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PurposeThe purpose of this paper is to develop a novel intuitionistic fuzzy seasonality regression (IFSR) with particle swarm optimization (PSO) algorithms to accurately forecast air pollutions, which are typical seasonal time series data. Seasonal time series prediction is a critical topic, and some time series data contain uncertain or unpredictable factors. To handle such seasonal factors and uncertain forecasting seasonal time series data, the proposed IFSR with the PSO method effectively extends the intuitionistic fuzzy linear regression (IFLR).Design/methodology/approachThe prediction model sets up IFLR with spreads unrestricted so as to correctly approach the trend of seasonal time series data when the decomposition method is used. PSO algorithms were simultaneously employed to select the parameters of the IFSR model. In this study, IFSR with the PSO method was first compared with fuzzy seasonality regression, providing evidence that the concept of the intuitionistic fuzzy set can improve performance in forecasting the daily concentration of carbon monoxide (CO). Furthermore, the risk management system also implemented is based on the forecasting results for decision-maker.FindingsSeasonal autoregressive integrated moving average and deep belief network were then employed as comparative models for forecasting the daily concentration of CO. The empirical results of the proposed IFSR with PSO model revealed improved performance regarding forecasting accuracy, compared with the other methods.Originality/valueThis study presents IFSR with PSO to accurately forecast air pollutions. The proposed IFSR with PSO model can efficiently provide credible values of prediction for seasonal time series data in uncertain environments.

To protect the environment and achieve the Sustainable Development Goals (SDGs), reducing greenhouse gas emissions has been actively promoted by global governments. Thus, clean energy, such as wind power, has become a very important topic among global governments. However, accurately forecasting wind power output is not a straightforward task. The present study attempts to develop a fuzzy seasonal long short-term memory network (FSLSTM) that includes the fuzzy decomposition method and long short-term memory network (LSTM) to forecast a monthly wind power output dataset. LSTM technology has been successfully applied to forecasting problems, especially time series problems. This study first adopts the fuzzy seasonal index into the fuzzy LSTM model, which effectively extends the traditional LSTM technology. The FSLSTM, LSTM, autoregressive integrated moving average (ARIMA), generalized regression neural network (GRNN), back propagation neural network (BPNN), least square support vector regression (LSSVR), and seasonal autoregressive integrated moving average (SARIMA) models are then used to forecast monthly wind power output datasets in Taiwan. The empirical results indicate that FSLSTM can obtain better performance in terms of forecasting accuracy than the other methods. Therefore, FSLSTM can efficiently provide credible prediction values for Taiwan’s wind power output datasets.

The aim of this study was to develop a novel intuitionistic Type-2 fuzzy inference system (IT-2 FIS) which adopts a parameterized Yager-generating function and particle swarm optimization (PSO). In IT-2 FIS, the intuitionistic Type-2 is set as a fuzzy symmetrical triangular number in which the hesitation degree adopts the Yager-generating function, and the parameters of the proposed IT-2 FIS adopting the PSO are tuned. The intuitionistic and Type-2 fuzzy sets have been proven to be the most effective for handling more uncertainty. Therefore, this study proposes an intuitionistic Type-2 set with a Yager-generating function to enhance the conventional fuzzy inference system. Moreover, PSO can improve the fuzzy inference system by searching for the optimal parameters of IT-2 FIS. In this study, linguistic variables were represented by triangular fuzzy numbers (TFS). Two numerical examples were examined: capacity-planning and medical diagnosis problems. An approaching capacity-loadings example was used to verify that the proposed IT-2 FIS could effectively estimate the results of the capacity loadings. In the medical diagnosis problem, IT-2 FIS could obtain a higher correct rate by revealing experts&rsquo, knowledge. In both examples, the proposed IT-2 FIS provided more objective estimated values than traditional fuzzy inference systems (FIS) and Type-2 FIS.

