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A broad range of companies around the world has welcomed artificial intelligence (AI) technology in daily practices because it provides decision-makers with comprehensive and intuitive messages about their operations and assists them in formulating appropriate strategies without any hysteresis. This research identifies the essential components of AI applications under an internal audit framework and provides an appropriate direction of strategies, which relate to setting up a priority on alternatives with multiple dimensions/criteria involvement that need to further consider the interconnected and intertwined relationships among them so as to reach a suitable judgment. To obtain this goal and inspired by a model ensemble, we introduce an innovative fuzzy multiple rule-based decision making framework that integrates soft computing, fuzzy set theory, and a multi-attribute decision making algorithm. The results display that the order of priority in improvement—(A) AI application strategy, (B) AI governance, (D) the human factor, and (C) data infrastructure and data quality—is based on the magnitude of their impact. This dynamically enhances the implementation of an AI-driven internal audit framework as well as responds to the strong rise of the big data environment. Highlights: Artificial intelligence (AI) promotes the sustainability development of audit tasks. A fuzzy MRDM model extracts key factors from large amounts of data. Fuzzy decision-making trial and evaluation laboratory analysis accounts for dependence and feedback among factors. An effective framework of AI-driven business audit is proposed in which "AI cognition of senior executives" is the most important criterion. [ABSTRACT FROM AUTHOR]

In the family-owned traditional manufacturing businesses, the intricate required competencies and traits of a successful successor resulting in ineffectual acquirement of competent successors could be regarded as one of the main reasons of predicament of inefficient succession. The issues of selecting and training a competent successor should be closely correlated with corporate sustainability and competitiveness of the industries. Nevertheless, in most previous studies, which were related to the successor selection or identification of critical indicators of competent successors, the data collections mainly relied on the subjective viewpoints, expressed in natural language, of shareholders/entrepreneurs. Thus, for the purposes of selecting competent successors, but rectifying the fuzziness and indeterminacy of information, which was generated by the natural language expressions, this study proposed a hybrid successor-selection and efficient successor-training model. The model simultaneously took the incompleteness, ambiguity, and non-concordance of information into consideration. First, based on an intensive literature review and the results of roundtable meetings, an evaluation framework was constructed. Second, under distinct circumstances with varied purposes, the single valued neutrosophic set and triangular fuzzy set technologies were respectively applied to quantify the incomplete, indeterminate and inconsistent information. Finally, Decision Making Trial and Evaluation Laboratory (DEMATEL)-based analytic network process (ANP) with a modified VIKOR (DANP-mV) method was employed to proceed successor-selection evaluation and to develop an effective successor-training program. Additionally, to demonstrate the feasibility and validity of this model, an empirical study of successor selection for a business in the pulp industry was further conducted. The research results considerably benefit family-owned traditional manufacturing industries to develop a successor-selection plan, establish a systematic and efficacious successor-training program, and remedy the conundrum of inefficacious succession. Furthermore, this research considered the fuzziness and indeterminacy of language expression, the developed model could be expected to propose an optimal feasible decision-making scheme for the real world. [ABSTRACT FROM AUTHOR]

The spread of COVID-19 has triggered one of the largest pandemics in modern human history. Humanity is still in the incomplete information period for this infectious disease, and how to effectively deal with such a major public crisis is a crucial problem. Although there are divergences in human natural semantics, the incomplete information increases it. Therefore, this study integrates the neutrosophic set and the Decision-Making Trial and Evaluation Laboratory (DEMATEL) methods to explore the key factors which would prevent expansion of the epidemic in the face of incomplete knowledge. The neutrosophic set technique is an effective tool for the representation of the ambiguity of natural human semantic expression, for the analysis of incomplete, uncertain, and inconsistent information. DEMATEL is used to explore the causes and effects between factors and to generate an influential network relationship map. The results of analysis can help the government and relevant organizations to understand the cause and effect relationship between the factors and set appropriate prevention strategies. The results of this study show that the incorporation of neutrosophic set theory leads to a more meaningful evaluation under incomplete information. "Detect" is a key factor affecting the entire system. The results of this study contribute to the advancement and development of scientifically based decision-making by helping governments and relevant organizations to understand the causal relationships between factors, to set appropriate prevention strategies. [ABSTRACT FROM AUTHOR]

