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Long-term development in computer manufacturing is crucial to satisfying the demands for the software and hardware needed for artificial intelligence (AI) computation. However, the computer manufacturing sector is highly energy-intensive and resource-consuming, making company sustainability an issue. The framework developed in this study incorporates 15 sustainability criteria focusing predominantly on the economy, society, environment, and risk perspectives. A novel hybrid approach that integrates the ISVTNN (interval single-valued trapezoidal neutrosophic number), DEMATEL (decision-making trial and evaluation laboratory), and PGRA (probability-based grey relational analysis) techniques is proposed. This approach is applied to evaluate the interdependent weights of the criteria for a comprehensive calculation of the performance of the evaluated companies based on probabilities. The inclusion of ISVTNNs allows not only for the broader consideration of information uncertainty but also for more effective integration of experts' judgments. To verify the robustness of the proposed model, three sensitivity analyses were conducted. The results show no significant changes in the rankings of the evaluated companies, demonstrating the stability and reliability of the model. The proposed framework and methodology can serve as the foundation for assessing the sustainable performance of companies in the electronics manufacturing industry. This study indirectly strengthens the backbone of the AI system, by improving sustainability assessment in the computer manufacturing sector (which is integral to the foundation for AI computation). In the AI-driven era, it can be expected that hardware demands will skyrocket, making sustainable manufacturing the linchpin for long-term operational success, resilience, and industry growth. • Developed a novel group decision-making approach for assessing corporate sustainability performance. • Integrated ISVTNN to measure uncertainty and effectively amalgamate expert opinions. • The proposed ISVTNN PGRA retains fuzziness, calculating through probabilistic means. • Demonstrated approach efficacy across six electronics manufacturing enterprises. • Sensitivity analysis underscores the approach's robustness. [ABSTRACT FROM AUTHOR]

Mass Rapid Transit (MRT) systems are essential in densely populated cities, providing efficient, reliable transportation that alleviates congestion, reduces pollution, and promotes sustainable development. By accommodating large passenger volumes, MRTs enhance urban mobility, economic vitality, and quality of life, making them a vital component of modern urban infrastructure. Identifying key economic, social, environmental, and institutional determinants is crucial for advancing sustainable transportation systems. Integrating technological and strategic perspectives, this paper employs the innovative Rough-Fermatean Fuzzy Decision-Making Trial and Evaluation Laboratory (RFF-DEMATEL) approach to delve into the critical determinants of transportation sustainability in the Taipei Mass Rapid Transit (TMRT). The study acknowledges the interconnections among these factors, leading to a more genuine and precise evaluation, distinct from conventional methods that presuppose factor independence. Research outcomes indicate that "operations and performance" stand out as the paramount factors, underscoring the importance of operational strategies and economic allocation. "Digital technology economy" and "sustainable supply chain management" are also emphasized, pinpointing the necessity to harmonize public welfare with financial sustainability while cultivating enduring partnerships. The proposed approach's thorough categorization offers specific managerial suggestions, assisting TMRT in realizing its sustainability aspirations and aligning with the Sustainable Development Goals (SDGs). This investigation accentuates the significance of recognizing factor interdependence and adeptly managing uncertainties, bolstering the research's credibility and applicative worth. • RFF-DEMATEL used to analyze critical determinants of TMRT's transportation sustainability. • "Operations and performance" identified as the most important factors for sustainability. • Evaluation framework integrates economic, social, and environmental aspects. • RFF-DEMATEL acknowledges factor interdependence for accurate importance assessment. • RFF-DEMATEL categorizes criteria using INRM, providing targeted management implications. [ABSTRACT FROM AUTHOR]