This study develops the approximate fuzzy Decision Making Trial and Evaluation Laboratory (AFDEMATEL) to analyze uncertain influential factors. The approximate fuzzy arithmetic operations under the weakest t-norm (Tω) arithmetic operations to evaluate sustainable supply chain management based on AFDEMATEL. The fuzzy DEMATEL is one of important decision-making method under uncertain environment. The fuzzy DEMATEL had to be developed for clearly display expert’s options with linguistic variables. The fuzzy operations usually adopt α-cut arithmetic in fuzzy DEMATEL. In this research, the fuzzy DEMATEL technology is substituted with the AFDEMATEL technology. In the sustainable supply chain management example, the AFDEMATEL is employed to find fuzzy cause and effect relationships among criteria. Particular note should be made of the following: [1] the fuzzy DEMATEL with α-cut arithmetic model cannot exactly handle fuzzy cause and effect relationships under uncertain environment, and the fuzziness accumulation phenomenon of the α-cut arithmetic may influence final fuzzy cause and effect relationships; and [2] the approximate fuzzy arithmetic operations gives a justifiable fuzziness spread to analyze fuzzy cause and effect relationships. In the case of selection of cans suppliers, the AFDEMATEL examines the influential factors. Proposed method provides justifiable fuzzy cause and effect relationships with approximate fuzzy arithmetic and can analyze across quadrants phenomenon under uncertain environment, since decision-makers usually want to accurately estimate uncertain influential factors.

Printed circuit boards (PCBs) are very important materials in consumer electronic products. The process of making PCBs usually uses chemicals, and large quantities of water are needed. PCB manufacturers are devoted to improving the process in order to conform to environmental protection rules and regulations. However, evaluating the performance of environmental protection strategies by quantitative methods is very difficult. Therefore, this study attempts to evaluate the environmental protection strategy of a PCB manufacturer using the novel weakest t-norm (Tw) fuzzy importance-performance analysis with Google Trends (TFIPA-Google). The TFIPA-Google methodology obtain advantages of Tw operations, IPA, and Google Trends, which can handle uncertainty based on a fuzzy matrix of IPA, reduce fuzzy accumulation using the Tw operators, and analyze social media viewpoints using Google Trends. This empirical example based on the TFIPA-Google method shows that the recovered waste material management system should be a priority for PCB manufacturers. Moreover, the TFIPA-Google method can provide more creditable information, based on Tw operations and volume of Google Trends for decision-makers, than a conventional importance-performance analysis (IPA) model.

Classification problems are very important issues in real enterprises. In the patent infringement issue, accurate classification could help enterprises to understand court decisions to avoid patent infringement. However, the general classification method does not perform well in the patent infringement problem because there are too many complex variables. Therefore, this study attempts to develop a classification method, the support vector machine with new fuzzy selection (SVMFS), to judge the infringement of patent rights. The raw data are divided into training and testing sets. However, the data quality of the training set is not easy to evaluate. Effective data quality management requires a structural core that can support data operations. This study adopts new fuzzy selection based on membership values, which are generated from fuzzy c-means clustering, to select appropriate data to enhance the classification performance of the support vector machine (SVM). An empirical example based on the SVMFS shows that the proposed SVMFS can obtain a superior accuracy rate. Moreover, the new fuzzy selection also verifies that it can effectively select the training dataset.

A decision model for selecting a suitable supplier is a key to reducing the environmental impact in green supply chain management for high-tech companies. Traditional fuzzy weight average (FWA) adopts linguistic variable to determine weight by experts. However, the weights of FWA have not considered the public voice, meaning the viewpoints of consumers in green supply chain management. This paper focuses on developing a novel decision model for green supplier selection in the One Belt and One Road (OBOR) initiative through a fuzzy weighted average approach with social media. The proposed decision model uses the membership grade of the criteria and sub-criteria and its relative weights, which consider the volume of social media, to establish an analysis matrix of green supplier selection. Then, the proposed fuzzy weighted average approach is considered as an aggregating tool to calculate a synthetic score for each green supplier in the Belt and Road initiative. The final score of the green supplier is ordered by a non-fuzzy performance value ranking method to help the consumer make a decision. A case of green supplier selection in the light-emitting diode (LED) industry is used to demonstrate the proposed decision model. The findings demonstrate (1) the consumer’s main concerns are the “Quality” and “Green products” in LED industry, hence, the ranking of suitable supplier in FWA with social media information model obtained the difference result with tradition FWA; (2) OBOR in the LED industry is not fervently discussed in searches of Google and Twitter; and (3) the FWA with social media information could objectively analyze the green supplier selection because the novel model considers the viewpoints of the consumer.