The 2019–2020 COVID-19 pandemic has caused an economic turndown and high volatility in the global financial markets. Determining the factors that influence services to high-net-worth customers who typically have large investments in the financial markets is important for wealth management banks. This study constructs a hybrid approach by integrating the fuzzy logic and Decision-making Trial and Evaluation Laboratory (DEMATEL) methods to identify the most influential criteria affecting the performance of wealth management banks in Taiwan during the 2019–2020 COVID-19 pandemic. Surveys and interviews are designed using four dimensions (bank performance, service quality, customer relationship, and COVID-19) and 16 criteria for 10 banking executives using fuzzy linguistic scales, to obtain their expert opinions. The results show the order of influence as being service quality, customer relationships, bank performance, and COVID-19. The most important criteria are customized investment information (in-service quality), switching behavior (in-customer relationship), fee income (in-bank performance), and the number of confirmed cases in the top five countries affected by COVID-19. The managerial implication is that wealth management banks should focus on improving their service quality. Banks may strive to provide a wide range of customized investment information and increase customers' usage of personalized financial products and services. The results of this study will benefit bank managers in serving high-net-worth individuals during the crisis period. [ABSTRACT FROM AUTHOR]

Biometrics has been developing for decades in diverse industries, such as consumer electronics, internet of things, financial industry, etc. The purpose of this research is to build a decision-making model to evaluate and improve the performances of biometrics for administrators to design and make suitable biometric systems. This paper adopts a hybrid multiple criteria decision making (MCDM) model, comprising decision-making trial and evaluation laboratory (DEMATEL), and DEMATEL-based analytic network process (called DANP) to probe into the interrelationship and influential weights among criteria of biometrics. According to DEMATEL technique, the empirical results indicate that criteria of biometrics have self-effect relationships. The dimension of biometrics that administrators of biometrics should enhance first when improving the performances is usability. The criterion of universality with the highest influencing value to systematically affect all other evaluation factors is what administrators of biometrics should comprehensively consider. In the top three criteria for evaluation by DANP, biometric systems with the most influential weight is the criterion that can be improved to have higher recognition rates for increasing the performances of biometrics, followed by biometric conditions and permanence. [ABSTRACT FROM AUTHOR]

Although certain countries have proposed revitalization to address the issue of inefficient industrial parks in recent years, there is little research exploring the causes of inefficient industrial parks—although this would help in formulating improvement strategies and further resource allocation. This study presents an evaluation framework for the causes of inefficient urban industrial parks based on a novel hybrid modified multi-attribute decision-making model in real cases, which includes three dimensions and 10 criteria. Two cases of "industrial parks designated by urban planning" in Taiwan were examined. The results of the Decision-Making Trial and Evaluation Laboratory (DEMATEL) and the DEMATEL technique based on the basic concept of the analytic network process indicated that the parcel dimension (A1) and policy dimension (A3) had more influence on causing inefficiency in industrial parks. The criteria with bigger impact were "property rights (B1)," "size and shape (B2)," "location (B3)," "planning and development mechanisms (B8)," "management mechanisms (B9)," and "spatial planning policy for industry (B10)." The modified Vlse Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) revealed that policy dimension (A3) and its criteria have relatively poor performance in two cases; solving the policy dimension is the most critical strategy for IPDUP. This presented decision-making model not only identifies the key causes of the inefficient industrial park and further improvements to it, but also provides a direction for evaluating, planning, and developing sustainable industrial parks. [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]

This paper proposes fuzzy integral-based decision methods to identify the core factors and their relationships for smart homes product improvement. The dominance-based rough set approach was used to retrieve core attributes and obtain rough knowledge-based rules. The decision-making trial and evaluation laboratory (DEMATEL) technique was used to build an influential network relationship map, and influential weights were determined through the DEMATEL-based analytic network process. Subsequently, the inter-relationships among criteria were calculated. Finally, the fuzzy integral method was used to measure the plausible synergy effects among the criteria, evaluate/rank alternatives for smart homes, and then provide suggestions for product improvement. The main innovation is the use of rough knowledge-based rule retrieval procedures and fuzzy measures for exploring the synergy effects on smart home improvement. Three smart home products/systems were examined to illustrate their performance on each criterion for improvement planning. This study contributes knowledge to research on consumer adoption of smart homes and presents improvement strategies. [ABSTRACT FROM AUTHOR]

