Proposed Ranking Method Research Articles

Prioritization of key quality characteristics with the three-dimensional HoQ model-based interval-valued spherical fuzzy-ORESTE method

Ranking key quality characteristics (KQCs) which are widely utilized to evaluate the product quality level and selecting the most important one are necessary for improving the quality of mechanical and electrical product. Many classical ranking methods have been proposed to rank KQCs, e.g., the classical quality function deployment (QFD) method, the classical ORESTE method. However, some drawbacks in constructing ranking framework, rating KQCs/influence factors (IFs) and ranking KQCs are existed in these methods. To solve this problem, we develop a three-dimensional house of quality (HoQ) model-based interval-valued spherical fuzzy-ORESTE (IVSF-ORESTE) method to rank KQCs. First, the three-dimensional HoQ models which add meta-action units (MAUs) dimension are constructed to provide a ranking framework, and interval-valued spherical fuzzy sets (IVSFSs) are utilized to rate KQCs/IFs. Secondly, some new distance and weight distance measures based on the Dice and the Jaccard similarity are proposed to extend the traditional distance measure between IVSFSs. Furthermore, the IVSF-QFD method is used to calculate the importance degrees of IFs and the KQCs are ranked with the IVSF-ORESTE method. Finally, a real case vis-à-vis a NC rotary table is illustrated to explain the implementation process of the proposed method, and discussion results indicate that the proposed ranking method is rational and valid.

Ranking and characterization of precipitation extremes for the past 113 years for Indian western Himalayas

AbstractGlobally, mountain systems are unevenly exposed to risks of extreme precipitation. Within the Himalayan region, precipitation extremes are a rising concern, but their current understanding is limited. In this study, we use 113 years of precipitation data to rank and characterize precipitation extremes in the Indian Western Himalayas (IWH). Our statistical ranking method integrates precipitation spatial extent and its intensity across different durations for determining the severity of extreme events. The proposed ranking method accounts for multi‐day duration ranking method to capture persistent precipitation episodes. Results show that the method accurately detects and ranks the most extreme precipitation events that occurred in the IWH and indicate locations of these events. Our results highlight that critical long duration events in the region (e.g., 10 days) are missed at ranks at shorter duration (e.g., 2–3 days), thereby highlighting the importance to multi‐day precipitation extremes ranking. In addition, the proposed ranking method provides information about the event duration that will be associated with the highest impact on society, carrying high significance. Our findings are valuable for flood risk management and disaster risk reduction.

Strategic Management in Marketing: A Game Theoretic Approach

In marketing, a real-world dilemma emerging between two rivals, McDonald's and Burger King, is investigated. Both firms use three strategies: discounted pricing, status quo, and aggressive commercial. In such cases, ambiguity is a determining factor. To deal with confusion in payoffs, octagonal fuzzy numbers are used. To rank fuzzy numbers, the average of odd positions, average of even positions, and quartile deviations are used. To solve the reduced modelled two competitors zero sum fuzzy matrix games, the proposed ranking methods are used. Finally, the findings are compared to current approaches that are quite similar to the proposed approach.

Ranking interval type-2 fuzzy number based on a novel value-ambiguity ranking index and its application in risk analysis

Consequence of decision making problems under fuzzy domain mostly necessitate choosing the best alternative among the sets of alternatives. This requirement is generally fulfilled through the methodology called the ranking of fuzzy numbers. In this paper, an attempt under fuzzy domain to develop a coherent methodology of ordering fuzzy numbers is thrived. The ill-defined quantity ‘value’ and the amount of uncertainty in the ill-defined quantity ‘ambiguity’ of a fuzzy number constituted the important tools in ranking fuzzy numbers. As such, an effort has been made to employ these quantities in ranking interval type-2 fuzzy numbers constructing a novel value-ambiguity ranking index. A through comparative study with the existing method and the present method is executed to exhibit the essence of the proposed ranking method. The comparative study exhibit the superiority of the present method of ranking interval type-2 fuzzy numbers over the existing method. Further, the method is not just limited to interval type-2 fuzzy numbers, it can be broadly applied to arbitrary fuzzy numbers. Finally, a risk analysis problem has been discussed and the proposed novel value-ambiguity ranking index of ranking interval type-2 fuzzy numbers has been applied successfully.