Over the past two decades, corporate social responsibility (CSR) has received worldwide attention. Publication of CSR reports has become the trend for domestic and foreign enterprises. In the constantly changing and competitive corporate environment, public attention has come to be focused on how enterprises play the role of corporate citizen, and how they achieve a balance of profitable, environmental and charitable activities. However, most quantitative CSR studies to date have concentrated on traditional statistical approaches. The data mining technique has not been widely explored in this area. Thus, this investigation proposes a hybrid data mining CSFSC model, which stands for the first letters of CFS, SMOTE, FCM, SVMOAO and C5.0, integrating data-preprocessing approaches, a classification method and a rule generation mechanism for analyzing CSR data. The data-preprocessing approaches include correlation-based feature selection (CFS), the synthetic minority over-sampling technique (SMOTE) and the fuzzy c-means (FCM) clustering algorithm. The support vector machine one-against-one (SVMOAO) method was employed as a classifier for performing multiclassification, and the C5.0 decision tree algorithm was utilized to generate rules from the results of the SVMOAO model. In this study, CSR data collected from China's listed firms in 2010 were used to test the performance of the proposed model. The empirical results showed that the designed CSFSC model yields satisfactory classification accuracy, and can provide rules for decision makers. Therefore, the presented CSFSC model is a feasible and effective alternative in analyzing CSR data.

This study proposes a novel evolutionary kernel intuitionistic fuzzy c-means clustering algorithm (EKIFCM) that combines Atanassov's intuitionistic fuzzy sets (IFSs) with kernel-based fuzzy c-means (KFCM), and genetic algorithms (GA) are optimally used simultaneously to select the parameters of the EKIFCM. The EKIFCM can obtain the advantages of intuitionistic fuzzy sets, kernel functions, and GA in actual clustering problems. Experiments on 2-D synthetic datasets and machine learning repository (http://archive.ics.uci.edu/beta/) datasets show that the proposed EKIFCM is more efficient than conventional algorithms such as the k-means (KM), FCM, Gustafson-Kessel (GK) clustering algorithm, Gath-Geva (GG) clustering algorithm, Chaira's intuitionistic fuzzy c-means (IFCM), and kernel-based fuzzy c-means with Gaussian kernel functions [KFCM(G)] in standard measurement indexes.

Accurate prediction of tourism demand is a crucial issue for the tourism and service industry because it can efficiently provide basic information for subsequent tourism planning and policy making. To successfully achieve an accurate prediction of tourism demand, this study develops a novel forecasting system for accurately forecasting tourism demand. The construction of the novel forecasting system combines fuzzy c-means (FCM) with logarithm least-squares support vector regression (LLS-SVR) technologies. Genetic algorithms (GA) were optimally used simultaneously to select the parameters of the LLS-SVR. Data on tourist arrivals to Taiwan and Hong Kong were used. Empirical results indicate that the proposed forecasting system demonstrates a superior performance to other methods in terms of forecasting accuracy.

Accurate project-profit prediction is a crucial issue because it can provide an early feasibility estimate for the project. In order to achieve accurate project-profit prediction, this study developed a novel two-stage forecasting system. In stage one, the proposed forecasting system adopts fuzzy clustering technology, fuzzy c-means (FCM) and kernel fuzzy c-means (KFCM), for the correct grouping of different projects. In stage two, least-squares support vector regression (LSSVR) technology is employed for forecasting the project-profit in different project groups, respectively. Moreover, genetic algorithms (GA) were simultaneously used to select the parameters of the LSSVR. The project data come from a real enterprise in Taiwan. In this study, some forecasting methodologies are also compared, for instance Generalized Regression Neural Network (GRNN), Radial Basis Function Neural Networks (RBFNN), and Back Propagation Neural Network (BPNN), to predict project-profit in this real case. Empirical results indicate that the two-stage forecasting system (FCM+LSSVR and KFCM+LSSVR) has superior performance in terms of forecasting accuracy, compared to other methods. Furthermore, in observing the results of the two-stage forecasting system, it can be seen that FCM+LSSVR can achieve superior performance, and KFCM+LSSVR can achieve consistently good performance. Therefore, based on the empirical results, the two-stage forecasting system was verified to efficiently provide credible predictions for project-profit forecasting.

