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Failure mode and effects analysis (FMEA) is known to be a proactive reliability analysis model broadly utilized to recognize and evaluate potential failure modes in various industries. The normal risk priority number (RPN) method, however, has suffers from a lot of criticisms, such as requirement of precise risk estimation, lack of scientific basis in computing RPN, and neglecting the weights of risk factors. Therefore, this paper devises a new FMEA model to evaluate and prioritize the risk of failure modes by integrating probabilistic linguistic term sets and TODIM (an acronym in Portuguese for interactive multi-criteria decision making) method. The probabilistic linguistic term sets are utilized to handle the intrinsic ambiguity existed in the risk assessments of FMEA team members, whilst an extended TODIM method is employed for determining the priority ranking of the individuated failure modes. Further, based on the technique for order of preference by similarity to ideal solution (TOPSIS), an objective weighting method is presented to derive the relative weights of risk factors. Finally, two illustrative examples are implemented and comparisons with other existing methods are performed to demonstrate the rationality and superiority of our proposed FMEA model. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Nowadays, failure mode and effect analysis (FMEA) is a widely used reliability analysis technique for products, processes, and services because of its relative simplicity. The conventional risk priority number method, however, has been criticized as having many limitations, such as in the assessment of failure modes, the weighting of risk factors, and the prioritization of failure modes. In this paper, we aim to develop a new risk priority model for FMEA by integrating hesitant 2-tuple linguistic term sets and an extended QUALIFLEX approach. The concept of hesitant 2-tuple linguistic term sets is first presented to express various uncertainties in the assessment information of FMEA team members. Borrowing the idea of grey relational analysis, a multiple objective optimization model is constructed to determine the relative weights of risk factors with incomplete weight information. The extended QUALIFLEX approach with an inclusion comparison method is then suggested to determine the risk ranking of failure modes identified in FMEA. Finally, the practicality and effectiveness of the proposed FMEA are demonstrated through a case study, and the results show that the new risk priority approach is useful and flexible for handling complicated FMEA problems and can yield a reasonable and credible priority ranking of failure modes. [ABSTRACT FROM AUTHOR]

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 © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Health-care waste (HCW) management is a high priority public health and environmental concern particularly in developing countries. The decision to select an optimal technology for the disposal of HCW is a complicated multi-criteria decision analysis problem involving both qualitative and quantitative factors. Evaluating HCW treatment technologies may be based on imprecise information or uncertain data. Moreover, there can be significant dependence and feedbacks between the different dimensions and criteria. However, most existing decision models for HCW cannot capture these complex interrelationships. In response, this paper proposes a novel hybrid multi-criteria decision making (MCDM) model by integrating the 2-tuple DEMATEL technique and fuzzy MULTIMOORA method for selection of HCW treatment alternatives. It makes use of modified 2-tuple DEMATEL for obtaining the relative weights of criteria and fuzzy MULTIMOORA for assessing the alternatives according to each criterion. Specifically, an empirical case in Shanghai, one of the largest cities in China, is provided to illustrate the potential of the new model. Results show that the proposed framework for evaluating HCW treatment technologies is effective and provides meaningful implications for engineering designers to refer. [ABSTRACT FROM AUTHOR]

Highlights: [•] A novel hybrid MCDM model for material selection is proposed. [•] DEMATEL-based ANP is used to determine the degrees of influence among criteria. [•] Modified VIKOR is utilized for calculating the compromise ranking of alternatives. [•] The new model can be used to analyze the interrelationships in material selection. [ABSTRACT FROM AUTHOR]

Highlights: [•] Propose a VIKOR-based fuzzy MCDM technique for evaluating HCW disposal methods. [•] Linguistic variables are used to assess the ratings and weights for the criteria. [•] The OWA operator is utilized to aggregate individual opinions of decision makers. [•] A case study is given to illustrate the procedure of the proposed framework. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a hybrid decision making approach integrating induced aggregation operators into VIKOR is proposed for tackling multicriteria problems with conflicting and noncommensurable (different units) criteria. For doing so, we develop a new distance aggregation operator called the induced ordered weighted averaging standardized distance (IOWASD) operator. It is an aggregation operator that provides a wide range of standardized distance measures between the maximum and the minimum by using the induced OWA (IOWA) operator. The main advantage of the IOWA-based VIKOR (IOWA-VIKOR) is that it is able to reflect the complex attitudinal character of the decision maker by using order inducing variables and provide much more complete information for decision making. We also studied some of the IOWASD’s main properties and different particular cases and further generalized it by using the induced generalized OWA (IGOWA) operator. Finally, we apply the integrated IOWA-VIKOR method in a multi-criteria decision making problem regarding the selection of materials and the results are compared for different types of standardized distance aggregation operators. [Copyright &y& Elsevier]

As a proactive risk management instrument, failure mode and effect analysis (FMEA) has been broadly utilized to recognize, evaluate and eliminate failure modes of products, processes, systems and services. Nevertheless, the conventional FMEA method suffers from many important deficiencies when used in the real world. First, crisp numbers are adopted to describe the risk of failure modes; but, in many practical situations, it is difficult to obtain exact assessment values due to inherent vagueness in the human judgments. Second, the priority ranking of failure modes is determined based on the risk priority number (RPN), which is questionable and strongly sensitive to the variation of risk factor ratings. Therefore, this paper applies linguistic distribution assessments to represent FMEA team members’ risk evaluation information and employs an improved TODIM (an acronym in Portuguese of interactive and multicriteria decision making) method to determine the risk priority of failure modes. Furthermore, both subjective weights and objective weights of risk factors are taken into account while conducting the risk analysis process. Finally, an empirical case concerning the risk evaluation of a grinding wheel system is presented to demonstrate the practicality and effectiveness of the proposed new FMEA model. [ABSTRACT FROM AUTHOR]