A Possibility Theory-Based Approach to the Ranking of Generalized Fuzzy Numbers

The ranking of fuzzy numbers plays a remarkable role in some application systems such as approximate reasoning, decision analysis, optimization and forecasting under fuzzy environments. In this paper, we propose a novel possibility degree formula of ranking generalized fuzzy numbers based on the possibility theory. The combined effects of the possibilistic mean and the variance/standard deviation on the ranking of generalized fuzzy numbers are considered. The axiomatic properties of the proposed ranking method are further verified. It is found that the possibilistic mean exhibits the dominant role as compared to the possibilistic variance or standard deviation. Some comparisons with the existing approaches are reported by carrying out lots of numerical examples. The observations reveal that the shortcomings in an existing method can be overcome. Generalized fuzzy numbers can be distinguished using a possibility degree. The developed ordering procedures of fuzzy numbers are consistent with human intuition, where the inherent uncertainty of fuzzy quantities is revealed.

Aggregating the results of benevolent and aggressive models by the CRITIC method for ranking of decision-making units: A case study on seven biomass fuel briquettes generated from agricultural waste

The ranking of decision-making units (DMUs) is one of the main issues in data envelopment analysis (DEA). Hence, many different ranking models have been proposed. However, each of these ranking models may produce different ranking results for similar problems. Therefore, it is wise to try different ranking models and aggregate the results of each ranking model that provides more reliable results in solving the ranking problems. In this paper, a novel ranking method (Aggregating the results of aggressive and benevolent models) based on the CRITIC method is proposed. To prove the applicability of the proposed ranking method, it is examined in three numerical examples, six nursing homes, fourteen international passenger airlines and seven biomass materials for processing into fuel briquettes. First, benevolent and aggressive models were used to calculate the efficiency rating for each DMU. As a result, the decision matrix was generated. In the decision matrix, the results of benevolent and aggressive models were viewed as criteria and DMUs were viewed as alternatives. Then, the weights of each criterion were generated by the CRITIC method. Finally, each DMU was ranked. In a comparative analysis, the proposed method can lead to achieving a more reliable decision than the method which is based on a stand-alone method.

Fuzzy data envelopment analysis in the presence of undesirable outputs with ideal points

Data envelopment analysis (DEA) is a prominent technique for evaluating relative efficiency of a set of entities called decision making units (DMUs) with homogeneous structures. In order to implement a comprehensive assessment, undesirable factors should be included in the efficiency analysis. The present study endeavors to propose a novel approach for solving DEA model in the presence of undesirable outputs in which all input/output data are represented by triangular fuzzy numbers. To this end, two virtual fuzzy DMUs called fuzzy ideal DMU (FIDMU) and fuzzy anti-ideal DMU (FADMU) are introduced into proposed fuzzy DEA framework. Then, a lexicographic approach is used to find the best and the worst fuzzy efficiencies of FIDMU and FADMU, respectively. Moreover, the resulting fuzzy efficiencies are used to measure the best and worst fuzzy relative efficiencies of DMUs to construct a fuzzy relative closeness index. To address the overall assessment, a new approach is proposed for ranking fuzzy relative closeness indexes based on which the DMUs are ranked. The developed framework greatly reduces the complexity of computation compared with commonly used existing methods in the literature. To validate the proposed methodology and proposed ranking method, a numerical example is illustrated and compared the results with an existing approach.

Multi-criteria decision making based on ranking of neutrosophic trapezoidal fuzzy numbers

In this ebb and flow time, the neutrosophic set is a significant theme to exhibit the uncertain data, as it shows up the presence of three disjunctive segments, and it gives a wide scope of utilization in particular fields for the analysts. Neutrosophic sets are the extension of intuitionistic fuzzy sets to concentrate on the dubious, reluctant, and equivocal figures of genuine scientific problems. Ranking of neutrosophic fuzzy numbers is a challenging task. Only a very few models have been proposed in the literature for the ranking of neutrosophic fuzzy numbers. In this paper, we have first ranked the neutrosophic trapezoidal fuzzy numbers using the Euclidean measure to frame the centroid concept. We have studied a few properties of the ranking function. The effectiveness of the proposed ranking method is shown by comparative examples. Finally, the created positioning system is utilized to make a decision in the purchase problem.

