Yizeng Chen

Evaluating the risk of failure modes with extended MULTIMOORA method under fuzzy environment

Failure mode and effects analysis (FMEA) is a prospective risk assessment tool which has been widely used within various industries, particularly the aerospace, automotive and healthcare industries. However, the conventional risk priority number (RPN) method has been criticized much for its deficiencies in risk factor weights, computation of RPN, evaluation of failure modes and so on. Therefore ranking of failure modes based on their related risk factors is necessary seeking to overcome the shortcomings and enhance the assessment capability of the traditional FMEA. In this paper, we treat the risk factors and their weights as fuzzy variables and evaluate them using fuzzy linguistic terms. As a result, a new risk priority model is proposed for evaluating the risk of failure modes based on fuzzy set theory and MULTIMOORA method. An empirical case of preventing infant abduction is provided to illustrate the potential applications and benefits of the proposed fuzzy FMEA. The main findings of this article are related with the proposed technique for failure modes assessment and ranking, and application of this technique for the prevention of infant abduction, which is a devastating problem for a healthcare facility. A new FMEA model is developed by using fuzzy set theory and MULTIMOORA method.Risk factors and their weights are evaluated using fuzzy linguistic variables.Extended MULTIMOORA is used to determine the risk priority of failure modes.The proposed fuzzy FMEA can be a useful tool for failure modes assessment and ranking.

Site selection in waste management by the VIKOR method using linguistic assessment

Abstract Site selection is an important issue in municipal solid waste (MSW) management. Selection of the appropriate solid waste site is an extensive evaluation process that requires consideration of multiple alternative solutions and evaluation criteria. In reality, it is easier for decision makers to express their judgments on the alternatives by using linguistic terms, and there usually exists uncertain and incomplete assessment information. Moreover, decision makers may have different risk attitudes in the siting process because of their different backgrounds and personalities. Therefore, an attitudinal-based interval 2-tuple linguistic VIKOR (ITL-VIKOR) method is proposed in this paper to select the best disposal site for MSW. The feasibility and practicability of the proposed method are further demonstrated through an example of refuse-derived fuel (RDF) combustion plant location. Results show that the new approach is more suitable and effective to handle the MSW site selection problems by considering the decision makers attitudinal character and incorporating the uncertain and incomplete assessment information.

A Novel Approach for FMEA: Combination of Interval 2‐Tuple Linguistic Variables and Gray Relational Analysis

Failure mode and effect analysis (FMEA) is a powerful tool for defining, identifying, and eliminating potential failures from the system, design, process, or service before they reach the customer. Since its appearance, FMEA has been extensively used in a wide range of industries. However, the conventional risk priority number (RPN) method has been criticized for having a number of drawbacks. In addition, FMEA is a group decision behavior and generally performed by a cross-functional team. Multiple experts tend to express their judgments on the failure modes by using multigranularity linguistic term sets, and there usually exists uncertain and incomplete assessment information. In this paper, we present a novel FMEA approach combining interval 2-tuple linguistic variables with gray relational analysis to capture FMEA team members’ diversity opinions and improve the effectiveness of the traditional FMEA. An empirical example of a C-arm X-ray machine is given to illustrate the potential applications and benefits of the proposed approach. Copyright

Site selection in municipal solid waste management with extended VIKOR method under fuzzy environment

Nowadays, selection of the suitable disposal site in municipal solid waste (MSW) management has become a challenge task for the municipal authorities, especially in fast-growing areas. Site selection can be viewed as a complicated multi-criteria decision-making (MCDM) problem requiring consideration of multiple alternative solutions and conflicting quantitative and qualitative criteria. In this paper, linguistic variables, which can be expressed as trapezoidal fuzzy numbers, are used to assess the ratings and weights for the selection criteria. The ordered weighted averaging operator is utilized to transform the fuzzy decision matrix into crisp values considering the decision maker’s attitudinal character. For selecting the best site, the extended VIsekriterijumska optimizacija i KOmpromisno Resenje (VIKOR) method is applied to determine the priority ranking of alternatives. As a result, a hierarchy MCDM model based on fuzzy set theory and VIKOR method is proposed to deal with the site selection problems in the MSW management system. An empirical study in Shanghai, China, is provided and comparison with the existing approach is conducted to illustrate the applicability and benefits of the proposed method.

Risk evaluation in failure mode and effects analysis using fuzzy digraph and matrix approach

Failure mode and effects analysis (FMEA) is a widely used engineering technique for identifying and eliminating known and potential failures from systems, designs, products, processes or services. However, the conventional risk priority number method has been extensively criticized in the literature for a lot of reasons such as ignoring relative importance of risk factors, questionable multiplication procedure, and imprecisely evaluation. In this article, a new FMEA model based on fuzzy digraph and matrix approach is developed to solve the problems and improve the effectiveness of the traditional FMEA. All the information about risk factors like occurrence (O), severity (S) and detection (D) and their relative weights are expressed in linguistic terms, represented by fuzzy numbers. By considering the risk factors and their relative importance, a risk factors fuzzy digraph is developed for the optimum representation of interrelations. Then, corresponding fuzzy risk matrixes are formed for all the identified failure modes in FMEA and risk priority indexes are computed for determining the risk priorities of the failure modes. Finally, a case study of steam valve system is included to illustrate the proposed fuzzy FMEA and the advantages are highlighted by comparing with the listed methods.