This paper developed a novel intuitionistic fuzzy least-squares support vector regression with genetic algorithms (IFLS-SVRGAs) to accurately forecast the long-term indexes of business cycles. Long-term business cycle forecasting is an important issue in economic evaluation, as business cycle indexes may contain uncertain factors or phenomena such as government policies and financial meltdowns. In order to effectively handle such factors and accidental forecasting indexes of business cycles, the proposed method combined intuitionistic fuzzy technology with least-squares support vector regression (LS-SVR). The LS-SVR method has been successfully applied to forecasting problems, especially time series problems. The prediction model in this paper adopted two LS-SVRs with intuitionistic fuzzy sets, in order to approach the intuitionistic fuzzy upper and lower bounds and to provide numeric prediction values. Furthermore, genetic algorithms (GAs) were simultaneously employed in order to select the parameters of the IFLS-SVR models. In this study, IFLS-SVRGA, intuitionistic fuzzy support vector regression (IFSVR), fuzzy support vector regression (FSVR), least-squares support vector regression (LS-SVR), support vector regression (SVR) and the autoregressive integrated moving average (ARIMA) were employed for the long-term index forecasting of Taiwanese businesses. The empirical results indicated that the proposed IFLS-SVRGA model has better performance in terms of forecasting accuracy than the other methods. Therefore, the IFLS-SVRGA model can efficiently provide credible long-term predictions for business index forecasting in Taiwan.

Revenue forecasting is difficult but essential for companies that want to create high-quality revenue budgets, especially in an uncertain economic environment with changing government policies. Under these conditions, the subjective judgment of decision makers is a crucial factor in making accurate forecasts. This investigation develops a fuzzy least-squares support vector regression model with genetic algorithms (FLSSVRGA) to forecast seasonal revenues. The FLSSVRGA uses the H-level to control the possibility distribution range yielded by the fuzzy model and to provide the fuzzy prediction interval. Depending on various factors, such as the global economy and government policies, a decision maker can elect a different level for H using the FLSSVRGA. The proposed FLSSVRGA model is a rolling forecasting model with time series data updated monthly that predicts revenue for the coming month. Four other forecasting models: the seasonal autoregressive integrated moving average (SARIMA), generalized regression neural networks (GRNN), support vector regression with genetic algorithms (SVRGA) and least-squares support vector regression with genetic algorithms (LSSVRGA), are employed to forecast the same data sets. The experimental results indicate that the FLSSVRGA model outperforms all four models in terms of forecasting accuracy. Thus, the FLSSVRGA model is a useful alternative for forecasting seasonal time series data in an uncertain environment; it can provide a user-defined fuzzy prediction interval for decision makers.

Material selection is a very important issue for an electronics company as it includes many qualitative or quantification factors. The material selection problem is associated with design and manufacturing problems which have been widely investigated. This study develops a hybrid fuzzy decision-making model which combines the fuzzy weight average (FWA) with the fuzzy inference system (FIS) for material substitution selection in the electronics industry. FWA is employed to select a substitute material in an uncertain environment, while FIS is used for reasoning purposes. FWA with @a-cuts arithmetic (FWA"@a"-"c"u"t) is a popularly technology in decision-making problems. However, FWA"@a"-"c"u"t may result in the following unanticipated situations: (1) unclear decision situations; (2) undecided results expressed by fuzzy membership functions; and (3) high computational complexity. Therefore, a fuzzy weight average with the weakest t-norm (FWA"T"@w) is designed as an alternative method for group decision making. In contrast to traditional FWA methods, FWA"T"@w obtains more visible fuzzy results for decision makers with lower computational complexity, and can provide exacter estimation by the weakest t-norm operations in uncertain environment. Thus, the proposed hybrid fuzzy decision-making model imitates an expert's experiences and can estimate substitution purchasing in various statuses. A real material substitution selection case is employed to examine the feasibility of the proposed model; experimental results reveal that the proposed model performs better than the traditional FWA model in coping with material substitution selection problems.

