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Green 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 chapter proposes an extended VIKOR method for GSES with interval 2-tuple linguistic information. The feasibility and practicability of the proposed interval 2-tuple linguistic VIKOR (ITL-VIKOR) method are demonstrated through two realistic examples and comparisons with the existing approaches. Results show that the ITL-VIKOR method being proposed is more suitable and effective to handle the green supplier selection problems under vague, uncertain and incomplete information environment.

With strengthening global consciousness of environmental protection, green supply chain management plays an increasingly important role in modern enterprise production operation management. A critical means to implement the green supply chain management is incorporating environmental requirements into supplier selection practices. In this chapter, we put forward a new integrated approach by using interval-valued intuitionistic uncertain linguistic sets (IVIULSs) and grey relational analysis (GRA)-technique for order preference by similarity to ideal solution (TOPSIS) method for the evaluation and selection of green suppliers. First, various qualitative assessments of alternatives provided by decision makers are described by the IVIULSs. Then, the GRA-TOPSIS method is extended and employed to prioritize the alternative suppliers. Finally, an illustrative example in the agri-food industry is presented to verify the proposed GSES method and demonstrate its practicality and effectiveness.

With respect to multi-criteria GSES problems with interval 2-tuple linguistic information, a new approach that uses distance measures is proposed in this chapter. Motivated by the ordered weighted distance (OWD) measures, we develop some interval 2-tuple linguistic distance operators such as the interval 2-tuple weighted distance (ITWD), the interval 2-tuple ordered weighted distance (ITOWD), and the interval 2-tuple hybrid weighted distance (ITHWD) operators. These aggregation operators are very useful for the treatment of input data in the form of interval 2-tuple linguistic variables. We study some desirable properties of the ITOWD operator and further generalize it by using the generalized and the quasi-arithmetic means. Finally, the new approach is utilized to complete a green supplier selection study for an actual hospital from the healthcare industry.

Considering the fact that it is difficult to precisely determine criteria weights and the ratings of suppliers on each criterion in real-life situations, the VIKOR (VIsekriterijumska optimizacija i KOmpromisno Resenje) method has been modified for intuitionistic fuzzy data in this chapter for green supplier selection. Further, we take into account both subjective and objective weights of evaluation criteria in the GSES process, as most green supplier selection approaches consider only subjective criteria weights. Finally, two green supplier selection examples are provided to illustrate the proposed intuitionistic fuzzy hybrid VIKOR (IFH-VIKOR) method, and its merits are highlighted by comparing with other relevant approaches.

Selecting the optimum green supplier is crucial for green supply chain management, which is a challenging multi-criteria decision making problem. Moreover, while evaluating the performance of alternative suppliers, decision makers tend to determine their assessments using linguistic descriptors due to experts’ vague knowledge and information deficiency. This chapter develops an integrated model based on cloud model and QUALIFLEX (qualitative flexible multiple criteria method) approach to assess the green performance of companies under economic and environmental criteria. For the introduced model, the linguistic terms, expressed in normal clouds, are utilized to assess alternatives against each evaluation criterion. A linear programming model is established to compute the weights of criteria with unknown or incompletely known weight information. An extended QUALIFLEX approach is proposed and used to select the most suitable green supplier. Finally, the proposed GSES method is demonstrated by an empirical example of an auto manufacturer to confirm its rationality and effectiveness.

With the increasing pressure from global competition, manufacturers have realized that green production is significant in supply chain management. Green supplier selection and order allocation (GSSOA) play a distinct and critical role for organizations to achieve green development and build competitive advantage. In this chapter, we develop a GSSOA model for selecting the most suitable green 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 green performance of alternative suppliers. Then, an extended decision field theory is proposed to choose efficient green suppliers dynamically. Considering quantity discount, a multi-objective linear programming model is established to allocate reasonable order quantities among the selected suppliers. The applicability and effectiveness of the developed model are illustrated through its application in the electronic industry and a comparative analysis with other methods.