Natural disasters and man-made incidents have many negative impacts on major cities, including casualties, economic losses, disruption of social order, and environmental contamination. Cities need to be resilient in order to protect people's lives and property. Although research on urban resilience has been rapidly emerging in recent years, there are still some research gaps. The interplay of attributes for assessing urban resilience has not been explored, and the Multiple Attribute Decision Making- (MADM-) based framework for evaluating urban resilience is rarely studied. Therefore, this study proposes a novel model to evaluate urban resilience, the Z number-based Decision-Making Trial, and Evaluation Laboratory (Z-DEMATEL), to identify the mutual influential relationships and the weights of the attributes. In addition, the Z number-based Reference Ideal Method (Z-RIM) is used to determine the resilience capacity of cities and to suggest improvements for decision makers to develop appropriate strategies. In this study, we not only use trapezoidal fuzzy numbers to reflect the uncertainty of information but also measure the reliability/confidence of experts in the assessment. The integrated methodology is presented for the first time in this study, and we use the firmness data of six major metropolitan cities in Taiwan as an example of model demonstration. The results of the study show that population density, value of business activity, healthcare facilities, electricity supply, and number of business registrations are the most important attributes influencing the resilience of cities. Taipei City and Taichung City are the two major cities with better resilience in Taiwan based on the analysis of this study. [ABSTRACT FROM AUTHOR]

The rising regulatory and public pressure to align investments with sustainability causes an increasing need to analyze how investment institutions conduct and execute investment strategies related to their environmental, social, and corporate governance (ESG) funds. A thorough literature review shows that multiple criteria decision-making models are rarely used to discuss the relationships and weights of ESG fund indices. Furthermore, no ESG investment strategy evaluation framework has been created to deal with such funds. Therefore, a comprehensive framework is developed in this study to help select appropriate investment trust companies (ITCs). Z-numbers are incorporated into the proposed decision-making model in order to reflect information uncertainty and to assess the confidence of expert evaluations. First, a Z-based decision-making trial and evaluation laboratory approach is used to obtain the relationships and weights of indices to generate a causality diagram that allows decision-makers to quickly recognize the critical influencing factors in their evaluation systems. Then, a Z-based reference ideal method is applied to integrate the performance of the ITCs. The empirical application provides a demonstration of the usefulness of the proposed approach for selecting ITCs, which can aid to formulate appropriate strategies. • A comprehensive framework is proposed for selecting ITCs aligned with ESG factors for pension fund management. • Z-numbers are used to reflect information uncertainty and assess expert evaluations. • Four dimensions and 16 indices for evaluating ESG investment strategies are considered. • Key indices for selecting ITCs are identified. • The study can aid the development of ESG investment strategies in the public sector. [ABSTRACT FROM AUTHOR]

The effective identification of critical failure modes of individual equipment components or processes and the development of plans for improvement are crucial for the manufacturing industry. Recently, the failure modes and effects analysis (FMEA) approach based on multiple criteria decision making (MCDM) has been utilized effectively for the assessment of primary failure modes and risks. However, the ranking results of failure modes produced by different MCDM methods might be different. This study proposes an integrated risk assessment model where several techniques are combined to produce an FMEA model for the generation of comprehensive failure mode ranking. First, the anticipated costs and environmental protection indicators are included in the FMEA model to enhance the comprehensiveness of assessment. Then, an influential network relationship map of risk factors is obtained by using the decision-making trial and evaluation laboratory (DEMATEL) technique to assist in identifying the critical factors. Finally, the ranking of the failure modes is identified using the four integrated MCDM methods, based on the technique for order preference by similarity to ideal solution (TOPSIS) concept. In addition, data from a machine tool manufacturing company survey are applied to demonstrate the effectiveness and robustness of the proposed model. [ABSTRACT FROM AUTHOR]

Highlights l We integrate several state-of-the-art methods to analyze system/product failures. l The proposed model combines the rough number, BWM, and modified TOPSIS techniques. l The variety of expert opinions can be effectively integrated with this method. l The proposed model is demonstrated using data provided by a machine tool company. l Comparisons are performed between results from the proposed model and past methods. Abstract Increasing the reliability of machine tools and reducing possible risks during the manufacturing process is crucial for the future of industry. The failure mode and effects analysis (FMEA) method is reliant upon the experience of experts to determine the primary failure modes and detect the most critical factors for preventing risk. Clearly, an effective method capable of integrating the various different expert opinions is required. This study proposes a novel FMEA model based on multi-criteria group decision-making, which is developed by integrating a rough best–worst method, and modified rough technique for order preference by similarity to an ideal solution for ranking failure modes. The model can overcome some of the limitations of the conventional FMEA. It also includes the expected cost as a risk element to provide a more practical result. The effectiveness of the proposed model is demonstrated by conducting a case study involving a machine tool company. The results indicate that the proposed model can effectively assist managers in evaluating risk factors and identifying critical failure modes. [ABSTRACT FROM AUTHOR]

