Rank Reversal Problem Research Articles

TOPSIS-RTCID for range target-based criteria and interval data

<p>The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is receiving considerable attention as an essential decision analysis technique and becoming a leading method. This paper describes a new version of TOPSIS with interval data and capability to deal with all types of criteria. An improved structure of the TOPSIS is presented to deal with high uncertainty in engineering and engineering decision-making. The proposed Range Target-based Criteria and Interval Data model of TOPSIS (TOPSIS-RTCID) achieves the core contribution in decision making theories through a distinct normalization formula for cost and benefits criteria in scale of point and range target-based values. It is important to notice a very interesting property of the proposed normalization formula being opposite to the usual one. This property can explain why the rank reversal problem is limited. The applicability of the proposed TOPSIS-RTCID method is examined with several empirical litreture’s examples with comparisons, sensitivity analysis, and simulation. The authors have developed a new tool with more efficient, reliable and robust outcomes compared to that from other available tools. The complexity of an engineering design decision problem can be resolved through the development of a well-structured decision making method with multiple attributes. Various decision approches developed for engineering design have neglected elements that should have been taken into account. Through this study, engineering design problems can be resolved with greater reliability and confidence.</p>

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

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.

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

In multi-criteria decision-making (MCDM) methods, the rank reversal is a highly important problem for analyzing the reliability of MCDM methods. The main objective of this paper is to analyze and precisely predict the occurrence of rank reversal in the ELECTRE II method. First, some useful properties of the optimal alternatives are obtained in the descending and ascending distillation processes. Second, two new concepts named combination relation digraph and numerator matrix are proposed to precisely analyze and greatly simplify the rank reversal in the ELECTRE II method. Third, all the reasons for the rank reversal in the ELECTRE II method are revealed more completely. Fourth, the necessary and sufficient conditions are proposed for determining whether or not the rank reversal occurs in the ELECTRE II method. Additionally, the proposed methods in this paper are valid for analyzing the rank reversal in the ELECTRE method with discriminating thresholds. Finally, five examples are used to evaluate the correctness and effectiveness of the methods that are proposed in this paper.

QoE Aware IoT Application Placement in Fog Computing Using Modified-TOPSIS

Over the years, fog computing has emerged as a paradigm to complement the cloud computing in handling the delay sensitive IoT applications in a better manner. Using fog resources, better performance such as in-time service delivery, reduced network load, optimal energy usage etc. can be achieved. With such performance gain, users availing the IoT services are more satisfied. A well-known metric Quality of Experience (QoE), used to measure the satisfaction of IoT users, can be improved by enhancing the performance of the IoT applications. Fog computing is a geographically distributed paradigm and primary service of fog computing may not include the execution of offloaded tasks/applications from the IoT devices. This makes QoE aware placement of applications in fog computing a greater challenge. Since placement algorithm is itself a computational task and both IoT applications and fog nodes need a mediator fog node to execute the placement algorithm, the placement policy should be light weighted in terms of computational complexity. This work proposes a lightweight QoE aware application placement policy in fog computing using Modified TOPSIS that prioritizes the applications and fog instances based on their expectation and computational capability respectively for the placement. Modified TOPSIS inherits all the features of classical TOPSIS while it removes rank reversal problem of classical TOPSIS. Simulation experiments, for a comparative study, depict that the proposed model not only achieves the desired resource utilization, processing time, and reduced network congestion but reduces the application placement time also significantly compared to the state of art.

A novel multi-attribute decision-making framework based on Z-RIM: an illustrative example of cloud service selection

In the multi-attribute decision-making (MADM) problems, decision makers refer to the extreme attribute values in general when evaluating the alternatives. However, in the real world, the ideal solution may lie in somewhere between the extreme values. The recently proposed reference ideal method (RIM) is able to solve the problem rightly. This study aims at developing a novel MADM framework combining best–worst method (BWM), maximizing deviation method (MDM), and RIM under Z-number environment. In this framework, Z-number is used to depict the inherent uncertainty and reliability of information in the decision makers’ judgments. And BWM and MDM are combined to determine the comprehensive attribute weights, in which BWM is utilized to obtain the subjective weights, while MDM is utilized to obtain the objective weights. In addition, Z-RIM is proposed by extending the traditional RIM under Z-number environment, which is employed for ranking the alternatives. An illustrative example of cloud service selection problem is implemented to illustrate the proposed framework. By comparison analysis, we demonstrate that Z-RIM can not only avoid rank reversal problem, but also generate reasonable results during the MADM processes.