For a manufacturing company, selecting the most suitable green supplier plays an important role in enhancing its green production performance. In this chapter, we develop a new GSES model through the combination of heterogeneous criteria information and an extended multi-attributive border approximation area comparison (MABAC) method. Considering the complexity of decision context, heterogeneous information, including real numbers, interval numbers, trapezoidal fuzzy numbers, and linguistic hesitant fuzzy sets, is utilized to evaluate alternative suppliers with respect to the evaluation criteria. A maximizing consensus approach is constructed to determine the weight of each decision-maker based on incomplete weighting information. Then, the classical MABAC method is modified for ranking candidate green suppliers under the heterogeneous information environment. Finally, the developed GSES model is applied in a case study from the automobile industry to illustrate its practicability and efficiency.

Selecting the best green supplier is essential for companies to promote green supply chain management, which is a complex multi-criteria decision making (MCDM) problem. Besides, decision makers tend to utilize linguistic terms for expressing their evaluations owing to their fuzzy knowledge. This chapter reports a new MCDM model for green supplier selection by integrating best-worst method (BWM) and alternative queuing method (AQM) within interval-valued intuitionistic uncertain linguistic setting. This approach allows to capture the uncertainty and vagueness of decision makers’ judgements with the aid of interval-valued intuitionistic uncertain linguistic sets. Furthermore, the BWM method can obtain the optimal weights of criteria via a nonlinear programing model. The AQM is reliable and intuitive to generate the ranking of candidate suppliers. Finally, a watch manufacturer is used as an example for illustrating the practicability and effectiveness of the proposed GSES model.

To improve GSES, a large number of approaches have been proposed in the past decades. This chapter systematically reviews the literature which aim to develop models and methods in helping enterprises to assess and select the right green suppliers. A total of 193 journal article extracted from the Scopus database over the period of 2009 to 2020 were chosen and reviewed. These publications were classified into ten categories based on the adopted GSES models, and analyzed concerning the evaluation criteria, criteria weighting methods, and performance evaluation methods. Moreover, a bibliometric analysis was conducted according to the frequency of supplier selection methods, citation number, publication year, journal, country and application area. The literature review results support practitioners, managers, and researchers in effectively recognizing and applying the GSES methods to enhance organizational competitiveness and provide an insight into its state-of-the-art.

This chapter aims to develop a modified VIKOR technique for GSES, that uses ordered weighted distance operators in the aggregation of picture fuzzy information. Concretely, we first propose the picture fuzzy ordered weighted standardized distance (PFOWSD) operator and the picture fuzzy Euclidean ordered weighted standardized distance (PFEOWSD) operator, and extended them by using the hybrid average operator. Then, we develop a green supplier selection approach by combining the picture fuzzy distance operators and the VIKOR method. The new approach can manipulate attitudinal character of the classical VIKOR method, so that a decision maker can take decisions according to his or her preference. Further, by using the PFOWSD operator, one can parametrize the VIKOR method from the maximum to the minimum result. Thus, the information obtained using the new green supplier selection approach is much more complete. Finally, a practical case example in the beef supply chain is given to explain the proposed picture fuzzy-ordered weighted distance (PFOWD)-VIKOR model.

To derive the best result, large group of decision makers are often involved in the green supplier selection nowadays. Besides, decision makers tend to express their evaluations utilizing uncertain linguistic terms due to the vagueness of human thinking. Hence, this chapter aims to propose a hybrid approach for green supplier selection within the large group setting. More concretely, interval-valued intuitionistic uncertain linguistic sets (IVIULSs) are applied for assessing the performance of green suppliers concerning each criterion. Ant colony algorithm is utilized to cluster decision makers into several subgroups. The linear programming technique for multidimensional analysis of preference (LINMAP) is adopted for the determination of criteria weights objectively. Finally, an extended MULTIMOORA approach is utilized to generate the ranking of alternative suppliers. The practicality and usefulness of the developed large group GSES framework is illustrated using an empirical example of a real estate company.

With the increased awareness of environmental issues worldwide, green production and development have become very important for various industries. To select appropriate supply partners and reasonable allocate order quantity, this chapter develops an integrated GSSOA model through the combination of linguistic Z-numbers, alternative queuing method (AQM) and multi-objective line programming (MOLP) model. The linguistic Z-numbers are adopted to express the decision makers’ performance evaluations of alternative suppliers. A step-wise weight assessment ratio analysis (SWARA) technique is used to calculate the weights of evaluation criteria. An extended AQM is proposed for the ranking of green suppliers. A MOLP model is established to determine the optimal order quantity for the qualified green suppliers according to their priority values. Finally, an empirical example is presented to illustrate the rationality and efficiency of the developed GSSOA model.