International airports are responding to the threat of climate change and various man-made hazards by proposing impact protection measures. Airport managers and risk controllers should develop a comprehensive risk assessment model to measure the mutual influence relationships of resilience factors. In this paper, the problem of treating resilience factors as independent ones in previous studies is overcome. In this study, we not only develop a framework for assessing resilience factors in international airports based on an aviation safety perspective, but also develop the Fermatean fuzzy decision-making trial and evaluation laboratory (FF-DEMATEL) to identify the mutual influence relationships of resilience factors. Fermatean fuzzy sets are incorporated in DEMATEL to reflect information incompleteness and uncertainty. The critical resilience factors of international airports were identified through real-case analysis. In terms of importance, the results show that rescue capability is a core capability that is important for airport resilience. In addition, "security management system (SeMS) integrity", "education and training of ground staff on airport safety awareness", "first aid mechanism for the injured", and "adequate maintenance equipment for rapid restoration tasks" are identified as key factors that are given more weights. On the other hand, in terms of influence strength, the detection capability has the highest total influence and significantly influenced the other resilience capabilities. Finally, the influential network relation map (INRM) is utilized to assist decisionmakers in swiftly comprehending the impact of factors and formulating viable strategies to enhance airport resilience. This enables airport managers and risk controllers to make informed decisions and allocate resources efficiently. [ABSTRACT FROM AUTHOR]

Green purchasing is a critical factor in sustainable enterprise development, and it often affects a company's business performance and environmental protection practices. An enterprise must have an appropriate assessment model to address the complexities of green purchasing. Most green purchasing studies have focused on the use of green criteria in the selection of suppliers to develop sustainable operations. By contrast, there have been few articles on green supply chain management discussing both green supplier evaluation and order allocation. This study proposes a novel model that integrates the best–worst method (BWM), modified fuzzy technique for order preference by similarity to ideal solution (TOPSIS), and fuzzy multi-objective linear programming (FMOLP) to solve problems in green supplier selection and order allocation. We demonstrated the proposed method using actual data provided by an electronics company. The results indicate that this model can effectively evaluate the performance of green suppliers and can also optimize order allocation for qualified suppliers. [ABSTRACT FROM AUTHOR]

[Display omitted] • This study develops a comprehensive sustainable supplier evaluation framework for Industry 5.0 era. • This study proposes a novel data-driven decision-making concept. • This study integrates VC-DRSA, CRITIC, modified CTOPSIS techniques. • A multinational medical equipment manufacturer is used as a demonstration case to illustrate the utility. Effective supplier management is critical for an enterprise's success, as supplier procurement accounts for up to approximately 70% to 80% of total manufacturing costs. Correct supplier selection can ensure the competitiveness of the enterprise and contribute to supply chain integration and innovation. In recent years, supplier evaluation frameworks based on Industry 4.0 concepts have contributed to the development of the industry. However, the novel concepts of Industry 5.0 require examination from a people-oriented and sustainable perspective. Unfortunately, at the present time, supplier evaluation frameworks based on Industry 5.0 are lacking. Therefore, the primary task of this study is to develop a novel and comprehensive supplier evaluation framework for the Industry 5.0 era. This study proposes a data-driven decision support system to execute the supplier evaluation process. First, variable precision- dominance-based rough set approach (VC-DRSA) is applied to extract the core criteria, to remove the noise factors and to generate decision rules for the decision-makers' reference. Second, the criterion importance through intercriteria correlation (CRITIC) approach is adopted to obtain the dependency weights of the core criteria and their ranking. Finally, a modified classifiable technique for order preference by similarity to ideal solution (CTOPSIS) is used to integrate the final performance values of suppliers when new alternative suppliers are added. The research concept is in line with the conception of data-driven decision support in business intelligence and does not rely on the subjective judgments and opinions of experts. Data provided by a multinational medical equipment manufacturer are used as an example to demonstrate the proposed model. VC-DRSA retains nine core criteria from the original twenty criteria, which greatly reduces the labor and cost of supplier audits. In addition, the CRITIC results show that digital transformation, real-time information sharing, and organizational culture transformation are the three main factors affecting the development of enterprises towards Industry 5.0. The results show that CTOPSIS can be used to quickly assess the ratings of new alternative suppliers are listed. [ABSTRACT FROM AUTHOR]