Search engine optimization (SEO) has been considered one of the most important techniques in internet marketing. This study establishes a decision model of search engine ranking for administrators to improve the performances of websites that satisfy users' needs. To probe into the interrelationship and influential weights among criteria of SEO and evaluate the gaps of performance to achieve the aspiration level in real world, this research utilizes hybrid modified multiple criteria decision-making models, including decision-making trial and evaluation laboratory (DEMATEL), DEMATEL-based analytic network process (called DANP), and VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR). The empirical findings discover that the criteria of SEO possessed a self-effect relationship based on DEMATEL technique. According to the influential network relation map (INRM), external website optimization is the top priority dimension that needs to be improved when implementing SEO. Among the six criteria for evaluation, meta tags is the most significant criterion influencing search engine ranking, followed by keywords and website design. The evaluation of search engine ranking reveals that the website with lowest gap would be the optimal example for administrators of websites to make high ranking website during the time that this study is executed. [ABSTRACT FROM AUTHOR]

Governments worldwide are cognizant of the deficiencies of measuring quality of life according to gross domestic product; consequently, they have created indicators of citizen well-being that can be applied in governmental policy. To improve citizens' lives, governments should create an environment with maximal quality of life, ideal material living conditions, and sustainable well-being and determine the true needs of citizens. This paper proposes a hybrid multiple-attribute decision-making model with a modified PROMETHEE method that can identify practical performance-improvement strategies for the sustainable development of a better life by accounting for the complex dependencies among the indicators. In the proposed model, an influential network relation map is first obtained using the DEMATEL method to delineate the interrelationships among the indicators. Second, to address the feedback and dependence concerns associated with interrelationships among criteria, the influence weights are identified through the DANP method. Finally, the degree of preference is determined through the modified PROMETHEE method and combined with the influence weights to calculate flow indicators, which facilitate attaining the aspiration level. The aspiration level is set and an aspiration alternative is identified as an improvement benchmark to facilitate continual melioration and sustainable development of a better life. This model uses the OECD well-being indicators consisting of 11 dimensions and 24 criteria to measure material living conditions (namely housing, income, jobs) and quality of life (namely civic engagement, community, education, environment, health, life satisfaction, safety, and work–life balance). An empirical case study was performed to illustrate the feasibility and effectiveness of the proposed model by using questionnaire data of six cities in Taiwan. Our results prove that the proposed model (aspired-worst as benchmark) outperforms the traditional model (max–min as benchmark) and that it can obtain more realistic net flows and an improvement benchmark to conduct ranking, selection, and continual improvement for facilitating sustainable development of a better life. [ABSTRACT FROM AUTHOR]

In the recent years, various statistical and computational intelligence or machine learning techniques have contributed to the progress of automation or semiautomation for measuring consumer credit scoring in the banking sector. However, most of the Taiwanese commercial banks still rely on seasoned staffs' judgments on making the final approvals or rejections. To enhance the understanding and transparency of a decision support system (or model) that can assist bank staffs on making their consumer credit loan decisions—while uncertainty exist—is of high business value. One of the promising approaches is multiple rule-based decision-making (MRDM), a subfield of the hybrid multiple criteria decision-making that leverages the advantages of machine learning, soft computing, and decision methods (or techniques). The MRDM approach reveals comprehensible logics (rules or patterns) that can be justified and compared with the existing knowledge of veterans to reinforce the confidence of their judgments. Therefore, in the present study, we propose and compare two MRDM approaches in assisting decision makers on the consumer credit loan evaluations. A set of historical data from a commercial bank in Taiwan is analyzed for illustrating the plausible pros and cons of the two approaches with discussions. [ABSTRACT FROM AUTHOR]

Because gross domestic product is a deficient measure of well-being, designing well-being indicators has become a new international trend for informing government policies to better citizens' lives. To facilitate achieving this goal, governments must establish sustainable, sufficient material living conditions and quality of life and attempt to understand citizens' real needs. Given that analyzing well-being involves imprecise information, a fuzzy multiple criteria decision-making (MCDM) model is conducive to addressing the complex interrelationships among all indicators. Therefore, this paper proposes a hybrid fuzzy MCDM model that could facilitate strategizing for better life development. With aspiration levels based on the well-being indicators of the Organization for Economic Cooperation and Development, the proposed model combines the fuzzy DEMATEL technique, fuzzy DEMATEL-based analytic network process, and modified fuzzy VIKOR methods to process the interrelationships among all indicators and select the most pressing factors requiring improvement and development. A case study was used to demonstrate the effectiveness of the proposed fuzzy model. The empirical results attested that the proposed fuzzy model is superior to the traditional fuzzy model, identifying that for Taiwan, the 'air pollution' criterion in the 'environment' dimension is the most pressing issue to be addressed. [ABSTRACT FROM AUTHOR]