A stepwise integrated risk-assessment framework in aquaculture: the case of sea lice tolerance to freshwater treatments on salmon farms

Aquaculture studies are often faced with data limitations when carrying out a quantitative risk assessment. Consolidating results from a literature search of potentially applicable methods, we propose a stepwise integrated methods approach that incorporates foundations from an antimicrobial resistance framework, the Office International Epizooties risk model, quantitative microbial risk assessment and infectious disease transmission models. We suggest that an initial ranking profile can be used to prioritize more in-depth qualitative and quantitative risk assessments, when data are available. The ranking method was done using a software that provides practical and interactive graphics for visualizing the impact of different factors and their respective weights on the likelihood of undesirable events (hazards) occurring. For this step, we illustrate how to include available data to obtain ranking results for decision makers using information from a recent sea lice freshwater tolerance literature review (Groner et al. 2019) that identified a gap in quantitative data. In our case example, for copepodid sea lice life stages, hypothetically changing how much experts believe that location and time are important factors revealed the most impact on the ranking for different degrees of freshwater tolerance evolution (no evolution, various partial options, known evolution). The factors ‘location’ and ‘time’, as well as ‘freshwater treatment’, have the greatest impact on the ranking for the preadult sea lice life-stages model. Results from our proposed ranking method can help to drive decisions around interpreting the various factors as they apply to mitigation planning and prioritizing those that should be included in further research. Additionally, we identify where quantitative data could be incorporated, as they become available, into a full risk assessment model with suggested models for a freshwater tolerance risk analysis framework.

A preference degree for ranking k-dimensional vectors of qualitative labels and its application in multi-attribute group decision-making

ABSTRACT In this paper, a ranking procedure is proposed for the order-of-magnitude qualitative reasoning problem. To this end, a preference degree was suggested for comparing a set of k-dimensional vectors of qualitative labels to provide a criterion to interpret the concept of ‘preference’ between two k-dimensional vectors of qualitative labels. Some useful properties of the proposed preference degree were also examined and discussed. It was shown that the proposed preference degree can satisfy the common and desired properties expected in the space of k-dimensional vectors of qualitative labels. Numerical examples were also employed to illustrate the proposed ranking method. Three application examples were used to show the applicability of the proposed ranking method in a single criterion group decision-making (SAGDM) and multiple criteria group decision-making (MAGDM). Moreover, the proposed preference degree was compared with a conventional method in terms of ranking the k-dimensional vector of qualitative labels. Unlike the proposed method, the numerical evaluations revealed that the previous techniques are not applicable to some situations in decision-making.

Integer multiplication ranking method for cloud services selection

Several popular cloud service providers such as Microsoft, Amazon, Google and others are competing to provide fast, reliable and efficient cloud services to the customers. Cloud computing has changed the cyber world by offering infrastructure and application services with rental charges. The decision making problem arises when selection has to be done amongst a wide range of available cloud service providers. Analytical Hierarchy Process (AHP) is a decision making method for selection amongst different options with multiple criteria. Different types of ranking methods are used in AHP which have great influence on the priorities assigned to these options. In this work, we propose an efficient and simplified ranking method named as Integer Multiplication (IM) to select the best Cloud service among the available services as per the user’s preference for different criteria and past experiences with the cloud service providers. The proposed method helps in making quick decisions as per customer’s preferences. We have demonstrated the applicability of this mechanism using a case study and also through simulated results. The results verify that the efficient and simplified proposed ranking method—“Integer Multiplication” gives accurate results for cloud service selection with reduced calculations and hence time saving in cloud service selection.

A New Ranking Approach for Solving Fully Fuzzy Transportation Problem in Intuitionistic Fuzzy Environment

Ranking of intuitionistic fuzzy numbers is an engrossing and very difficult task among the several researchers. The present studies introduce a new ranking method for intuitionistic fuzzy numbers (IFN) by using the concept of distance minimizer between two intuitionistic fuzzy numbers, with the help of their defined membership and non-membership functions. The proposed ranking method for intuitionistic fuzzy numbers satisfies the general axioms of ranking functions. Further, we have applied the developed ranking method to solve fully intuitionistic fuzzy transportation problems in which cost, supplies and requirements all are in term of intuitionistic fuzzy numbers. The developed method has been illustrated by an example, and the result obtained by the proposed method shows a superior performance compared with some existing methods available in the literature.