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

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 framework for prioritizing cloud services in neutrosophic environment

Cloud service selection assists cloud users to find the best cloud services as per their needs and minimizes the loss occurring due to improper selection of services. This paper aims to develop a cloud service selection framework for the neutrosophic environment using single-valued neutrosophic set (SVNS) theory and multi-criteria decision making (MCDM) based technique for order of preference by similarity to ideal solution (TOPSIS). The SVNS helps cloud users and experts to express their opinion in linguistic terms rather than crisp value due to partial knowledge involving some degree of truth, indeterminacy and falsehood. TOPSIS is used to rank cloud services efficiently. A case study has been performed on a real dataset obtained from CloudHarmony to demonstrate its practicality and usefulness. Sensitivity analysis has been carried out with the addition and deletion of cloud services to rank them and found that the framework is consistent and robust to rank reversal problem. The framework is also capable to strongly handle a fuzzy environment without rank reversal phenomenon in comparison with other MCDM based cloud service selection frameworks available in the literature.

Best-Worst PROMETHEE method for evaluating school performance in the OECD's PISA project

This paper introduces the Best-Worst PROMETHEE method, which avoids the rank reversal problem of other PROMETHEE methods. We use this new technique to rank schools in the Programme for International Student Assessment (PISA). We consider three separate outputs, provided by average student attainments in mathematics, language, and sciences. Our sample comprises 16,500 schools in 66 countries, and our results show significant differences between school performance both within and between countries. The top half of the ranking mainly comprises European and Asian countries, while the bottom half includes many North African, Middle Eastern, and South American countries. We find no strong association between inequality between schools and the country-level performance, suggesting the absence of a trade-off between equity and performance in education. The Best-Worst PROMETHEE is a generic method that can be used to support decisions in strategic sectors with multidimensional outcomes.

A framework for IoT service selection

IoT is getting popular as it makes human life comfortable. The industry giants such as IBM, Microsoft, Cisco and Amazon have started offering IoT assistance in form of services. Numerous IoT applications exist today with different roles to play in day-to-day life. Because of application diversity and a good number of IoT service providers, it is difficult for IoT users to select the best one as per the requirement and expected quality of service, QoS. To address this, QoS metrics related to major IoT components, i.e., communication, computing and things, are designed to assess the alternative services. IoT users can express their requirements regarding QoS, while service providers exhibit their offerings. Because of three major IoT components, service selection is considered as multi-criteria group decision-making (MCGDM) problem. This work proposes a new MCGDM framework to rank the IoT services that considers rank reversal problem, judgments of decision makers in linguistic term and the uncertainty and risk-attitudinal characteristics in human decision-making. The proposed framework is validated by comparing it with an existing MCGDM model. A case study on IoT health-care application is provided besides the sensitivity analysis to demonstrate the effectiveness of the proposed framework.

Selection of E-learning websites using a novel Proximity Indexed Value (PIV) MCDM method

This paper presents application of a newly developed multi-criteria decision-making (MCDM) method, i.e. Proximity Indexed Value (PIV) method for the ranking and selection of the E-learning websites. PIV is a computationally simpler method as compared to other MCDM methods such as AHP, VIKOR, COPRAS, WEDBA, WDBA, and it also minimises the rank reversal problem. The applicability and efficacy of the PIV method has been demonstrated with the help of two illustrative examples pertaining to the selection of the E-learning websites which have already been solved by researchers using different MCDM methods. Results of this study revealed that the ranking of the E-learning websites obtained by the PIV method exactly matched with those derived by AHP, VIKOR and COPRAS. However, a small difference in the ranking by PIV method with those of WEDBA and WDBA was observed. It suggests that PIV method is a simple, effective and efficient method which can be used to solve different types of problems related to the ranking and selection of alternatives.