Supplier selection is a complex multiple criteria decision-making (MCDM) problem, which considers a number of alternative suppliers as well as conflicting and noncommensurable criteria. Considering the fact that it is difficult to precisely determine criteria weights and the ratings of alternatives on each criterion in real-life situations, the VIKOR (VIsekriterijumska optimizacija i KOmpromisno Resenje) method has been modified for intuitionistic fuzzy data in this study for supplier evaluation and selection. Further, we take into account both subjective and objective weights of criteria in the decision-making process, as most supplier selection approaches consider only subjective criteria weights. Finally, two supplier selection examples are provided to illustrate the proposed intuitionistic fuzzy hybrid VIKOR (IFH-VIKOR) method, and its merits are highlighted by comparing it with other relevant approaches. [ABSTRACT FROM AUTHOR]

Quality function deployment (QFD) is a widely used quality system tool for translating customer requirements (CRs) into the engineering design requirements (DRs) of products or services. The conventional QFD analysis, however, has been criticized as having some limitations such as in the assessment of relationships between CRs and DRs, the determination of CR weights and the prioritization of DRs. This paper aims to develop a new hybrid group decision-making model based on hesitant 2-tuple linguistic term sets and an extended QUALIFLEX (qualitative flexible multiple criteria method) approach for handling QFD problems with incomplete weight information. First, hesitant linguistic term sets are combined with interval 2-tuple linguistic variables to express various uncertainties in the assessment information of QFD team members. Borrowing the idea of grey relational analysis (GRA), a multiple objective optimization model is constructed to determine the relative weights of CRs. Then, an extended QUALIFLEX approach with an inclusion comparison method is suggested to determine the ranking of the DRs identified in QFD. Finally, an analysis of a market segment selection problem is conducted to demonstrate and validate the proposed QFD approach. [ABSTRACT FROM AUTHOR]

How to select the suitable suppliers in the supply chain is critical for an organization’s success and has attracted much attention of both researchers and practitioners. Supplier selection can be regarded as a complex group multiple criteria decision making problem requiring consideration of a number of alternative suppliers and quantitative and qualitative criteria. Additionally, decision makers cannot easily express their judgments on the alternatives with exact numerical values in many practical situations, and there usually exists uncertain and incomplete assessments. In response, this paper proposes an extended VIKOR method for group multi-criteria supplier selection with interval 2-tuple linguistic information. The feasibility and practicability of the proposed interval 2-tuple linguistic VIKOR (ITL-VIKOR) method are demonstrated through three realistic supplier selection examples and comparisons with the existing approaches. Results show that the ITL-VIKOR method being proposed is more suitable and effective to handle the supplier selection problem under vague, uncertain and incomplete information environment. [ABSTRACT FROM AUTHOR]

Site selection for electric vehicle charging stations (EVCSs) is the process of determining the most suitable location among alternatives for the construction of charging facilities for electric vehicles. It can be regarded as a complex multicriteria decision-making (MCDM) problem requiring consideration of multiple conflicting criteria. In the real world, it is often hard or impossible for decision makers to estimate their preferences with exact numerical values. Therefore, Pythagorean fuzzy set theory has been frequently used to handle imprecise data and vague expressions in practical decision-making problems. In this paper, a Pythagorean fuzzy VIKOR (PF-VIKOR) approach is developed for solving the EVCS site selection problems, in which the evaluations of alternatives are given as linguistic terms characterized by Pythagorean fuzzy values (PFVs). Particularly, the generalized Pythagorean fuzzy ordered weighted standardized distance (GPFOWSD) operator is proposed to calculate the utility and regret measures for ranking alternative sites. Finally, a practical example in Shanghai, China, is included to demonstrate the proposed EVCS sitting model, and the advantages are highlighted by comparing the results with other relevant methods. [ABSTRACT FROM AUTHOR]

Nowadays robots have been commonly adopted in various manufacturing industries to improve product quality and productivity. The selection of the best robot to suit a specific production setting is a difficult decision making task for manufacturers because of the increase in complexity and number of robot systems. In this paper, we explore two key issues of robot evaluation and selection: the representation of decision makers' diversified assessments and the determination of the ranking of available robots. Specifically, a decision support model which utilizes cloud model and TODIM (an acronym in Portuguese of interactive and multiple criteria decision making) method is developed for the purpose of handling robot selection problems with hesitant linguistic information. Besides, we use an entropy-based combination weighting technique to estimate the weights of evaluation criteria. Finally, we illustrate the proposed cloud TODIM approach with a robot selection example for an automobile manufacturer, and further validate its effectiveness and benefits via a comparative analysis. The results show that the proposed robot selection model has some unique advantages, which is more realistic and flexible for robot selection under a complex and uncertain environment. [ABSTRACT FROM AUTHOR]

With the increasing pressure from global competition, manufacturers have realized that sustainable production is significant in supply chain management. Sustainable supplier selection and order allocation (SSS&OA) play a distinct and critical role for organizations to achieve sustainable development and build competitive advantage. In this paper, we aim to develop a novel SSS&OA model for selecting the most suitable sustainable suppliers and determining the optimal order sizes among them. First, double hierarchy hesitant linguistic term sets (DHHLTSs) are adopted to deal with uncertainty in evaluating the sustainable performance of alternative suppliers. Then, an extended decision field theory is proposed to choose efficient sustainable suppliers dynamically. Considering quantity discount, a multi-objective linear programming (MOLP) model is established to allocate reasonable order quantities among the selected suppliers. Finally, the applicability and effectiveness of the developed model are illustrated through its application in the electronic industry and through a comparative analysis with other methods. [ABSTRACT FROM AUTHOR]