Similarity Aggregation Method Research Articles

WITHDRAWN: Fuzzy Bayesian network based on an improved similarity aggregation method for risk assessment of storage tank accident

The Publisher regrets that this article is an accidental duplication of an article that has already been published in [PSEP, 149 (2021), https://doi.org/10.1016/j.psep.2021.03.017]. The full Elsevier Policy on Article Withdrawal can be found at https://www.elsevier.com/about/our-business/policies/article-withdrawal.

Prioritizing Risk-level Factors in Comprehensive Automobile Insurance Management: A Hybrid Multi-criteria Decision-making Model

An efficient risk-level prediction for newly proposed insurance policies plays a significant role in the survival of companies in the highly competitive insurance market. In Iran, risk assessment in comprehensive automobile insurance, which is a part of motor insurance, is only based on the vehicle attributes without proper consideration of personal and behavioural characteristics of driver(s). As a result, pricing is unfair in most of the cases and this can put insurance companies in an unfavourable financial position due to attracting high-risk drivers instead of low-risk ones. In this scenario, to identify and prioritize important factors affecting risk levels and to move towards a fair ratemaking, a two-phase process based on fuzzy Delphi method (FDM) and fuzzy analytic hierarchy process (FAHP) is proposed in this research. Additionally, similarity aggregation method (SAM) is applied to combine the individual fuzzy opinions of the surveyed experts into a group fuzzy consensus opinion. The results of this empirical study contribute to the insurance market of Iran by proposing appropriate weighting of the relevant risk factors to support stakeholders and policymakers for assessing risks more accurately, as well as designing more effective databases and insurance proposal forms.

Safety assessment of natural gas storage tank using similarity aggregation method based fuzzy fault tree analysis (SAM-FFTA) approach

Fault tree analysis (FTA) is an important method to analyze the failure causes of engineering systems and evaluate their safety and reliability. In practical application, the probabilities of bottom events in FTA are usually estimated according to the opinions of experts or engineers because it is difficult to obtain sufficient probability data of bottom events in fault tree. However, in many cases, there are many experts with different opinions or different forms of opinions. How to reasonably aggregate expert opinions is a challenge for the engineering application of fault tree method. In this study, a fuzzy fault tree analysis approach based on similarity aggregation method (SAM-FFTA) has been proposed. This method combines SAM with fuzzy set theory and can handled comprehensively diverse forms of opinions of different experts to obtain the probabilities of bottom events in fault tree. Finally, for verifying the applicability and flexibility of the proposed method, a natural gas spherical storage tank with a volume of 10,000 m3 was analyzed, and the importance of each bottom event was determined. The results show that flame, lightning spark, electrostatic spark, impact spark, mechanical breakdown and deformation/breakage have the most significant influence on the explosion of the natural gas spherical storage tank.

Risk Assessment of Oxygen Lance Burning Loss Using Bow-Tie Analysis Based on Fuzzy Theory

Oxygen lances (OLs) are important devices used in converter steel making. However, the occurrence of OL burning loss (OLBL) failure may lead to explosion accidents. To better prevent OLBL failure, it is necessary to perform a probabilistic assessment. Bow-tie analysis based on fuzzy theory was proposed to assess OLBL, which represents a hazardous event. In this paper, fuzzy theory based on triangular fuzzy numbers (TFNs) was applied to calculate the failure data. Fuzzy fault tree analysis (FFTA) in combination with the improved similarity aggregation method (ISAM) was employed to reduce the error generated due to the subjective judgement of experts. Furthermore, a comprehensive importance analysis method was developed to rank the importance of basic events (BEs), facilitating the adoption of the corresponding safety decisions. When performing fuzzy event tree analysis (FETA), the occurrence probability of outcome events (OEs) was determined by conducting a layer of protection analysis (LOPA). Finally, safety measures were proposed based on the assessment results to achieve safe production. The results indicated that the use of bow-tie analysis is appropriate to perform qualitative and quantitative assessment. Through bow-tie analysis based on fuzzy theory, the occurrence probability of OLBL was determined to be in the interval (5.34E − 02, 2.69E − 01). By adding independent protective layers (IPLs), the occurrence probability of OEs caused by OLBL can be effectively reduced.