It is necessary to emphasize both the process and results of performance management to find the balance between quality and quantity needed to ensure the sustainable development of universities to make the best use of limited educational resources and meet the diverse needs of students. This study applies failure mode and effects analysis (FMEA) to analyze obstacles to university sustainability by constructing complete risk assessment modes and reference indicators. The neutrosophic set theory was incorporated into the FMEA to account for information uncertainty and asymmetry. A specialist team then evaluated the importance of the risk factors using neutrosophic indifference threshold-based attribute ratio analysis to determine objective weights for the risk factors. Furthermore, the neutrosophic technique for order preference by similarity to the ideal solution based on aspiration level (N-TOPSIS-AL) is employed to aggregate the total risk scores of the failure modes. Using neutrosophic sets to measure truth, falsity, and indeterminacy in the assessment significantly improve the adaptability of the fuzzy theory to real-world problems. The study results indicate that when assessing university affairs management and analyzing possible risks, priority must be given to the occurrence of risks and that the lack of educational facilities is the riskiest item in the specialist assessment. The proposed assessment model can be applied as a basis for university sustainability assessments to accelerate the development of other forward-looking approaches. [ABSTRACT FROM AUTHOR]

This study aims to explore the comparison of the regional competitiveness of the nations targeted by Taiwan's New Southbound Policy and the key indicators that influence regional competitiveness. Through comparative analysis between each other, we will further select countries and regions that are most suitable for investment and exchange. Regional competitiveness describes the ability of various economies to compete for resources or markets in a region. Exploring regional competitiveness allows the best nations for investment to be identified so that unnecessary costs and losses may be avoided. This paper proposes a multiple attribute decision making method, grey relational analysis (GRA), for solving this kind of problem. This study investigates the regional competitiveness of various nations using competitiveness rankings obtained through the ranking of grey relational ordinals. The study seeks to identify critical factors underlying regional competitiveness. The study variables are grouped into four major factors: economy, human resources, technology, and trade capability. The results reveal that India, Singapore, and Australia are nations with regional competitive advantages. Labor force and technology are indispensable factors for enhancing regional competitiveness. ASEAN countries have greater regional competitiveness than South Asia or Australia and New Zealand. [ABSTRACT FROM AUTHOR]

This is a forward-looking approach that uses a multiple-criteria decision analysis (MCDA) model as an assessment tool for risk identification. This study proposes an indifference threshold-based attribute ratio analysis and technique for order preference by similarity to an ideal solution (ITARA-TOPSIS)-based assessment model to identify critical failure modes in products and systems. The improved indifference threshold-based attribute ratio analysis (ITARA) method can generate more reliable weights for risk factors. In addition, the modified technique for order preference by similarity to an ideal solution (TOPSIS) is used to obtain the risk levels of the failure modes. The gray correlation coefficient is applied to replace the conventional Euclidean distance, and a new index is used to determine the priority of failure modes. The determination of risk factors is based on the failure mode and effect analysis (FMEA) theory, including severity, occurrence, and detection. An important indicator, the expected cost, is also included in the framework. The case of a steam turbine for a nuclear power plant is used to demonstrate the approach, and the analysis results show that the proposed model is practical and effective. Moreover, the advantages of our integrated model are illustrated through model comparisons and sensitivity analysis. This paper can help decision-makers, risk engineers, and related researchers to better understand how a systematic risk assessment can be conducted. [ABSTRACT FROM AUTHOR]