Nowadays, sustainable development through green initiatives has received considerable attention in view of global pressure, legislature, consumers’ awareness, environmental policies and societal issues. So, green initiatives have been given much importance by international organizations, national policies, and industries to reduce energy waste, conserve biodiversity and eco-system, and protect the environment. In response, manufacturing organizations are adopting green supply chain management (GSCM) in their supply chain practices to achieve positive outcomes on environmental, social and economic aspects. The GSCM is able to reduce the total negative environmental impact of a manufacturing plant involved in supply chain operations which can contribute to sustainable development and ensure performance enhancement. In this chapter, the background knowledge related to supply chain, supply chain management, and GSCM are introduced.

Network teaching has been widely developed under the influence of COVID-19 pandemic to guarantee the implementation of teaching plans and protect the learning rights of students. Selecting a particular website for network teaching can directly affects end users’ performance and promote network teaching quality. Normally, e-learning website selection can be considered as a complex multi-criteria decision making (MCDM) problem, and experts’ evaluations over the performance of e-learning websites are often imprecise and fuzzy due to the subjective nature of human thinking. In this article, we propose a new integrated MCDM approach on the basis of linguistic hesitant fuzzy sets (LHFSs) and the TODIM (an acronym in Portuguese of interactive and multi-criteria decision making) method to evaluate and select the best e-learning website for network teaching. This introduced method deals with the linguistic assessments of experts based on the LHFSs, determines the weights of evaluation criteria with the best–worst method (BWM), and acquires the ranking of e-learning websites utilizing an extended TODIM method. The applicability and superiority of the presented linguistic hesitant fuzzy TODIM (LHF-TODIM) approach are demonstrated through a realistic e-learning website selection example. Results show that the LHF-TODIM model being proposed is more practical and effective for solving the e-learning website selection problem under vague and uncertain linguistic environment.

This paper provides a novel risk priority approach for failure mode and effect analysis (FMEA), which can overcome some inherent drawbacks of the traditional risk priority number (RPN) method in imprecise risk evaluation, risk factor weighting and questionable RPN computation. Considering FMEA team members’ vagueness and uncertainty in their evaluations on failure modes, two-dimensional uncertain linguistic variables are advised to describe the risk evaluation result of a failure mode and the reliability of the evaluation result. The grey relation analysis–technique for order preference by similarity to ideal solution (GRA–TOPSIS) is applied for determining the risk ranking of the identified failure modes. In particular, a maximizing deviation method is employed for calculating the optimal weights of risk factors in an objective way. Via a practical healthcare risk analysis case, the new FMEA is proved to be appropriate and effective in coping with the risk evaluation problems with uncertain linguistic information. Furthermore, by comparing with existing methods, it is shown that the proposed integrated approach excels in the risk evaluation and prioritization of failure modes in FMEA.

Human reliability analysis (HRA) is a systematic technique to assess human contribution to system risk and has been widely used in diverse complex systems. Dependence assessment among human errors is an important activity in HRA, which depends heavily on domain experts’ knowledge and experience. Normally, it is common for experts to give their judgments using linguistic labels and different types of uncertainties may exist in the dependence assessments. Additionally, the existing dependence assessment methods are limited to small-scale expert groups, which reduce the accuracy of dependence analysis with the increasing complexity of high risky systems. In this article, we develop a large group dependence assessment (LGDA) model based on interval 2-tuple linguistic variables and cluster analysis method to manage the dependence in HRA. Further, we propose an extended Muirhead mean operator to determine the dependence levels between consecutive operator actions. Finally, an empirical healthcare dependence analysis is taken as an example to illustrate the effectiveness and practicality of our proposed LGDA approach.

Failure mode and effect analysis (FMEA) is a team-based technique for prospectively identifying and prioritizing failure modes of products, processes, and services. Given its simplicity and visibil...