A Consensus Measure of Expert Judgment in the Fuzzy TOPSIS Method

In the case of many complex, real-world decision problems solved with the participation of a group of experts, it is important to capture the uncertainty of opinions and preferences expressed. In such situations, one can use many modifications of the technique for order preference by similarity to the ideal solution (TOPSIS) method, for example, based on fuzzy numbers. In fuzzy TOPSIS, two aggregation methods of fuzzy expert opinions dominate, the first based on the average value technique and the second one extended by the minimum and maximum functions for determining the support of the aggregated fuzzy number. An important disadvantage of both techniques is the fact that the agreement degree of expert opinions is not taken into account. This article proposes the inclusion of the modified procedure for aggregating individual expert opinions, taking into account the degree of agreement of their opinions (called the similarity aggregation method—SAM) and the ranking of experts into the fuzzy TOPSIS method. The fuzzy TOPSIS method extended in this way was used to solve the decision problem of recruiting employees by a group of experts. As part of the solution, the modified SAM was compared with aggregation procedures based on the average value and min-max (minimum and maximum) support. The results of the conducted research indicate that SAM allows fuzzy numbers to be obtained, characterized by less imprecision and greater stability than the other two considered aggregation procedures.

Analysis of the Weights of Service Quality Indicators for Drone Filming and Photography by the Fuzzy Analytic Network Process

The service of drone filming and photography has been getting more and more popular. However, the service provider does not have enough information about service quality indicators and its weights. Analyzing the weights of service quality indicators by the Fuzzy Analytic Network Process (FANP) combined with Similarity Aggregation Method (SAM) is an important research topic. Therefore, in order to solve this real life problem, based on the SERVQUAL scale, this research analyzes the weights and the rankings from a comprehensive consensus by FANP combined with geometric mean and SAM, and then compares the differences between them. The results reveal that both the comprehensive consensus of experts’ opinions deemed that the most important dimension and indicator are reliability and “Employees are professional and get adequate support to do their jobs well.” The 2nd to 4th indicators from a comprehensive consensus of experts’ opinions are the same but the order is different. They are: “Drone service team’s employees give custom personal attention,” “Drone service team has up-to-date equipment,” and “Drone service team provides service legally, safely, and reliably.” The findings of the research reveal the weights of dimensions and indicators and help us to keep good service quality of filming and photography by drone.

Continuous Fuzzy Kano Model and Fuzzy AHP Model for Aesthetic Product Design: Case Study of an Electric Scooter

The fuzzy Kano model (FKM) adopts a linear scoring system that cannot accurately reflect the relationship between users’ needs and satisfaction and may underestimate the importance of users’ evaluation and needs. In this paper, we propose a preference-based evaluation-fuzzy-quantification method to determine the priority of the development of attractive factors of an electric scooter. In the evaluation analysis stage, the evaluation grid diagrams of all the interviewees are systemized. In the fuzzy computing stage, the continuous fuzzy Kano model (C-FKM) combined with the fuzzy analytic hierarchy process (FAHP) is developed to determine the priority of the development of attractive factors. By processing the ambiguity of users’ needs, the C-FKM can obtain a more accurate representation of users’ needs than the FKM. The opinions of 20 experts are integrated using the similarity aggregation method (SAM); then the FAHP is applied to calculate the weight of each evaluation criterion. Lastly, we use quantitative analysis to discover the important and specific characteristics that influence the attractiveness of an electric scooter. Research shows that Kansei images with attractive qualities are reliable and may be the first choice for satisfying the perceptual demands of consumers and can provide reference for the related research.

Flexible user-centric service selection algorithm for Internet of Things services

In Internet of Things (IoT), the similar functional services are evolving in different quality of services (QoS) due to the widespread deployment of spatially distributed things on dynamic networks through the web. Therefore, the user involvement in service selection systems becomes a vital role to enhance the system performance by taking into account of subjective factors. This paper proposes a flexible QoS-based service selection algorithm (FQSA) which mainly targets to the users to be able to give their subjective preferences in an easy and friendly manner. The FQSA algorithm selects the services based on subjective information provided by the service consumers and objective information supported by the service providers. It adopts artificial neural network backpropagation algorithm to find the objective factors and applies similarity aggregation method to evaluate the creditability of the user subjective factors which have already been evaluated by ontological reasoning with the help of proposed QoS ontology. The comparative study and experimental results show that our FQSA algorithm is superior to other service selection approaches.

Medical image retrieval based on unclean image bags

Traditional content-based image retrieval (CBIR) scheme with assumption of independent individual images in large-scale collections suffers from poor retrieval performance. In medical applications, images usually exist in the form of image bags and each bag includes multiple relevant images of the same perceptual meaning. In this paper, based on these natural image bags, we explore a new scheme to improve the performance of medical image retrieval. It is feasible and efficient to search the bag-based medical image collection by providing a query bag. However, there is a critical problem of noisy images which may present in image bags and severely affect the retrieval performance. A new three-stage solution is proposed to perform the retrieval and handle the noisy images. In stage 1, in order to alleviate the influence of noisy images, we associate each image in the image bags with a relevance degree. In stage 2, a novel similarity aggregation method is proposed to incorporate image relevance and feature importance into the similarity computation process. In stage 3, we obtain the final image relevance in an adaptive way which can consider both image bag similarity and individual image similarity. The experiments demonstrate that the proposed approach can improve the image retrieval performance significantly.

