otherOnline machine learning
dc.subject.otherAnalytic hierarchy process
dc.titleMachine learning-driven approach for large scale decision making with the analytic hierarchy process
dc.typeArtigo de periódico
local.citation.issue3
local.citation.volume11
local.description.resumoThe Analytic Hierarchy Process (AHP) multicriteria method can be cognitively demanding for large-scale decision problems due to the requirement for the decision maker to make pairwise evaluations of all alternatives. To address this issue, this paper presents an interactive method that uses online learning to provide scalability for AHP. The proposed method involves a machine learning algorithm that learns the decision maker’s preferences through evaluations of small subsets of solutions, and guides the search for the optimal solution. The methodology was tested on four optimization problems with different surfaces to validate the results. We conducted a one factor at a time experimentation of each hyperparameter implemented, such as the number of alternatives to query the decision maker, the learner method, and the strategies for solution selection and recommendation. The results demonstrate that the model is able to learn the utility function that characterizes the decision maker in approximately 15 iterations with only a few comparisons, resulting in significant time and cognitive effort savings. The initial subset of solutions can be chosen randomly or from a cluster. The subsequent ones are recommended during the iterative process, with the best selection strategy depending on the problem type. Recommendation based solely on the smallest Euclidean or Cosine distances reveals better results on linear problems. The proposed methodology can also easily incorporate new parameters and multicriteria methods based on pairwise comparisons.
local.identifier.orcidhttps://orcid.org/0000-0001-6934-6745
local.identifier.orcidhttps://orcid.org/0000-0002-2572-4924
local.identifier.orcidhttps://orcid.org/0000-0001-7777-7625
local.identifier.orcidhttps://orcid.org/0000-0001-9238-8839
local.publisher.countryBrasil
local.publisher.departmentICX - DEPARTAMENTO DE CIÊNCIA DA COMPUTAÇÃO
local.publisher.initialsUFMG
local.url.externahttps://www.mdpi.com/2227-7390/11/3/627

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