Multi-Faceted Analysis of Systematic Risk-Based Wind Energy Investment Decisions in E7 Economies Using Modified Hybrid Modeling with IT2 Fuzzy Sets

This study aimed to analyze the systematic risks of wind energy investments. Within this framework, E7 countries are included in the scope of the examination. A large literature review was carried out and 12 different systematic risk factors that could exist in wind energy investments were identified. The analysis process of the study consisted of two different stages. First, the specified risk criteria were weighted with the help of the interval type 2 (IT2) fuzzy decision-making trial and evaluation laboratory (DEMATEL) method. Second, E7 countries were ranked according to the risk management effectiveness in wind energy investments. In this process, the IT2 fuzzy Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) approach was taken into consideration. The findings show that volatility in exchange rates and interest rates were the most important risks in wind energy investments. In addition, it was determined that China and Indonesia were the most successful countries in managing risks in wind energy investments. In contrast, India, Russia, and Turkey were determined to be the least successful. Additionally, the IT2 fuzzy technique for order preference by similarity to ideal solution (TOPSIS) method was applied as a robustness check of the extended VIKOR method. It was concluded that the ranking results of the IT2 fuzzy TOPSIS method were similar to the results of the IT2 fuzzy VIKOR. It can be understood that the proposed ranking method was consistent with the comparative analysis results. From this point of view, it was observed that countries should take measures regarding their exchange rate and interest rate risks in order to increase the efficiency in wind energy investments. In this context, companies should first ensure that they do not have a foreign exchange short position in their balance sheets by conducting an effective financial analysis. In addition, it is important to use financial derivatives to minimize the exchange rate and interest rate risks. Using these results, it will be possible to manage this risk by taking the reverse position for the existing foreign currency and interest risk. In this way, it will be possible to increase the efficiency of wind energy investments, which will contribute to the social and economic development of each respective country.

An Attribute-Specific Ranking Method Based on Language Models for Keyword Search over Graphs

Many real-world networks such as Facebook, LinkedIn, and Wikipedia exhibit rich connectivity patterns along with worthwhile content nodes often labeled with meaningful attributes. Keyword search is an effective method to retrieve information from such useful networks. The aim of keyword search is to find a set of answers (subgraphs) covering all or part of the queried keywords. A challenge in keyword search systems is to rank answers according to their relevance to the query. This relevance lies in the textual content and structural compactness of the answers. In this paper, an attribute-specific ranking method is proposed based on language models to rank candidate answers according to their semantic information up to the attribute level. This method scores answers using a model enriched with attribute-specific preferences and integrating both the structure and content of answers. The proposed model is directly estimated on the sub-graphs (answers) and is defined such that it can preserve the local importance of keywords in nodes. Extensive experiments conducted on a standard evaluation framework with three real-world datasets illustrate the superior effectiveness of the proposed ranking method to that of the state-of-the-art methods.

Combining entity co-occurrence with specialized word embeddings to measure entity relation in Alzheimer\u2019s disease

BackgroundExtracting useful information from biomedical literature plays an important role in the development of modern medicine. In natural language processing, there have been rigorous attempts to find meaningful relationships between entities automatically by co-occurrence-based methods. It has been increasingly important to understand whether relationships exist, and if so how strong, between any two entities extracted from a large number of texts. One of the defining methods is to measure semantic similarity and relatedness between two entities.MethodsWe propose a hybrid ranking method that combines a co-occurrence approach considering both direct and indirect entity pair relationship with specialized word embeddings for measuring the relatedness of two entities.ResultsWe evaluate the proposed ranking method comparatively with other well-known methods such as co-occurrence, Word2Vec, COALS (Correlated Occurrence Analog to Lexical Semantics), and random indexing by calculating top-ranked entities related to Alzheimer’s disease. In addition, we analyze gene, pathway, and gene–phenotype relationships. Overall, the proposed method tends to find more hidden relationships than the other methods.ConclusionOur proposed method is able to select more useful related entities that not only highly co-occur but also have more indirect relations for the target entity. In pathway analysis, our proposed method shows superior performance at identifying (functional) cross clustering and higher-level pathways. Our proposed method, resulting from phenotype analysis, has an advantage in identifying the common genotype relating to phenotypes from biological literature.