This paper develops a novel weakest t-norm (T"@w) fuzzy Graphical Evaluation and Review Technique (GERT) simulation technology. This proposal is designed to be useful in a realistic environment and improve upon the traditional fuzzy GERT insofar as it has been developed for analyzing complex systems in uncertain environments; the traditional system usually adopts @a-cut arithmetic operations for its calculations. In this research, the fuzzy support system develops the T"@w fuzzy GERT as a substitute for traditional fuzzy GERT technology. In the examples, the fuzzy support system constructs a model of 300mm manufacturing processes in the context of a lithography area. Moreover, the manufacturing processes model is examined with regard to the fuzzy support system using two types of fuzzy arithmetic: @a-cut arithmetic and the T"@w operator. Notably: (1) both types of fuzzy arithmetic provide a reliable analysis of the fuzzy GERT model with regard to a lithography area; (2) under the traditional fuzzy GERT model, the @a-cut arithmetic provides results such that the fuzziness of the model calculation was fuzzier than that of the T"@w fuzzy arithmetic due to the accumulation of fuzziness of the @a-cut arithmetic; (3) the @a-cut arithmetic cannot effectively preserve the original shape of a membership function; and (4) the T"@w arithmetic gives a justifiable fuzziness/fuzzy spread because it takes only the maximal fuzziness encountered and calculates that into the operation. Our proposed T"@w fuzzy GERT can successfully analyze a 300mm manufacturing process; this has been evidenced in the research. Additionally, the proposed model uses simple arithmetic operations rather than traditional fuzzy GERT with applications in complex manufacturing systems. Moreover, the T"@w arithmetic provides more credible (or conservative) information/results with regard to the amount of fuzziness in the 300mm manufacturing processing model.

In general, the fuzzy Graphical Evaluation and Review Technique (GERT) usually evaluates/analyzes variables with interval arithmetic (α-cut arithmetic) operations, especially those with complicated fuzzy systems. Thus the interval arithmetic operations may occur accumulating phenomenon of fuzziness in complicated systems, and the accumulating phenomenon of fuzziness may make decision-maker that cannot effectively evaluate problems/systems under vague environment. In order to overcome the accumulating phenomenon of fuzziness or credibly reduce fuzzy spreads, this study adopts approximate fuzzy arithmetic operations under the weakest t-norm arithmetic operations (Tω) to evaluate fuzzy reliability models based on fuzzy GERT simulation technology. The approximate fuzzy arithmetic operations employ principle of interval arithmetic under the weakest t-norm arithmetic operations. Therefore, the novel fuzzy arithmetic operations may obtain fitter decision values, which have smaller fuzziness accumulating, under vague environment. In numerical examples the approximate fuzzy arithmetic operations has evidenced that it can successfully calculate results of fuzzy operations as interval arithmetic, and can more effectively reduce fuzzy spreads. In the real fuzzy repairable reliability model the performance also shows that the approximate fuzzy arithmetic operations successfully analyze the reliability problem and obtain more confident fuzzy results.

Standby redundant system is a powerful tool to enhance the reliability, availability, quality, and safety of operational plants. This study proposed an approximate Tω (the weakest t-norm) fuzzy Graphical Evaluation and Review Technique (GERT) simulation technology to simulate two-unit standby redundant system reliability. The approximate fuzzy arithmetic operations employ principle of interval arithmetic under the Tω arithmetic operations. Therefore, the novel fuzzy arithmetic operations may obtain fitter decision values, which have smaller fuzziness accumulating, under vague environment. In numerical examples the approximate fuzzy arithmetic operations has evidenced that it can successfully calculate results of fuzzy operations as interval arithmetic, and can more effectively reduce fuzzy spreads. In the real fuzzy repairable reliability model the performance also shows that the approximate fuzzy arithmetic operations successfully simulate/analyze the two-unit standby redundant system reliability and obtain more confident fuzzy results under vague environment.

Business cycle predictions face various sources of uncertainty and imprecision. The uncertainty is usually linguistically determined by the beliefs of decision makers. Thus, the fuzzy set theory is ideally suited to depict vague and uncertain features of business cycle predictions. Consequently, the estimation of fuzzy upper and lower bounds become an essential issue in predicting business cycles in an uncertain environment. The support vector regression (SVR) model is a novel forecasting approach that has been successfully used to solve time series problems. However, the SVR approach has not been widely applied in fuzzy forecasting problems. This study employs support vector regressions to calculate fuzzy upper and lower bounds; and presents a fuzzy support vector regression (FSVR) model for forecasting indices of business cycles. A numerical example of a business cycle prediction in Taiwan was used to demonstrate the forecasting performance of the FSVR model. The empirical results are satisfactory. Therefore, the FSVR model is an effective alternative in forecasting business cycles under uncertain circumstances.