Fuzzy Similarity Consensus Model for Early Alignment of Construction Project Teams on the Extent of Their Roles and Responsibilities

A fuzzy similarity consensus (FSC) model is presented for alignment of construction project owner and contractor project teams to their roles and responsibilities, identifying and reducing fundamental problems of conflicts, duplication, and gaps in roles and responsibilities as early as the project initiation stage. The model achieves its objective by incorporating consensus and quality of construction project teams in aggregating their opinions to decide on the party responsible for every standard task of a construction project. The roles and responsibilities of the owner and contractors are described to different extents using seven linguistic terms defined by triangular membership functions and constructed using a three-step Delphi approach, which allows experts to develop common understanding of the meaning of the terms by determining their overlap on a fuzzy linguistic scale. A modified similarity aggregation method (SAM) aggregates experts’ opinions in a linguistic framework using a consensus weight factor for each expert that is based on the similarity of his or her opinion relative to the other experts to ensure that the experts’ final decision is a result of common agreement. A fuzzy expert system (FES) determines an importance weight factor, representing expert quality for each expert; opinions are aggregated using this factor and the consensus weight factor. The FSC model contributes to the construction industry by solving a fundamental problem for project owners who want to identify and reduce potential conflicts between their project teams on the extent of their roles and responsibilities prior to the construction stage. Also, the FSC model provides an improvement over previous consensus-based approaches, which rely on a subjective assessment of experts’ important weights in aggregating their opinions, and it modifies the SAM to adapt it to a linguistic environment.

A fuzzy aggregation approach to group decision-making based on centroid measurement

Abstract: In group decision-making situations, the decision-makers’ opinions are generally characterized bysubjectivity, imprecision and vagueness. Such opinions can be represented by fuzzy numbers such as positivetrapezoidalfuzzynumbers.Severalaggregationmethodshavebeenproposedpreviously;however,therearestilldeficits which limit their applications. This paper proposes an aggregation method based on centroidmeasurement which aims to overcome some of the shortcomings. In the paper, a number of axioms areproposed. A numerical example is given at the end to illustrate the application of the proposed aggregationmethod.Keywords: fuzzy opinions, fuzzy numbers, group decision-making, aggregation 1. IntroductionIn multiple-criteria group decision-making sit-uations, group members’ opinions or evalua-tions are based on a certain set of criteria. Theopinions may vary due to different perspectiveson both criteria and alternatives (Xu, 1988,1990; Feng & Xu, 1999a, 1999b; Qiu et al.,2003; Zhang & Lu, 2003; Zhang et al., 2003;Zhang & Jiang, 2006). Aggregating individualopinions to form an opinion with group con-sensus is an important consideration in groupdecision-making.The consensus opinion can be obtained bycalculating the weighted sum ofindividual opin-ions. One way to assign the weights is to esti-mate the relative importance of an individualbased on the criterion of authority. In theprocess of group decision-making, individualmembers’ opinions usually involve subjectivity,imprecision and vagueness (Hsu & Chen, 1996;Lee, 2002). Bardossy et al. (1993) suggest that asubjectiveestimatecanberepresentedbyafuzzynumber. The concept of fuzzy numbers was firstproposed by Dubois and Prade (1978). In theexisting literature, positive trapezoidal or trian-gular fuzzy numbers are employed to representfuzzy opinions (Olcer & Odabasi, 2005). Anumerical approximation technique has beenproposed to convert linguistic terms to theircorresponding fuzzy numbers (Chen et al.,1992). Kacprzyk et al. (1992) showed how lin-guisticquantifierscanbeusedingroupdecision-making. Some aggregation approaches dealingwith fuzzy numbers have been proposed forforming opinions with group consensus.Hsu and Chen (1996) proposed a similarityaggregation method in which the weight indexesconsist of two facets: the degree of relativeimportance of the individual and the degree ofsimilarity of opinion among group members.

A MODIFIED AGGREGATION OF FUZZY OPINIONS UNDER GROUP DECISION MAKING

A modified similarity aggregation method is presented. Besides the relative agreement degree and degree of importance, the relative weight agreement degree of experts' estimate is also considered. Therefore, a more comprehensive new consensus degree coefficient to aggregate the experts'fuzzyopinion is developed. A numerical example is given to illustrate our method.