Nearly all companies need to retrieve valuable information from business data to increase its efficiency or value, and the rising interests of research in this domain could be named as business analytics. Because most of the problems (obstacles) faced by business have to consider a group of complex and interrelated factors, conventional statistics models (e.g., regression) have constraints in resolving these interrelated and complex problems. Therefore, this study proposes a novel multiple attribute decision-making model to resolve-from ranking/selection to improvement planning-the problems of business analytics in finance, based on the similarity with positive contexts (rules) and the dissimilarity with negative ones. The proposed model not only enhances the previous method (i.e., dominance-based rough set approach, DRSA) on ranking within the same decision class, but also provides a contextual approach to guide businesses for systematic improvements. Infused with the modified VIKOR method, the proposed model could support a company to transform analytics into priority contexts, which may guide improvement planning. To show the proposed model, a group of semiconductor companies in Taiwan is analyzed as an empirical case, and three companies are taken as examples to illustrate the ranking and improvement planning processes. The obtained findings thus contribute to bridge the applications of data-driven business analytics to the field of decision science in practice. [ABSTRACT FROM AUTHOR]

As mobile commerce (m-commerce) has continued to grow via advancements in wireless and mobile technology, the issue of m-commerce has become more significant. To improve m-commerce adoption, companies should establish a perfect m-commerce environment and learn to understand consumer needs. This paper proposes an evaluation model for m-commerce that can explore and improve m-commerce adoption for uncertain information in a fuzzy environment. The model addresses the interdependence and feedback effects between criteria or dimensions, the best alternative selection and systematic improvement by adopting a new hybrid fuzzy MADM model, which uses the fuzzy DEMATEL technique to construct the fuzzy INRM and determine the fuzzy influential weights using the fuzzy DANP. It further combines the fuzzy VIKOR methods for creating the best improvement plan based on the fuzzy INRM. An empirical case for evaluating m-commerce adoption is used to verify the proposed planning model. The results reveal that the proposed planning model can help companies improve m-commerce adoption for enhancing consumer trust via integrity. [ABSTRACT FROM AUTHOR]

To support investment decision based on technical analysis (TA), this study aims to retrieve the knowledge or rules of various indicators by a hybrid soft computing model. Although the validity of TA has been examined extensively by various statistical methods in literature, previous studies mainly explored the effectiveness of each technical indicator separately; therefore, a practical approach that may consider the inconsistency of various technical indicators simultaneously and the down-side risk of an investment decision is still underexplored. Thus, a hybrid model-by constructing a variable consistency dominance-based rough set approach (VC-DRSA) information system with the fuzzy inference-enhanced discretization of signals-is proposed, to retrieve the imprecise patterns from commonly adopted technical indicators. At the first stage, the trading signals (i.e., buy, neutral, or sell) are preprocessed in two groups: straight-forward signals and complicated signals. The straight-forward technical indicators (i.e., for signals that are decided by precise rules) are suggested by domain experts, and the buy-in signals are simulated by several trading strategies to examine the outcomes of each indicator. As for those complicated signals (i.e., for signals that require imprecise judgments with perceived feeling of domain experts to identify patterns), a fuzzy inference technique is incorporated to enhance the discretization of signals; those signals are also simulated by the aforementioned trading strategies to obtain the corresponding results. At the second stage, the trading signals generated by each technical indicator and their pertinent results from the previous stage are combined for VC-DRSA modeling to gain decision rules. To illustrate the proposed model, the weighted average index of the Taiwan stock market was examined from mid/2002 to mid/2014, and a set of decision rules with nearly 80 % classification accuracy (both in the training and the testing sets) were obtained in this empirical case. The findings suggest that several technical indicators should be considered simultaneously, and the retrieved rules (knowledge) have practical implications for investors. [ABSTRACT FROM AUTHOR]