In today's competitive business environment, corporations attempt to achieve sustainability through innovation. Innovation is considered by researchers and scholars to be a key driver for achieving sustainability. One of the key parts in any sustainable supply chains is sustainable supplier evaluation and selection. However, few sustainable supply chain management (SSCM) studies have focused on sustainable supplier evaluation and selection, particularly in the context of sustainable innovation management. Thus, supplier evaluation and selection studies that consider overall sustainability (social, environmental, and economic) innovation criteria are nearly non-existent. To deal with this issue, this paper proposes a decision framework to assess sustainable innovative suppliers. A combination of best worst method (BWM) and modified Preference Ranking Organization Method for Enrichment of Evaluations (PROMETHEE) is employed as an integrated model in the analysis. The BWM is initially applied to identify the sustainable innovation criteria weights, and then the modified PROMETHEE is used to analyze the suppliers' performance. A manufacturing case example is employed to verify the utility and applicability of the proposed methodology. This paper can assist industrial managers, researchers, and decision-makers in understanding and focusing on sustainable innovation, particularly when selecting suppliers, and enhancing their supply chains' sustainability to make progress toward sustainable development. • Develops a framework for evaluating suppliers based on sustainability innovation. • The BWM and modified PROMETHEE is employed in the assessment and selection process. • A manufacturing case example is used to verify the usefulness and applicability. • Findings assist industry experts to better understand sustainability innovation. [ABSTRACT FROM AUTHOR]

Failure mode and effects analysis (FMEA) is a risk assessment method that effectively diagnoses a product's potential failure modes. It is based on expert experience and investigation to determine the potential failure modes of the system or product to develop improvement strategies to reduce the risk of failures. However, the traditional FMEA has many shortcomings that were proposed by many studies. This study proposes a hybrid FMEA and multi-attribute decision-making (MADM) model to establish an evaluation framework, combining the rough best worst method (R-BWM) and rough technique for order preference by similarity to an ideal solution technique (R-TOPSIS) to determine the improvement order of failure modes. In addition, this study adds the concept of aspiration level to R-TOPSIS technology (called R-TOPSIS-AL), which not only optimizes the reliability of the TOPSIS calculation program, but also obtains more potential information. This study then demonstrates the effectiveness and robustness of the proposed model through a multinational audio equipment manufacturing company. The results show that the proposed model can overcome many shortcomings of traditional FMEA, and effectively assist decision-makers and research and development (R&D) departments in improving the reliability of products. [ABSTRACT FROM AUTHOR]

In order to remain competitive in the market, advance society and culture, and preserve the environment while ensuring continued benefits, corporations are urged to embrace sustainable development (SD). With the growing recognition of corporate sustainability, certain countries have implemented measures aimed at fostering its growth, including counseling and subsidies. However, it is crucial to evaluate the efficacy of these subsidies in transforming the performance of recipient corporations. Hence, the assessment of corporate sustainability performance emerges as a critical undertaking. This paper proposes a corporate sustainability performance (CSP) assessment framework, which incorporates environmental, social, economic, and institutional perspectives. The framework contributes to achieving several sustainable development goals (SDGs). In terms of methodology, a novel multiple attribute decision-making (MADM) model is developed. First of all, fuzzy indifference threshold-based attribute ratio analysis (FITARA) is used to assign weights to the sustainability indicators. Secondly, the fuzzy reference ideal method (FRIM) is applied to aggregate the sustainability performance of corporations and obtain their rankings. The ITARA-RIM approach incorporates trapezoidal fuzzy sets to reflect the uncertainty of information. Furthermore, our defuzzification procedure is different from the general center of gravity defuzzification method, because it uses centroids and the maximizing and minimizing set (CMM) method to obtain more reliable crisp data. An example from the foundry industry in Taiwan is used to demonstrate the suitability and effectiveness of the approach. This study identified 27 assessment indicators to assess the sustainability performance of 14 corporations. The analysis revealed that indicators such as information sharing, green product design and recyclability, pollutant emissions reduction, enterprise size, and project control management are significant factors in determining the CSP. This study can assist decision-makers/managers/investigators in both government and industry to better comprehend how systematic performance assessment can be conducted. Academically, the proposed approach can be applied as a basis for the application of CSP to promote the evolvement of other more forward-looking methodologies. [ABSTRACT FROM AUTHOR]

