Selected Issues of Rank Reversal Problem in ANP Method

2016 ◽  
pp. 203-225 ◽  
Author(s):  
Paweł Ziemba ◽  
Jarosław Wątróbski
Keyword(s):  
Reversal Problem ◽  
Rank Reversal

A Robust and Efficient MCDM-Based Framework for Cloud Service Selection Using Modified TOPSIS

2021 ◽  
Vol 11 (1) ◽  
pp. 21-51
Author(s):  
Rohit Kumar Tiwari ◽  
Rakesh Kumar
Keyword(s):  
Service Provider ◽  
Service Providers ◽  
Service Selection ◽  
Cloud Service ◽  
Reversal Problem ◽  
Rank Reversal ◽  
Reversal Phenomenon ◽  
Optimal Service ◽  
Selection Framework ◽  
Cloud Users

Cloud computing has become a business model and organizations like Google, Amazon, etc. are investing huge capital on it. The availability of many organizations in the cloud has posed a challenge for cloud users to choose a best cloud service. To assist the cloud users, we have proposed a MCDM-based cloud service selection framework to choose a best service provider based on QoS requirement. The cloud service selection methods based on TOPSIS suffers from rank reversal problem as it ranks optimal service provider to non-optimal on addition or removal of a service provider and deludes the cloud user. Therefore, a robust and efficient TOPSIS (RE-TOPSIS)-based novel framework has been proposed to rank the cloud service providers using QoS provided by them and cloud user's priority for each QoS. The proposed framework is robust to rank reversal problem and its effectiveness has been demonstrated through a case study performed on a real dataset. Sensitivity analysis has also been performed to show the robustness against the rank reversal phenomenon.

Rank Reversal and Rank Preservation in TOPSIS

2011 ◽  
Vol 204-210 ◽  
pp. 36-41 ◽  
Author(s):  
Feng Kong
Keyword(s):  
Decision Makers ◽  
Reversal Problem ◽  
Numerical Example ◽  
Rank Reversal ◽  
Fundamental Cause ◽  
Scientific Decision ◽  
Rank Preservation

TOPSIS has been in use for more than 20 years in many fields. However, few researches have studied its defect of rank reversal. This paper, by studying the fundamental cause for the rank reversal problem in TOPSIS, put forward an improvement on TOPSIS based on the decision maker’s subjective preferences, which can, proved by a numerical example, overcome the rank reversal problem and lead to more scientific decision result that is in more agreement with the decision makers’ subjective intensions.

scholarly journals Eliminating Rank Reversal Problem Using a New Multi-Attribute Model—The RAFSI Method

Mathematics ◽  
2020 ◽  
Vol 8 (6) ◽  
pp. 1015 ◽  
Author(s):  
Mališa Žižović ◽  
Dragan Pamučar ◽  
Miloljub Albijanić ◽  
Prasenjit Chatterjee ◽  
Ivan Pribićević
Keyword(s):  
Decision Making ◽  
Real World ◽  
Mathematical Formulation ◽  
Simple Algorithm ◽  
Data Normalization ◽  
Reversal Problem ◽  
Rank Reversal ◽  
Multi Attribute Decision Making ◽  
Attribute Model ◽  
Real World Problems

Multi-attribute decision-making (MADM) methods represent reliable ways to solve real-world problems for various applications by providing rational and logical solutions. In reaching such a goal, it is expected that MADM methods would eliminate inconsistencies like rank reversal issues in a given solution. In this paper, an endeavor is taken to put forward a new MADM method, called RAFSI (Ranking of Alternatives through Functional mapping of criterion sub-intervals into a Single Interval), which successfully eliminates the rank reversal problem. The developed RAFSI method has three major advantages that recommend it for further use: (i) its simple algorithm helps in solving complex real-world problems, (ii) RAFSI method has a new approach for data normalization, which transfers data from the starting decision-making matrix into any interval, suitable for making rational decisions, (iii) mathematical formulation of RAFSI method eliminates the rank reversal problem, which is one of the most significant shortcomings of existing MADM methods. A real-time case study that shows the advantages of RAFSI method is presented. Additional comprehensive analysis, including a comparison with other three traditional MADM methods that use different ways for data normalization and testing the resistance of RAFSI method and other MADM methods to rank the reversal problem, is also carried out.

A method to analyze the rank reversal problem in the ELECTRE II method

Omega ◽  
2020 ◽  
pp. 102317
Author(s):  
Xianliang Liu ◽  
Yonghao Ma
Keyword(s):  
Reversal Problem ◽  
Rank Reversal

scholarly journals A NEW LOGARITHM METHODOLOGY OF ADDITIVE WEIGHTS (LMAW) FOR MULTI-CRITERIA DECISION-MAKING: APPLICATION IN LOGISTICS

2021 ◽  
Vol 19 (3) ◽  
pp. 361
Author(s):  
Dragan Pamučar ◽  
Mališa Žižović ◽  
Sanjib Biswas ◽  
Darko Božanić
Keyword(s):  
Decision Making ◽  
Comparative Analysis ◽  
Outcome Variable ◽  
Multi Criteria Decision Making ◽  
Competitive Growth ◽  
Reversal Problem ◽  
Rank Reversal ◽  
Logistics Service Provider ◽  
Operational Metrics

Logistics management has been playing a significant role in ensuring competitive growth of industries and nations. This study proposes a new Multi-Criteria Decision-making (MCDM) framework for evaluating operational efficiency of logistics service provider (LSP). We present a case study of comparative analysis of six leading LSPs in India using our proposed framework. We consider three operational metrics such as annual overhead expense (OE), annual fuel consumption (FC) and cost of delay (CoD, two qualitative indicators such as innovativeness (IN) which basically indicates process innovation and average customer rating (CR)and one outcome variable such as turnover (TO) as the criteria for comparative analysis. The result shows that the final ranking is a combined effect of all criteria. However, it is evident that IN largely influences the ranking. We carry out a comparative analysis of the results obtained from our proposed method with that derived by using existing established frameworks. We find that our method provides consistent results; it is more stable and does not suffer from rank reversal problem.

scholarly journals THE RANK REVERSAL PROBLEM IN MULTI-CRITERIA DECISION MAKING: A LITERATURE REVIEW

2018 ◽  
Vol 38 (2) ◽  
pp. 331-362 ◽  
Author(s):  
Renan Felinto de Farias Aires ◽  
Luciano Ferreira
Keyword(s):  
Decision Making ◽  
Literature Review ◽  
Multi Criteria Decision Making ◽  
Reversal Problem ◽  
Rank Reversal

scholarly journals Ingenious Solution for the Rank Reversal Problem of TOPSIS Method

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Wenguang Yang
Keyword(s):  
Theoretical Analysis ◽  
Evaluation Process ◽  
Simple Calculation ◽  
Global Perspective ◽  
Ideal Solution ◽  
Topsis Method ◽  
Reversal Problem ◽  
Rank Reversal ◽  
Negative Ideal Solution ◽  
Definition Of

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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