An integrated failure mode and effect analysis approach for accurate risk assessment under uncertainty

ABSTRACT Failure Mode and Effect Analysis (FMEA) is a reliability analysis technique that plays a prominent role in improving the reliability and safety of systems, products, and/or services. Although commonly used in quality improvement efforts, the conventional Risk Priority Number (RPN) method has been heavily criticized in the literature for its various limitations, such as in failure mode evaluations, risk factor weights, and RPN computation. In this article, we describe the application of an ELECTRE (ELimination Et Choix Traduisant la REalité)-based outranking approach for FMEA within the interval two-tuple linguistic environment. Considering different types of FMEA team members assessment information, we employ a hybrid averaging operator to construct the group assessment matrix and use a modified ELECTRE method to analyze the group interval two-tuple linguistic data. Furthermore, the new risk-ranking model deals with the subjective and objective weights of risk factors concurrently, considering the degree of importance that each concept has in the risk analysis. The practicality and applicability of the proposed methodology are demonstrated by applying it to a risk evaluation problem of proton beam radiotherapy, and a comparative study is conducted to validate the effectiveness of the new FMEA approach.

Determination of target values of engineering characteristics in QFD using a fuzzy chance-constrained modelling approach

Quality function deployment (QFD) is a method used for the manufacturing process of a product or service that is devoted to transforming customer requirements (CRs) into appropriate engineering characteristics (ECs) by specifying the importance of the ECs and then setting their target values. Confronting the inherent vagueness or impreciseness in the QFD process, we embed the fuzzy set theory into QFD. A fuzzy chance-constrained modelling approach with core philosophies of fuzzy expected value model and fuzzy chance-constrained programming is used in this paper. Thus, a novel fuzzy chance-constrained programming model whose objective is to minimize the fuzzy expected cost is proposed to determine the target values of the ECs with risk control to ensure satisfying CRs. Meanwhile, when considering the importance of the ECs, we adopt a more reasonable dispose which is to aggregate the relationships between the CRs and the ECs, and the correlations among the ECs. In order to solve the presented model, a hybrid intelligent algorithm is designed by integrating fuzzy simulation and genetic algorithm. Finally, an example of a motor car design is given to demonstrate the feasibility and effectiveness of the devised modelling approach and algorithm.

Using fuzzy non-linear regression to identify the degree of compensation among customer requirements in QFD

As an effective customer-driven approach, the quality function deployment (QFD) takes numbers of customer requirements (CRs) into account in the process of the initial product design and the competitive analysis. It is a traditional multi-attribute decision making problem, and the trade-off strategy among CRs which is interpreted as decision parameters, is crucial for resulting the overall customer satisfaction. Although the general trade-off strategies concern about the importance weights of CRs, which are specified with a variety of methods, they ignore the influence of the degree of compensation among them. In this paper, we embed the degree of compensation among CRs into QFD, which is expressed as a symmetric triangular fuzzy number, and develop a fuzzy non-linear regression model using the minimum fuzziness criterion to identify it. Furthermore, an illustrative example is provided to demonstrate the application and the performance of the modeling approach. It can be verified from the experimental results that the overall customer satisfaction as well as the prioritization of products are affected by the degree of compensation among CRs. Meanwhile, against to the products in example, the overall customer satisfaction obtained with the traditional weighted-sum method is confirmed to be underestimated.

Fuzzy linear regression models for QFD using optimized h values

Abstract In recent years, the fuzzy linear regression (FLR) approach is widely applied in the quality function deployment (QFD) to identify the vague and inexact functional relationships between the customer requirements and the engineering characteristics on account of its advantages of objectiveness and reality. However, the h value, which is a vital parameter in the proceeding of the FLR model, is usually set by the design team subjectively. In this paper, we propose a systematic approach using the FLR models attached with optimized h values to identify the functional relationships in QFD, where the coefficients are assumed as symmetric triangular fuzzy numbers. The h values in the FLR models are determined according to the criterion of maximizing the system credibilities of the FLR models. Furthermore, an illustrative example is provided to demonstrate the performance of the proposed approach. Results of the numerical example show that the fuzzy coefficients obtained through the FLR models with optimized h values are more effective than those obtained through the FLR models with arbitrary h values selected by the design team.

An interactive satisficing approach for multi-objective optimization with uncertain parameters

Uncertain variables are used to describe the phenomenon where uncertainty appears in a complex system. For modeling the multi-objective decision-making problems with uncertain parameters, a class of uncertain optimization is suggested for the decision systems in Liu and Chen (2013), http://orsc.edu.cn/online/131020 which is called the uncertain multi-objective programming. In order to solve the proposed uncertain multi-objective programming, an interactive uncertain satisficing approach involving the decision-maker’s flexible demands is proposed in this paper. It makes an improvement in contrast to the noninteractive methods. Finally, a numerical example about the capital budget problem is given to illustrate the effectiveness of the proposed model and the relevant solving approach.