In today's digital age, the financial industry, a cornerstone of the global economy, is undergoing monumental shifts. While business performance hinges on proficient leadership, the seismic wave of digital transformation poses new challenges and magnifies the need for specific leadership competencies. The urgency is clear: adapt or become obsolete. However, there is a lack of clarity regarding which competencies are essential during such transformations. To address this gap, the purpose of this study is to identify the leadership competencies that leaders in the financial industry should possess during the digital transformation process and to determine the influential relationships among these competencies and which ones are highly influential. This paper extensively investigates the criteria for evaluating leadership competencies and integrates expert judgments to construct an evaluation framework for leadership competencies during digital transformation. The Z-based decision-making trial and evaluation laboratory (Z-DEMATEL) technique was applied to identify the influential relationships among the criteria and generate influence weights for each criterion. In addition, based on the results of Z-DEMATEL, an Influence Classification Map (ICM) was plotted that categorized the criteria into four groups: core, driving, independent, and impact. Management implications and improvement suggestions are provided accordingly. Z-DEMATEL enhances the general form of fuzzy DEMATEL. By integrating the Z-fuzzy theory, Z-DEMATEL not only accounts for the uncertainty of the evaluation environment but also measures the confidence level of experts. Taking Taiwan's financial industry as a case study, this study revealed that "vision and imagination," "critical analysis," and "strategic perspective" were the top three criteria with higher weights, while "critical analysis" and "interpersonal sensitivity" were the core criteria. Business managers can use the results to design leadership training programs that meet the requirements of digital transformation and ultimately improve the performance of organizations during the transformation. Moreover, the concepts and methods presented in this study can be applied to other industries that are embarking on their digital transformation journeys. [ABSTRACT FROM AUTHOR]

Abstract Cycling as a mode of transportation brings certain benefits to the user, such as improving health and saving money. It can provide a significant improvement in the quality of city life with minimal ecological impact. Thus, public bike-sharing systems have been adopted as a modal choice in many cities world-wide. However, there are some problems that must be addressed when designing a bike-sharing system, one of which is how to improve service quality and attract more users. This study proposes a hybrid model, combining the Decision-Making Trial and Evaluation Laboratory (DEMATAL) method with the Analytical Network Process (ANP) approach (called DANP), which addresses the dependent relationships between the various criteria for the quality of service for bike-sharing systems to better reflect the real-world situation. After building a complex evaluation system, a modified VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje) method is applied to explore the weighted-gap to the aspiration levels. Furthermore, given the vagaries of human judgments and ratings, preferences cannot be assigned an exact numerical value, which can make it difficult to evaluate the performance or make comparisons. One way to improve upon this uncertainty is to apply Grey Theory. The proposed method is then applied using data for Taipei's Youbike and Obike systems. The analytical results and the resultant managerial applications for improving the service quality of both bike-sharing systems are also discussed with particular attention to the current policies of Taipei City. Highlights • We propose a model to evaluate and improve the service quality of public bike-sharing systems. • The model addresses the dependent relationships among various criteria and information uncertainty. • Our results indicated the infrastructure is the causal factor and safety has the largest gap for improvement. • Results are discussed with regards to the current policies of Taipei City. [ABSTRACT FROM AUTHOR]