Mobile banking services are one of the most promising recent technological innovations. In this study, we developed a conceptual model to explore mobile banking services for user behavior in the financial banking industry in intention adoption. The aim of this study is to explore the effect of user behavior and guidance on the mobile banking services intention adoption structure model among customers based on decomposed theory of planned behavior and trust-related behaviors based on the knowledge of experts. In this study, we use a new hybrid model, the multiple attribute decision making (MADM) model, which combines decision making trial and evaluation laboratory (DEMATEL) for building an influential network relationship map (INRM), DANP (DEMATEL-based ANP) for determining the influential weights of criteria, and the VIKOR method using the influential weights to evaluate and integrate the criteria in the gaps and reduce the gaps to satisfy the users' behavior needs based on INRM. An empirical case of Taiwan's financial banking industry is used as an example to demonstrate the application of the proposed hybrid MADM model and its efficiency. In the results, we find that the proposed user behavior framework can offer a deeper understanding of the variables/criteria that influence the interrelationship for the intention adoption of mobile banking services by DEMATEL technique. We can also combine the influential weights of DANP with weighting gaps using the VIKOR method to evaluate how to reduce these gaps and provide the best improvement strategies to satisfy the mobile banking services for users' behavior needs. [ABSTRACT FROM AUTHOR]

This study attempts to diagnose the financial performance improvement of commercial banks by integrating suitable soft computing methods. The diagnosis of financial performance improvement comprises of three parts: prediction, selection and improvement. The performance prediction problem involves many criteria, and the complexity among the interrelated variables impedes researchers to discover patterns by conventional statistical methods. Therefore, this study adopts a dominance-based rough set approach to solve the prediction problem, and the core attributes in the obtained decision rules are further processed by an integrated multiple criteria decision-making method to make selection and to devise improvement plans. By using VIKOR method and the influential weights of DANP, decision maker may plan to reduce gap of each criterion for achieving aspired level. The retrieved attributes (i.e., criteria) are used to collect the knowledge of domain experts for selection and improvement. This study uses the data (from 2008 to 2011) from the central bank of Taiwan for obtaining decision rules and forming an evaluation model; furthermore, the data of five commercial banks in 2011 and 2012 are chosen to evaluate and improve the real cases. In the result, we found the top-ranking bank outperformed the other four banks, and its performance gaps for improvements were also identified, which indicates the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

There are many studies on the application examples and the definitions of the super-matrix in the Analytic Network Process (ANP). The ANP applies network structures with the alternatives and the criteria to clarify a complex decision-making. It is important to obtain the consent from both the criteria and the alternatives because the evaluation is relative and reciprocal. In addition, there are very few studies on the interaction between the criteria and the alternatives. There is no realistic interpretation of the maximum eigenvalue in the ANP and the negative elements of the criteria matrix. Based on our study, the evaluation with only the alternatives consists of the missing values or the non-square matrix, it is not easy to make. The ANP that contains these irregular alternatives is defined as 'Minor ANP'. Then, we describe these methods of making the priority of only the alternative's matrix values by using the ANP. This study has proven that the missing values in the alternative matrix can be replaced by zeros such that the criteria matrix can be derived without using the inverse alternative matrix. Then, the priority of alternative selection can be obtained through the calculation of eigenvector. An empirical case of the best selection of supplier illustrates our proposed methods for applying to the manufacturing industry. The result reveals the methods can be well adopted in the real world. [ABSTRACT FROM AUTHOR]

The concept of globalization has been prosperous in the past decades while manufacturing as well as logistics have already become one of the most significant issues in the globalization era. However, while modern globalized firms are leveraging both global manufacturing resources as well as logistics systems for pursuing higher quality, lower cost as well as product differentiation, how to evaluate, selecting an appropriate global manufacturing strategy by considering issues from both aspects of global manufacturing as well as logistics has become one of the most critical and difficult issues. Moreover, how the chosen intertwined global manufacturing as well as logistics system is to be optimized so that the aspired level of the global manufacturing system can be achieved have few been addressed. Thus, this research aims to resolve the above mentioned global manufacturing and logistics strategy selection as well as system reconfiguration issue. A Decision Making Trial and Evaluation Laboratory (DEMATEL) technique based novel multiple criteria decision making (MCDM) method with Analytic Network Process (ANP), Grey Relational Analysis (GRA) as well as VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) will be proposed for selecting and re-configuring the aspired global manufacturing and logistics system. An empirical study based on the global manufacturing and logistics system design of a semiconductor company will be provided for verifying the effectiveness of this proposed methodology. [ABSTRACT FROM AUTHOR]

