Jian-Xin You

Failure mode and effects analysis using D numbers and grey relational projection method

Abstract Failure mode and effects analysis (FMEA) is a widely used risk assessment tool for defining, identifying and eliminating potential failures or problems in products, process, designs and services. Two critical issues of FMEA are the representation and handling of various types of assessments and the determination of risk priorities of failure modes. Many different approaches have been suggested to enhance the performance of traditional FMEA; however, deficiencies exist in these approaches. In this paper, based on a more effective representation of uncertain information, called D numbers, and an improved grey relational analysis method, grey relational projection (GRP), a new risk priority model is proposed for the risk evaluation in FMEA. In the proposed model, the assessment results of risk factors given by FMEA team members are expressed and modeled by D numbers. The GRP method is used to determine the risk priority order of the failure modes that have been identified. Finally, an illustrative case is provided to demonstrate the effectiveness and practicality of the proposed model.

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

Failure mode and effects analysis using intuitionistic fuzzy hybrid TOPSIS approach

Failure mode and effects analysis (FMEA) is an effective reliability analysis technique used to identify and evaluate potential failures in systems, products, processes, and/or designs. In traditional FMEA, prioritization of failure modes is carried out by utilizing risk priority numbers (RPNs), which can be acquired by the multiplication of three risk factors: occurrence (O), severity (S) and detection (D). However, there are some inherent deficiencies in the conventional RPN method, which affect its effectiveness and thus limit its applications. In response, this paper introduces a new modified TOPSIS method, named intuitionistic fuzzy hybrid TOPSIS approach, to determine the risk priorities of failure modes identified in FMEA. Moreover, both the subjective and objective weights of risk factors are taken into consideration in the process of risk and failure analysis. A product example of the color super twisted nematic is presented at last to demonstrate the potential applications of the proposed approach, and the merits are highlighted by comparing with some existing methods.

Application of interval 2-tuple linguistic MULTIMOORA method for health-care waste treatment technology evaluation and selection.

The management of health-care waste (HCW) is a major challenge for municipalities, particularly in the cities of developing countries. Selection of the best treatment technology for HCW can be viewed as a complicated multi-criteria decision making (MCDM) problem which requires consideration of a number of alternatives and conflicting evaluation criteria. Additionally, decision makers often use different linguistic term sets to express their assessments because of their different backgrounds and preferences, some of which may be imprecise, uncertain and incomplete. In response, this paper proposes a modified MULTIMOORA method based on interval 2-tuple linguistic variables (named ITL-MULTIMOORA) for evaluating and selecting HCW treatment technologies. In particular, both subjective and objective importance coefficients of criteria are taken into consideration in the developed approach in order to conduct a more effective analysis. Finally, an empirical case study in Shanghai, the most crowded metropolis of China, is presented to demonstrate the proposed method, and results show that the proposed ITL-MULTIMOORA can solve the HCW treatment technology selection problem effectively under uncertain and incomplete information environment.

An interval-valued intuitionistic fuzzy MABAC approach for material selection with incomplete weight information

A hybrid group decision making approach is proposed for material selection.Uncertain and vague information is handled by interval-valued intuitionistic fuzzy sets.A maximizing optimization model is established for determining criteria weights.An extended group decision making method is used to rank alternative materials.The applicability and effectiveness are illustrated with two application examples. In engineering design, selecting the most suitable material for a particular product is a typical multiple criteria decision making (MCDM) problem, which generally involves several feasible alternatives and conflicting criteria. In this paper, we aim to propose a novel approach based on interval-valued intuitionistic fuzzy sets (IVIFSs) and multi-attributive border approximation area comparison (MABAC) for handling material selection problems with incomplete weight information. First, individual evaluations of experts concerning each alternative are aggregated to construct the group interval-valued intuitionistic fuzzy (IVIF) decision matrix. Consider the situation where the criteria weight information is partially known, a linear programming model is established for determining the criteria weights. Then, an extended MABAC method within the IVIF environment is developed to rank and select the best material. Finally, two application examples are provided to demonstrate the applicability and effectiveness of the proposed IVIF-MABAC approach. The results suggest that for the automotive instrument panel, polypropylene is the best, for the hip prosthesis, Co-Cr alloys-wrought alloy is the optimal option. Finally, based on the results, comparisons between the IVIF-MABAC and other relevant representative methods are presented. It is observed that the obtained rankings of the alternative materials are good agreement with those derived by the past researchers.

Failure mode and effect analysis using MULTIMOORA method with continuous weighted entropy under interval-valued intuitionistic fuzzy environment

Failure mode and effect analysis (FMEA) is a prospective risk assessment tool used to identify, assess and eliminate potential failure modes in various industries to improve security and reliability. However, the conventional risk priority number (RPN) method has been widely criticized for the deficiencies in risk factor weights, calculation of RPN, evaluation of failure modes, etc. In this paper, we present a novel approach for FMEA based on interval-valued intuitionistic fuzzy sets (IVIFSs) and MULTIMOORA method to handle the uncertainty and vagueness from FMEA team members’ subjective assessments and to get a more accurate ranking of failure modes identified in FMEA. In this proposed model, interval-valued intuitionistic fuzzy (IVIF) continuous weighted entropy is applied for risk factor weighting and the IVIF-MULTIMOORA method is used to determine the risk priority order of failure modes. Finally, an illustrative case is provided to demonstrate the effectiveness and practicality of the proposed FMEA and a comparison analysis with other relevant methods is conducted to show its merits.

Risk assessment in system FMEA combining fuzzy weighted average with fuzzy decision-making trial and evaluation laboratory

Failure mode and effects analysis (FMEA) is a risk assessment tool that mitigates potential failures in products, processes or systems before they occur. Although many industries use the traditional FMEA technique, it has been criticised for having several setbacks. First, the current FMEA determines the risk priorities of failure modes by using risk priority numbers (RPNs), which require the risk factors, occurrence (O), severity (S) and detection (D), to be evaluated in crisp values. Second, the conventional RPN method has not considered the indirect relations between components of a system and is insufficient for complex systems with many subsystems or components. Therefore, a new risk assessment methodology combining fuzzy weighted average with fuzzy decision-making trial and evaluation laboratory (fuzzy DEMATEL) is proposed in this article to rank the risk of failures in system FMEA. The new method can address some of the inherent limitations of the traditional FMEA. Also, an application to the thin film transistor liquid crystal display (TFT-LCD) product is presented to demonstrate the proposed FMEA. By comparing with the listed approaches, the results show that the proposed method is a suitable and effective method for prioritisation of failures in system FMEA.

Fuzzy Petri nets for knowledge representation and reasoning

Fuzzy Petri nets (FPNs) are a potential modeling technique for knowledge representation and reasoning of rule-based expert systems. To date, many studies have focused on the improvement of FPNs and various new algorithms and models have been proposed in the literature to enhance the modeling power and applicability of FPNs. However, no systematic and comprehensive review has been provided for FPNs as knowledge representation formalisms. Giving this evolving research area, this work presents an overview of the improved FPN theories and models from the perspectives of reasoning algorithms, knowledge representations and FPN models. In addition, we provide a survey of the applications of FPNs for solving practical problems in variety of fields. Finally, research trends in the current literature and potential directions for future investigations are pointed out, providing insights and robust roadmap for further studies in this field. We review the literature on FPNs published between 1988 and 2016.The reviewed papers are classified based on reasoning algorithms, knowledge representations and FPN models.A survey of the applications of FPNs for solving practical problems is provided.We offer directions for future research to improve the FPN performance.

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.