An Approach to Rank Picture Fuzzy Numbers for Decision Making Problems

Comparison of picture fuzzy numbers (PFNs) are performed using score and accuracy values. But when both of the score and accuracy values are equal, those PFNs are said to be identical. This article presents a novel method to compare the PFNs even when the score and accuracy values of those PFNs are equal. The proposed ranking method is based on positive ideal solution, positive and negative goal differences, and score and accuracy degrees of the picture fuzzy numbers. A new score function is proposed to calculate the actual score value which depends on the positive and negative goal differences and the neutral degree. Finally, a real-life example has been used to illustrate the efficiency of the proposed method.

An extension to fuzzy ELECTRE

An extension to fuzzy ELECTRE is proposed in this paper, where ratings of alternatives versus qualitative criteria and the importance weights of criteria are assessed in linguistic values represented by fuzzy numbers. Formulas for the membership functions of fuzzy weighted ratings of alternatives under criteria can be obtained, and some properties are investigated. The ranking method of mean of maximum and minimum based on the Chen method is suggested to rank those fuzzy weighted ratings to produce concordance and discordance sets to complete the model. A numerical example will be used to demonstrate feasibility of the proposed fuzzy ELECTRE method, in which the outranking relationships among the alternatives can be clearly displayed. A comparison between the proposed ranking method and the Chen method will also be conducted to show advantage of the proposed method.

Deviation Degree: A Perspective on Score Functions in Hesitant Fuzzy Sets

Score functions play an important role in ranking hesitant fuzzy elements (HFEs) and hesitant fuzzy sets (HFSs). Currently, various kinds of HFE and HFS score functions have been investigated in the literature. However, the essential characteristic and generation mechanism of these score functions have not been systematically studied. To address these issues, this paper introduces an axiomatic definition of deviation degree measure and proposes a general form of dual HFE and HFS deviation score functions, from which a family of existing HFE and HFS score functions can be derived. Besides, we develop two ranking methods based on a pair of dual deviation score functions for distinguishing HFEs and HFSs that are indiscernible by a single score function. Moreover, the mathematical and behavioral properties of HFS deviation score functions are analyzed for applying them in practice. Finally, the proposed ranking method for HFSs is applied to the multi-criteria decision-making problems with hesitant fuzzy information.

A mixed ideal and anti-ideal DEA model: an application to evaluate cloud service providers

Abstract The rapid development of cloud computing and the sharp increase in the number of cloud service providers (CSPs) have resulted in many challenges in the suitability and selection of the best CSPs according to quality of service requirements. The main objective of this study is to propose three novel models based on the enhanced Russell model to increase the discrimination power in the evaluation and selection of CSPs. The proposed models are designed based on the distances to two special decision-making units (DMUs), namely the ideal DMU and the anti-ideal DMU. There are two advantages to the proposed ranking methods. First, they consider both pessimistic and optimistic scenarios of data envelopment analysis, so they are more equitable than methods that are based on only one of these scenarios. The second strength of this approach is its discrimination power, enabling it to provide a complete ranking for all CSPs. The proposed method can help customers to choose the most appropriate CSP while at the same time, it helps software developers to identify inefficient CSPs in order to improve their performance in the marketplace.

A ranking method based on interval type-2 fuzzy sets for multiple attribute group decision making

Ranking of fuzzy numbers has become an important research direction for decision-making problems due to its role to find the best objects under uncertainty. In this paper, we propose a new approach to perform multiple attribute group decision-making (MAGDM) problems using the ranking of interval type-2 fuzzy sets. Initially, a new ranking method for interval type-2 fuzzy numbers based on centroid and rank index has been proposed. Next, we present a comparative study to analyze the ranking values of the proposed method with the existing approaches, where we explore the necessity of the proposed ranking method. After that, a new MAGDM approach has been developed using the proposed ranking procedure to solve uncertain MAGDM problems. Finally, the applicability of the proposed approach has been illustrated using two numerical examples and a case study related to car-sharing problems. The proposed study exhibits a useful way to solve fuzzy MAGDM problems with much efficient manner since it applies interval type-2 fuzzy sets compared to type-1 fuzzy sets to signify the evaluating values and weights of the attributes.