Medical tourism (MT) is the activity of traveling domestically or abroad to receive medical services. The scope of medical treatments covers dentistry, surgery, antiaging procedures, preventive medicine, and even some health-related treatments (meditation, physiotherapy, psychotherapy, addiction treatment, psychiatry, etc.). Due to the global boom in MT, governments are actively promoting MT packages to capture this huge business opportunity. However, what are the key factors that make MT development successful or unsuccessful? How can the performance of current MT operators be evaluated? And, how can the performance of underperforming operators be improved? This paper addresses these questions by proposing a MT assessment framework that summarizes the potential key factors of MT. In addition, this study proposes a model that integrates the Bayesian Best-Worst Method (Bayesian BWM) and grey Preference Ranking Organization Method for Enrichment Evaluations based on Aspiration Level (grey PROMETHEE-AL) to assess the performance of assessed MT operators. The Bayesian BWM not only aggregates the judgments of multiple experts but also generates a set of objective group criteria weights. Besides, the modified PROMETHEE incorporates the grey theory and aspiration level concept to increase the usefulness of the original PROMETHEE. The results of the analysis show that the two most critical criteria for MT are "the operators have cloud computing systems to analyze the travelers' sensor data in real-time and accurately to provide customized medical services" and "multilingualism and communication skills of medical travel-related personnel." Poor performers in the travel industry can be improved by prioritizing the criteria in order of importance. The management implications of this study can be used as a basis for performance evaluation by operators and government health care organizations. [ABSTRACT FROM AUTHOR]

This paper discusses a previously published paper titled "sustainable recycling partner selection using fuzzy DEMATEL-AEW-FVIKOR: A case study in small-and-medium enterprises (SMEs)", by Zhou et al. (2018). Authors of the aforementioned paper have discussed sustainable recycling partners for SMEs and contributed to the multiple-criteria decision-making (MCDM) modelling. However, in their study, the crucial weights generated using the decision-making trial and evaluation laboratory (DEMATEL) technique are questionable, may not be accurate for obtaining the subjective weights of criteria. In addition, using VIKOR together with an aspiration level concept is recommended for determining management implications. The present discussion includes several opinions and suggestions for improving the article and can be considered an attempt to complete the original paper. [ABSTRACT FROM AUTHOR]

It is a common practice for enterprises to use outsourcing strategies to reduce operating costs and improve product competitiveness. Outsourcing providers or operators need to be aware of environmental protection and make products comply with the restrictions of international environmental regulations. Therefore, this study proposes a set of multiple criteria decision-making (MCDM) approaches for systematic green outsourcing evaluation. First, a team of experts is established to discuss mutually dependent relationships among criteria, and the decision-making trial and evaluation laboratory (DEMATEL) technique is applied to generate subjective influential weights. Then, a large amount of data from outsourcing providers is collected, and the criteria importance through the intercriteria correlation (CRITIC) method is used to obtain the objective influential weights. Finally, a novel classifiable technique for ordering preference based on similarity to ideal solutions (classifiable TOPSIS) is proposed to integrate the performance of green outsourcing providers and classify them into four levels. The classifiable TOPSIS improves the shortcomings of conventional TOPSIS and establishes a visual rating diagram to help decision-makers to distinguish the performance of outsourcing providers more clearly. Taking a Taiwanese multinational machine tool manufacturer as an example, the performance of outsourcing providers related to manufacturing activities was investigated to demonstrate the effectiveness and applicability of this proposed model. [ABSTRACT FROM AUTHOR]

Reducing the potential risks in the manufacturing process to improve the reliability of the switched-mode power supply (SMPS) is a critical issue for the users' safety. This paper proposes a novel failure mode and effects analysis (FMEA) model based on hybrid multiple criteria decision-making (MCDM), which adopts neutrosophic set theory into the proposed model. A developed neutrosophic Best Worst method (NBWM) is used to evaluate the weights of risk factors and determine their importance. Secondly, the neutrosophic Weight Aggregated Sum Product Assessments (NWASPAS) method is utilized to calculate the Risk Priority Number (RPN) of the failure modes. The proposed model improves the shortcomings of traditional FMEA and improves the practical applicability and effectiveness of the Best Worst method (BWM) and Weight Aggregated Sum Product Assessments (WASPAS) methods. In addition, this study uses neutrosophic logic to reflect the true judgments of experts in the assessment, which considers authenticity, deviation, and uncertainty to obtain more reliable information. Finally, an empirical case study from an SMPS company headquartered in Taiwan demonstrates the effectiveness and robustness of the proposed model. In addition, by comparing with two other FMEA models, the results show that the proposed model can more clearly reflect the true and effective risks in the assessment. The results can effectively help power supply manufacturers to assess risk factors and determine key failure modes. [ABSTRACT FROM AUTHOR]