Integrating corporate social responsibility and financial performance

PurposeCorporate social responsibility (CSR) rating agencies have arisen with the aim of providing external and reliable information about business behaviour. The purpose of this paper is to present a multi-criteria methodology for integrating CSR valuations with the financial performance of companies in a unique measure of global sustainability performance.Design/methodology/approachThe authors present a hybrid TOPSIS methodology on transformed scores of both the CSR valuations and the financial ratios. The “attribute-specific evaluation” approach into Multi-attribute Prospect Theory (PT) has been applied and the Design of Experiments (DoE) is used with the TOPSIS value of the firm as the response variable.FindingsThe proposal has been applied to 118 companies evaluated by Vigeo and Covalence CSR agencies. The authors also have considered five financial ratios of the companies in order to assess their financial performance. Consistent aggregation for firms has been achieved. Relationships between the different rankings, both those of Vigeo and Covalence and the ones constructed in this research, have been analysed. All top 10 Global sustainable firms rank among the top 10 positions in at least one of the remaining rankings. The results show that Vigeo and Covalence provide different information about the CSR behaviour of the companies.Research limitations/implicationsAnother interesting question is to study the discrepancies between Vigeo and Covalence, for example, in which areas there is the greatest divergence between the two agencies and what could be the reasons for this.Practical implicationsThe results of this research could be of interest for both investors who want a global picture of companies in their selection process and stakeholders concerned with CSR issues who want to take advantage of different CSR ratings.Originality/valueThe application of PT softens the compensatory behaviour of the classical TOPSIS that may prove unsuitable for social evaluation. The DoE allows the aggregation of the weight sets from various decision markers. The combined methodology facilitates the scoring of new firms and the rank reversal problem can be mitigated with this methodology.

A new multi-criteria decision making approach for sustainable material selection problem: A critical study on rank reversal problem

Abstract The selection of appropriate materials for sustainable development of products is a very intricate process. Sustainable material selection problems with various non commensurable and incompatible decision factors can be efficiently handled using multi-criteria decision making (MCDM) methods. However, these methods suffer from a general and famous problem, namely rank reversal. In this phenomenon the ordering of alternatives inverts when an alternative is added or eliminated from the list of alternatives. In this paper, we reveal that COPRAS (Complex Proportional Assessment), TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje, Serbian term) as three of the most popular techniques in material selection area may produce rank reversal when an option is removed from or added to a decision process. Moreover, we provide a comparative analysis about the rank reversal problem in the aforementioned MCDM methods. To the best of our knowledge, this work is the first to study rank reversal phenomenon for general material selection problem using more than 100 scientific references. Finally, we suggest a new MCDM-based method for helping decision maker (DM) to select the best sustainable material against the rank reversal pitfall. We exemplify our reasoning with thirteen real-world cases adopted from literature.

SAFFRON: A Semi-Automated Framework for Software Requirements Prioritization

Due to dynamic nature of current software development methods, changes in requirements are embraced and given proper consideration. However, this triggers the rank reversal problem which involves re-prioritizing requirements based on stakeholders' feedback. It incurs significant cost because of time elapsed in large number of human interactions. To solve this issue, a Semi-Automated Framework for soFtware Requirements priOritizatioN (SAFFRON) is presented in this paper. For a particular requirement, SAFFRON predicts appropriate stakeholders' ratings to reduce human interactions. Initially, item-item collaborative filtering is utilized to estimate similarity between new and previously elicited requirements. Using this similarity, stakeholders who are most likely to rate requirements are determined. Afterwards, collaborative filtering based on latent factor model is used to predict ratings of those stakeholders. The proposed approach is implemented and tested on RALIC dataset. The results illustrate consistent correlation, similar to state of the art approaches, with the ground truth. In addition, SAFFRON requires 13.5-27% less human interaction for re-prioritizing requirements.

QoS Based Cloud Service Selection to Handle Large Volume of Concurrent Requests

Background/Objectives: With the exponential growth of cloud providers and users, the possibility of arrival of large number of concurrent requests within a small interval, may reduce the performance of broker. This paper presents a cloud service selection mechanism with service grouping. The main objective of this paper to show the performance difference between the selection mechanism with grouping and without grouping. Methods/Statistical Analysis: For the concurrent services groups and sub groups are created using functional and QoS parameters. Analytical hierarchy based selection algorithm is proposed. Performance of selection algorithm with grouping is evaluated. Performance of selection algorithm with grouping is compared with algorithm without grouping and the sufficient reduction in execution time is noted. Findings: AHP based selection algorithm with grouping improves the performance by reducing the computation time and reducing the repetitive repository access. Applications/Improvements: The limitation of AHP based algorithm is the rank reversal problem. The improved AHP based algorithm can be used to eliminate the rank reversal problem.