As a powerful instrument to fulfil customer needs, quality function deployment (QFD) is capable of converting customer requirements (CRs) into appropriate engineering characteristics (ECs) in the p...

Decision making trial and evaluation laboratory (DEMATEL) is considered as an effective method for the identification of cause-effect chain components of a complex system. It deals with evaluating interdependent relationships among factors and finding the critical ones through a visual structural model. Over the recent decade, a large number of studies have been done on the application of DEMATEL and many different variants have been put forward in the literature. The objective of this study is to review systematically the methodologies and applications of the DEMATEL technique. We reviewed a total of 346 papers published from 2006 to 2016 in the international journals. According to the approaches used, these publications are grouped into five categories: classical DEMATEL, fuzzy DEMATEL, grey DEMATEL, analytical network process- (ANP-) DEMATEL, and other DEMATEL. All papers with respect to each category are summarized and analyzed, pointing out their implementing procedures, real applications, and crucial findings. This systematic and comprehensive review holds valuable insights for researchers and practitioners into using the DEMATEL in terms of indicating current research trends and potential directions for further research.

Electric vehicles (EVs) have received significant global attention due to the high energy efficiency and low pollutant emissions. In recent years, the Chinese government has allocated considerable funding and taken a number of legislative and regulatory steps for promoting the deployment of EVs. However, the increasing trend of EV adoption is not satisfying. In this paper, we investigate the factors that influence the diffusion of EVs in China from a multi-stakeholder perspective. Considering that decision makers' evaluation information is often imprecise and there can be significant dependence and feedback among influencing factors, a three-phase framework based on 2-tuple linguistic method and decision making trial and evaluation laboratory (DEMATEL) is proposed. First, a set of specific influencing factors is identified through a literature review and expert interviews. Second, direct relations of the influencing factors are evaluated using the 2-tuple linguistic method. Third, the critical factors influencing the diffusion of EVs in China are determined via the DEMATEL technique. According to the results of an empirical case study, ‘technological level’, ‘policies and regulations’, ‘consumer acceptance and expectation’, ‘price and models’, and ‘market structure and competition’ are critical factors influencing the diffusion of EVs in the Chinese market, which should be emphasized by the government and automakers for the benefits of EV development.

Failure mode and effect analysis (FMEA) is a useful quality management technique applied in various industries for enhancing the reliability and safety of systems, products, processes and services. The traditional FMEA, however, has been criticised for many internal defects in the evaluation of failure modes, the determination of risk factor weights and the ranking of failure modes, which weaken its accuracy and effectiveness when applied. In this paper, we propose a new FMEA methodology integrating cloud model theory with extended grey relational analysis (GRA) for resolving the disadvantages of the classical risk priority number approach. First, cloud model theory is adopted to express the uncertain linguistic assessments given by FMEA team members. Then, an extended GRA method is presented to obtain the risk priority of the failure modes in FMEA. Moreover, based on the idea of GRA, a multiple objective optimisation model is established to calculate the risk factor weights with incomplete weight informa...

Fuzzy Petri nets (FPNs) are an important modeling tool for knowledge representation and reasoning, which have been extensively used in a lot of fields. However, the conventional FPN models have been criticized as having many shortcomings in the literature. Many different models have been suggested to enhance the performance of FPNs, but deficiencies still exist in these models. First, various types of uncertain knowledge information provided by domain experts are very hard to be modeled by the existing FPN models. Second, the traditional FPNs determine the results of knowledge reasoning using the min, max, and product operators, which may not work well in many practical applications. In this paper, we propose a new type of FPN model based on intuitionistic fuzzy sets and ordered weighted averaging operators to deal with the problems and improve the effectiveness of the conventional FPNs. Moreover, a max-algebra-based reasoning algorithm is developed in order to implement the intuitionistic fuzzy reasoning formally and automatically. Finally, a case study concerning fault diagnosis of aircraft generator is presented to demonstrate the proposed intuitionistic FPN model. Numerical experiments show that the new FPN model is feasible and quite effective for knowledge representation and reasoning of intuitionistic fuzzy expert systems.

