The Rank Reversal Problem in Multi-Criteria Decision Making: A Literature Review

Aires, Renan Felinto de Farias; Ferreira, Luciano Vaz

doi:10.1590/0101-7438.2018.038.02.0331

Cited by 95 publications

(70 citation statements)

References 133 publications

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“…In spite of its many advantages, the TOPSIS method, like many multi-attribute decision-making methods, can produce "rank reversal" outcomes, as several authors have described [32][33][34]. In this phenomenon, the alternatives' order of preference changes when an alternative is added to or removed from the decision problem.…”

Section: Methodsmentioning

confidence: 99%

Sustainability Performance. A Comparative Analysis in the Polish Banking Sector

Korzeb

Samaniego‐Medina

2019

Sustainability

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This article analyses the Polish banking sector’s involvement with sustainable development through a multidimensional evaluation applying the technique for order preference by similarity ideal solution (TOPSIS) method with different weight vectors. Our results highlight numerous shortcomings in the sustainability performance of commercial banking activities. In fact, there was backsliding during the analysed period (2015–2017), which suggests that supporting sustainability performance was not one of the priorities of the Polish banking sector. However, we found a dichotomy between national and foreign banks. The government-owned banks and national banks showed greater commitment to this issue than did the banks with foreign capital. This finding suggests that banks with foreign capital were not fully interested in sponsoring activities aimed at sustainable development.

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Section: Methodsmentioning

confidence: 99%

Sustainability Performance. A Comparative Analysis in the Polish Banking Sector

Korzeb

Samaniego‐Medina

2019

Sustainability

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“…For instance, several studies have debated about the rank reversal problem presented in AHP, a phenomenon consisting of changes observed in the final ranking order after the addition or removal of an alternative (Ishizaka & Labib, ; Rodríguez, Ortega, & Concepción, ; Aires & Ferreira, ). This problem is also a limitation in DEA (Shin, ; Soltanifar & Shahghobadi, ) and Simple Additive Weighting (Aires & Ferreira, ; Bendaoud, Didi, & Abdennebi, ; Shin, Lee, Chun, & Chung, ). Another constraint in the DEA method is that positive outputs and inputs are needed for each decision‐making unit (Huang & Li, ).…”

Section: Primary Studies From the Literaturementioning

confidence: 99%

“…The next step is then to identify the most suitable MCDM approach to address this particular aim (see the coming subsection). For instance, several studies have debated about the rank reversal problem presented in AHP, a phenomenon consisting of changes observed in the final ranking order after the addition or removal of an alternative (Ishizaka & Labib, 2009;Rodríguez, Ortega, & Concepción, 2016;Aires & Ferreira, 2018). This problem is also a limitation in DEA (Shin, 2017;Soltanifar & Shahghobadi, 2014) and…”

Section: Modelling Adoption Of Assistive Technologymentioning

confidence: 99%

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Selecting the most suitable classification algorithm for supporting assistive technology adoption for people with dementia: A multicriteria framework

Ortíz‐Barrios

Nugent

Cleland

et al. 2019

Multi Criteria Decision Anal

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The number of people with dementia (PwD) is increasing dramatically. PwD exhibit impairments of reasoning, memory, and thought that require some form of self‐management intervention to support the completion of everyday activities while maintaining a level of independence. To address this need, efforts have been directed to the development of assistive technology solutions, which may provide an opportunity to alleviate the burden faced by the PwD and their carers. Nevertheless, uptake of such solutions has been limited. It is therefore necessary to use classifiers to discriminate between adopters and nonadopters of these technologies in order to avoid cost overruns and potential negative effects on quality of life. As multiple classification algorithms have been developed, choosing the most suitable classifier has become a critical step in technology adoption. To select the most appropriate classifier, a set of criteria from various domains need to be taken into account by decision makers. In addition, it is crucial to define the most appropriate multicriteria decision‐making approach for the modelling of technology adoption. Considering the above‐mentioned aspects, this paper presents the integration of a five‐phase methodology based on the Fuzzy Analytic Hierarchy Process and the Technique for Order of Preference by Similarity to Ideal Solution to determine the most suitable classifier for supporting assistive technology adoption studies. Fuzzy Analytic Hierarchy Process is used to determine the relative weights of criteria and subcriteria under uncertainty and Technique for Order of Preference by Similarity to Ideal Solution is applied to rank the classifier alternatives. A case study considering a mobile‐based self‐management and reminding solution for PwD is described to validate the proposed approach. The results revealed that the best classifier was k‐nearest‐neighbour with a closeness coefficient of 0.804, and the most important criterion when selecting classifiers is scalability. The paper also discusses the strengths and weaknesses of each algorithm that should be addressed in future research.

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“…e problem of rank reversal in the TOPSIS method has received great attention, and different scholars have tried to find satisfactory solutions from an experimental or theoretical level [52][53][54][55][56][57]. By collecting 130 related papers published in international journals from 1980 to 2015, Aires and Ferreira provided an extensive literature review on MCDM methodologies and rank reversals, including the TOPSIS method [52]. Zavadskas et al [53] also reviewed the development of the TOPSIS method from 2000 to 2015, but did not discuss the solution of rank reversal.…”

Section: Introductionmentioning

confidence: 99%

Ingenious Solution for the Rank Reversal Problem of TOPSIS Method

Wei-guo

2020

Mathematical Problems in Engineering

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Although the classic TOPSIS method is very practical, there may be a problem of rank reversal in the addition, deletion, or replacement of the candidate set, which makes its credibility greatly compromised. Based on the understanding of the classical TOPSIS method, this paper establishes a new improved TOPSIS method called NR-TOPSIS. Firstly, the historical maximum and minimum values of all attribute indicators from a global perspective during the evaluation process are determined. Secondly, according to whether the attributes belong to the benefit attribute or cost attribute, standardization is carried out. And then, in the case where the historical values of attributes are determined, we re-fix the positive ideal solution and the negative ideal solution. At the same time, this paper gives the definition of ranking stable and proves that the NR-TOPSIS proposed satisfies ranking stable, which theoretically guarantees that the rank reversal phenomenon does not exist. Finally, in the verification of examples, the results are consistent with the theoretical analysis, which further support the theoretical analysis. The NR-TOPSIS method overcomes rank reversal, which is not only obviously superior to the classical TOPSIS method but also relatively superior to the R-TOPSIS method which has also overcome rank reversal. It is also superior to other reference methods due to its simple calculation.

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The Rank Reversal Problem in Multi-Criteria Decision Making: A Literature Review

Cited by 95 publications

References 133 publications

Sustainability Performance. A Comparative Analysis in the Polish Banking Sector

Sustainability Performance. A Comparative Analysis in the Polish Banking Sector

Selecting the most suitable classification algorithm for supporting assistive technology adoption for people with dementia: A multicriteria framework

Ingenious Solution for the Rank Reversal Problem of TOPSIS Method

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