Rank reversal and Rank Preservation in ANP method

The analytic network process (ANP) as a generalization of the analytic hierarchy process (AHP) can also produce rank reversal phenomenon in multiple criteria decision making (MCDM).The reverse phenomenon can influence the correctness of our decision-makers greatly. So the research of rank reversal has great significance in theoretical and practical application. In this paper through the analysis of its calculation process can find that rank reversal is caused by change of ratio among weight values before and after an alternative is added. And use a numerical example to illustrate the rank reversal in the ANP method, look into the cause of that phenomenon and find that rank reversal is caused by change of ratio among weight values before and after an alternative is added. An approach is also proposed based on ideal point to keep the ratio among weight values unchanged to avoid reversal phenomenon and verify the validity of our proposed approach in rank preservation. And we use a numerical instance to verified the approach which can effectively solve the rank reversal problem.

Simplified and improved multiple attributes alternate ranking method for vertical handover decision in heterogeneous wireless networks

Multiple Attribute Decision Making (MADM) is one of the best candidate network selection methods used for Vertical Handover Decision (VHD) in heterogeneous wireless networks (4G). Selection of the network in MADM is predominantly decided by two steps, i.e., attribute normalization and weight calculation. This dependency in MADM results in an unreliable network selection for handover, and in a rank reversal (abnormality) problem during the removal and insertion of the network in the network selection list. Hence, this paper proposes a Simplified and Improved Multiple Attributes Alternate Ranking method referred to as SI-MAAR to eliminate the attribute normalization and weight calculation methods, thereby solving the rank reversal problem. Further, the MATLAB simulation results demonstrate that the proposed SI-MAAR method outperforms MADM methods such as TOPSIS, SAW, MEW and GRA with respect to the network selection reliability and rank reversal problems.

Analytic hierarchy process and technique for order preference by similarity to ideal solution: a bibliometric analysis 'from' past, present and future of AHP and TOPSIS

Previous review papers on analytic hierarchy process AHP and technique for order preference by similarity to ideal solution TOPSIS mainly focused on the application areas and paid scant attention to the framework development of AHP, TOPSIS and their hybrid methods. The purpose of this paper is to review the literature on AHP, type of scale used in AHP, modified AHP, rank reversal problem of AHP, validation of AHP, application of AHP, TOPSIS, normalisation methods for TOPSIS, distance functions for TOPSIS, fuzzy hierarchical TOPSIS, rank reversal problem of TOPSIS and various applications of TOPSIS to prepare a ready-made reference for academician, research scholar and industry people. In this regard, research works are gathered from 1980 to 2013 searched via Science Direct, IEEE etc. and out of which 61 research papers are critically assayed to depict the development of AHP, TOPSIS and their hybrid methods. Meaningful information and critical remarks are summarised in various tabular formats and charts to give readers easy information.

English

The rank reversal problem related to wash criterion in Analytic Hierarchy Process (AHP) was studied. The purpose of this paper is threefold. Firstly, the rank reversal problem did not come off when proportional adjustment of Liberatore and Nydick (2004) and Saaty and Vargas (2006) was applied. Secondly, the conditions that insure the occurrence of rank reversal by using the approach of Finan and Hurley (2002) and Lin, Chou, Chouhuang and Hsu (2008) were found. Thirdly, the study showed that the invariant phenomenon proposed by Jung, Wou, Li and Julian (2009) implied that the combined criterion is still a wash criterion. The findings will help researchers select the synthesizing method and keep the rank under some specific range when a wash criterion is deleted.   Key words: Analytic hierarchy process, rank reversal problem, wash criterion.

Comparison of the REMBRANDT system with the Wang and Elhag approach: A practical example of the rank reversal problem

Analytic hierarchy process (AHP) has been considerably criticized for possible rank reversal phenomenon caused by: i, when new alternatives are added or old ones deleted; and ii, when new criteria are added or old ones deleted with the caveat that the priorities of alternatives would be tied under these criteria and hence argued that the criteria should be irrelevant when ranking the alternatives. While in many cases this is a perfectly valid phenomenon, there are also many cases where rank should be preserved. This paper deals with rank reversal due to the inconsistency of the inputs. The preference intensities on REMBRANDT scale are more compatible than AHP scale. The REMBRANDT system is therefore proposed to avoid rank reversal phenomenon. We have provided a practical example to show that the rank reversal phenomenon did not occur with the REMBRANDT system, but did occur with the Wang and Elhag approach. Key words: Analytic hierarchy process (AHP), REMBRANDT system, rank reversal, multiple-attribute decision making (MADM), uncertainty.

Rank Reversal and Rank Preservation in TOPSIS

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.