This paper proposes a linguistic reasoning Petri net (LRPN) model and develops an ordered weighted linguistic reasoning (OWLR) algorithm for knowledge representation and reasoning. Linguistic production rules in the knowledge base of a decision support system are modeled by LRPNs, where the truth degrees of the propositions in the linguistic production rules and the certainty factors of the rules are represented by linguistic 2-tuples. Moreover, both local and global weights of knowledge rules are taken into account in the linguistic reasoning process. The developed OWLR algorithm can allow the rule-based expert systems modeled with LRPNs to execute knowledge reasoning in a more flexible and intelligent manner. Finally, a case study regarding production rescheduling is presented to show the effectiveness and benefits of the proposed LRPN model and the linguistic reasoning approach.

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.

A novel FMEA approach is proposed using combination weighting and fuzzy VIKOR.Combination of fuzzy AHP and entropy method is applied for risk factor weighting.Fuzzy VIKOR method is used to determine the risk priorities of failure modes.An empirical example is offered to illustrate the effectiveness of the new method. Failure mode and effects analysis (FMEA) is one of the most popular reliability analysis tools for identifying, assessing and eliminating potential failure modes in a wide range of industries. In general, failure modes in FMEA are evaluated and ranked through the risk priority number (RPN), which is obtained by the multiplication of crisp values of the risk factors, such as the occurrence (O), severity (S), and detection (D) of each failure mode. However, the conventional RPN method has been considerably criticized for various reasons. To deal with the uncertainty and vagueness from humans' subjective perception and experience in risk evaluation process, this paper presents a novel approach for FMEA based on combination weighting and fuzzy VIKOR method. Integration of fuzzy analytic hierarchy process (AHP) and entropy method is applied for risk factor weighting in this proposed approach. The risk priorities of the identified failure modes are obtained through next steps based on fuzzy VIKOR method. To demonstrate its potential applications, the new fuzzy FMEA is used for analyzing the risk of general anesthesia process. Finally, a sensitivity analysis is carried out to verify the robustness of the risk ranking and a comparison analysis is conducted to show the advantages of the proposed FMEA approach.

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.

With the development of modern technology, industrial robots have been applied extensively in different industries to perform high-risk jobs and produce high-quality products. However, selecting an appropriate robot for a specific manufacturing environment is a difficult task for decision makers because of the increase in complexity, production demands, and the availability of different robot types. Normally, robot selection can be regarded as a complex multicriteria decision-making problem, and decision makers often use uncertain linguistic terms to express their assessments because of time pressure, lack of data, and their limited expertise. In this article, a modified MULTIMOORA (Multiobjective Optimization by Ratio Analysis plus the Full Multiplicative Form) method based on hesitant fuzzy linguistic term sets (named HFL-MULTIMOORA) is proposed for evaluating and selecting the optimal robot for a given industrial application. This method deals with the decision makers’ uncertain assessments with hesitant fuzzy linguistic variables, which can increase the flexibility of representing linguistic information. Finally, an empirical example is presented to demonstrate the proposed method, and the results indicate that the HFL-MULTIMOORA provides a useful and practical tool for solving robot selection problems within a hesitant linguistic information environment.

Performance analysis is an important way for hospitals to achieve higher efficiency and effectiveness in providing services to their customers. The performance of the healthcare system can be measured by many indicators, but it is difficult to improve them simultaneously due to the limited resources. A feasible way is to identify the central and influential indicators to improve healthcare performance in a stepwise manner. In this paper, we propose a hybrid multiple criteria decision making (MCDM) approach to identify key performance indicators (KPIs) for holistic hospital management. First, through integrating evidential reasoning approach and interval 2-tuple linguistic variables, various assessments of performance indicators provided by healthcare experts are modeled. Then, the decision making trial and evaluation laboratory (DEMATEL) technique is adopted to build an interactive network and visualize the causal relationships between the performance indicators. Finally, an empirical case study is provided to demonstrate the proposed approach for improving the efficiency of healthcare management. The results show that “accidents/adverse events”, “nosocomial infection”, ‘‘incidents/errors”, “number of operations/procedures” are significant influential indicators. Also, the indicators of “length of stay”, “bed occupancy” and “financial measures” play important roles in performance evaluation of the healthcare organization. The proposed decision making approach could be considered as a reference for healthcare administrators to enhance the performance of their healthcare institutions.