At present, there are many factors affecting customer's choice when they decide on what to buy in a fuzzy environment. In the current customer's market, everyone wants things that are affordable but of high quality. Taking this into consideration, it becomes very important for each business to make a competent marketing strategic decision. They must be aware of the customer's thoughts to expand their market share and outdo their competitors. Clearly, the market still has a lot of factors to consider. Therefore, the critical issue is to identify with the customer's thoughts and construct a Customer's Choice Behavior Model (CCBM) for solving this problem of Customer's Choice. In this paper, a framework that considers customer's choice behavior, applies the graphtheorybased fuzzy Decision Making Trial and Evaluation Laboratory (Fuzzy DEMATEL) approach combined with Fuzzy Multiple Criteria Decision Making (FMCDM) is proposed. First, fuzzy DEMATEL is used to find the key factors and attributes in building the structure relations of an ideal Customer's Choice Behavior. Then, FMCDM is demonstrated to evaluate whether the firm's performance and customer's satisfaction have differences or not. Empirical studies of Lexus and BMW are illustrated to show how well Lexus' sales is doing, whether or not they had satisfied Customers' needs, and determine the magnitude of the gap between them in order to correct this with the right strategy. [ABSTRACT FROM AUTHOR]

This paper applies fuzzy set theory to the Cox, Ross and Rubinstein (CRR) model to set up the fuzzy binomial option pricing model (OPM). The model can provide reasonable ranges of option prices, which many investors can use it for arbitrage or hedge. Because of the CRR model can provide only theoretical reference values for a generalized CRR model in this article we use fuzzy volatility and fuzzy riskless interest rate to replace the corresponding crisp values. In the fuzzy binomial OPM, investors can correct their portfolio strategy according to the right and left value of triangular fuzzy number and they can interpret the optimal difference, according to their individual risk preferences. Finally, in this study an empirical analysis of S&P 500 index options is used to find that the fuzzy binomial OPM is much closer to the reality than the generalized CRR model. [ABSTRACT FROM AUTHOR]

This paper describes a fuzzy hierarchical analytic approach to determine the weighting of subjective judgments. In addition, it presents a nonadditive fuzzy integral technique to evaluate a green engineering industry case as a fuzzy multicriteria decision-making (FMCDM) problem. When the investment strategies are evaluated from various aspects, such as economic effectiveness, technical feasibility, and environmental regulation, it can be regarded as an FMCDM problem. Since stakeholders cannot clearly estimate each considered criterion in terms of numerical values for the anticipated alternatives/strategies, fuzziness is considered to be applicable. Consequently, this paper uses triangular fuzzy numbers to establish weights and anticipated achievement values. By ranking fuzzy weights and fuzzy synthetic utility values, we can determine the relative importance of criteria and decide the best strategies. This paper applies what is called a λ fuzzy measure and nonadditive fuzzy integral technique to evaluate the synthetic performance of green engineering strategies for aquatic products processors in Taiwan. In addition, we demonstrate that the nonadditive fuzzy integral is an effective evaluation and appears to be appropriate, especially when the criteria are not independent. [ABSTRACT FROM AUTHOR]

Research has already shown that turbulent flow consists of some coherent time- and space-organized vortical structures. Some dynamic systems and experimental models are employed to understand the turbulent generation mechanism. However, these approaches still cannot provide a good nonlinear analysis of turbulent time-series. In the real turbulent flow, very complicated nonlinear behaviors, which are affected by many vague factors are present. Based on the nonlinear behavior and the results of from this traditional research, we introduce multivariate statistical analysis of an experimental study to explain practical phenomenon. In this paper, a new approach of fuzzy piecewise regression analysis with automatic change-point detection is proposed to predict the nonlinear time-series of turbulent flows. In order to show the practicality and usefulness of this model, we present an example of predicting the near-wall turbulence time-series as a verifiable model. The results of practical applications show that the proposed method is appropriate and appears to be useful in nonlinear analysis and in fuzzy environments to predict the turbulence time-series. [ABSTRACT FROM AUTHOR]