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Previous issue date: 2017-09-29 / Durante as ?ltimas d?cadas, v?rios m?todos de Apoio Multicrit?rio ? Decis?o (MCDM) t?m
sido utilizados para auxiliar decisores na sele??o de melhores alternativas para problemas
de decis?o diversos. Dentre eles, o Technique for Order Preference by Similarity to Ideal
Solution (TOPSIS) ? um dos mais utilizados. Apesar da sua grande difus?o, este tem
sido criticado devido ? ocorr?ncia de um problema chamado rank reversal (revers?o de
ordena??o), que, em sua mais conhecida denomina??o, se refere ? mudan?a na ordena??o
de um grupo de alternativas anteriormente ordenadas ap?s uma alternativa irrelevante ter
sido adicionada ou exclu?da desse grupo. Apesar da quantidade significativa de estudos
sobre este problema para os m?todos MCDM, tem-se que a an?lise desse problema para o
TOPSIS ainda ? feita de forma superficial, sem um estudo criterioso acerca das causas e
condi??es de ocorr?ncia, bem como marcada por proposi??es de modelos inadequados. Por
conta disso, o objetivo desse estudo foi propor uma extens?o do m?todo TOPSIS para
minimizar a revers?o de ordena??o. Para isso, foi realizada uma pesquisa experimental a
partir de simula??es computacionais geradas aleatoriamente com base em quatro situa??es
de revers?o selecionados na literatura. Nos casos de ambas as problem?ticas investigadas,
de escolha e de ordena??o, foram analisados os efeitos da normaliza??o utilizada e dos
limiares de indiferen?a. Adicionalmente, os casos da problem?tica de escolha tamb?m foram
analisados a partir da regress?o log?stica, no intuito de estimar as condi??es em que h? uma
maior probabilidade de ocorr?ncia de revers?o de ordena??o. Com base nos experimentos
e na an?lise dos modelos da literatura, foi proposta uma extens?o do TOPSIS. O modelo
proposto ? baseado na defini??o de um conjunto de valores intitulado de Dom?nio, que
representa os valores limites de cada crit?rio na matriz de decis?o no intuito de ultrapassar
os inconvenientes do TOPSIS. Para a valida??o da proposta, foi realizada uma aplica??o
num?rica para a problem?tica de sele??o de estudantes e concluiu-se que o modelo proposto
? robusto por, simultaneamente, evitar a ocorr?ncia da revers?o de ordena??o e apresentar
uma boa capacidade discriminat?ria. / During the last decades, various multi-criteria decision-making methods (MCDM) have
been used to assist decision makers in selecting the best alternatives for many decision
problems. Among them, the Technique for Order Preference by Similarity to Ideal Solution
(TOPSIS) is one of the most used. Despite its wide dissemination, it has been criticized
due to the occurrence of a problem called rank reversal, which in its most known meaning
refers to the change in the ordering of a group of previously ordered alternatives after an
irrelevant alternative has been added or removed from this group. Despite the significant
amount of research on this problem for MCDM methods, it has been superficially analyzed
in the case of TOPSIS, without a careful study on the occurrence causes and conditions,
as well as marked by propositions inadequate models. Therefore, the aim of this study was
to propose an extension of the TOPSIS method to minimize rank reversal. For this, it was
realized an experimental research through computer simulations randomly generated based
on four reversal situations selected in the literature. In the cases of the both problems types
investigated, of choice and rank, the effects of the normalization used and the indifference
thresholds were analyzed. In addition, the cases of the problem of choice were also analyzed
from the logistic regression, in order to estimate the conditions in which there is a greater
probability of occurrence of rank reversal. Based on the experiments and analysis of the
literature models, an extension of TOPSIS was proposed. The proposed model is based on
the definition of a set of values called Domain, which represents the limit values of each
criterion in the decision matrix in order to overcome the drawbacks of TOPSIS. For the
validation of the proposal, a numerical application was made for the problem of student
selection and it was concluded that the proposed model is robust because it simultaneously
prevents the occurrence of ranking reversal and presents a good discriminatory capacity.
| Identifer | oai:union.ndltd.org:IBICT/oai:repositorio.ufrn.br:123456789/24358 |
| Date | 29 September 2017 |
| Creators | Aires, Renan Felinto de Farias |
| Contributors | 70515255068, Almeida Filho, Adiel Teixeira de, 03935782403, Leoneti, Alexandre Bevilacqua, 26194658851, Gurgel, Andr? Morais, 05345000484, Sampaio, Luciano Menezes Bezerra, 00754306496, Ferreira, Luciano |
| Publisher | PROGRAMA DE P?S-GRADUA??O EM ADMINISTRA??O, UFRN, Brasil |
| Source Sets | IBICT Brazilian ETDs |
| Language | Portuguese |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, info:eu-repo/semantics/doctoralThesis |
| Source | reponame:Repositório Institucional da UFRN, instname:Universidade Federal do Rio Grande do Norte, instacron:UFRN |
| Rights | info:eu-repo/semantics/openAccess |
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