This paper develops a new simulation method for vendor managed inventory (VMI) model based on fuzzy arithmetic in the supply chain (SC). The traditional VMI model has been successfully used to reduce the ''Bullwhip Effect'' in the SC. Thus, in real industry the VMI model can be observed that some variables/parameters may belong to the uncertain factors. Therefore, the traditional VMI model may need to be extended to treat the vague variables or parameters. This study develops a fuzzy system dynamic to simulate vendor managed inventory, automatic pipeline, inventory and order based production control system (VMI-APIOBPCS) model based on fuzzy difference equations, and these operators of difference equations adopt the weakest t-norm (T"W) operators. Based on the weakest t-norm operators we can get the approximate result using sup-min convolution for simulating fuzzy VMI model, and the fuzzy VMI model can be easier simulated under uncertain environment Moreover, the results of fuzzy VMI-APIOBPCS model can provide the whole extended information regarding the system behavior uncertainties for the decision-makers with fuzzy interval. Furthermore, the study uses genetic algorithms (GA) to search optimal parameters of fuzzy VMI-APIOBPCS model. The performance of Bullwhip measures shows that fuzzy VMI-APIOBPCS model also can reduce the ''Bullwhip Effect'' as crisp VMI model which can be evidenced by analysis of variance (ANOVA), and the performance of integral of timexabsolute error (ITAE) shows that the fuzzy VMI model outperforms previous method with fixing customer service level.

Several papers have presented measured function to handle multi-criteria fuzzy decision-making problems based on interval-valued intuitionistic fuzzy sets. However, in some cases, the proposed function cannot give sufficient information about alternatives. Consequently, in this paper, we will overcome previous insufficient problem and provide a novel accuracy function to measure the degree of the interval-valued intuitionistic fuzzy information. And a practical example has been provided to demonstrate our proposed approach. In addition, to make computing and ranking results easier and to increase the recruiting productivity, a computer-based interface system has been developed for decision makers to make decisions more efficiently.

This paper presents a straightforward and useful fuzzy regression approach to estimate heat tolerance of plants by chlorophyll fluorescence measurement. The chlorophyll fluorescence measurement is an indicator of functional change of photosynthesis and is sensitive to temperature. Using the fluorescence-temperature curves, the experimenter may determine the heat tolerance (Tc) of plants by intersections of two linear regression lines. However, as traditional statistical regression analysis shows, the experiment may contain uncertain factors or phenomena such as leaf nature and growth environment, which concludes that data may vary among individual plants and different species. This research presents a fuzzy bicluster regression (FBCR) analysis with genetic algorithms, which helps derive a fuzzy intersection set and fuzzy heat tolerance of plants, in addition to the traditional statistical regression analysis. A fuzzy clustering concept and simultaneously optimal determination of data clusters is also developed. Especially, when there are nonlinear inflections in data curves, due to the imperative use of linear regression models, the traditional regression analysis may become unable to sufficiently model the uncertainties exhibited. The FBCR analysis can resolve this problem effectively due to the nonlinear tolerance of the system, even in a linear model. To demonstrate the FBCR analysis, it was applied to estimate the heat tolerance of five plant species. The results derived appeared to be more suitable than that of the conventional method. The approach may provide a useful means for the experimenters to derive more credible results from their chlorophyll fluorescence-temperature data.

This paper presents a system dynamics analysis based on the application of fuzzy arithmetic. Traditional crisp system dynamics observe that some variables/parameters may belong to the uncertain factors. It is necessary to extend the system dynamics to treat also the vague variables/parameters. The evaluation of fuzzy system dynamics may provide the decision-maker information regarding the system's behavior uncertainties. In this paper, the customer–producer–employment model is examined with the fuzzy system dynamics in two types of fuzzy arithmetic, α-cut fuzzy arithmetic and Tω weakest t-norm operator. Symmetrical and nonsymmetrical triangular fuzzy number (TFN), varied amount of fuzzy inputs’ fuzziness, and length of the system time delay are examined with useful results provided. Particularly, it is revealed that (1) both types of fuzzy arithmetic can provide the steady-state analysis of the system's variables as their counterpart, the crisp arithmetic analysis. (2) The α-cut arithmetic realizes the fu...

This paper develops a new the weakest t-norm (T ω ) fuzzy Decision Making Trial and Evaluation Laboratory (DEMATEL) to evaluate green supply chain management (GSCM). Traditional DEMATEL is a popular method for making decision. In order to apply a realistic environment the fuzzy DEMATEL had to be developed for analyzing expert judgments with preferences, which are often unclear and ambiguous. The fuzzy DEMATEL usually adopts α-cut arithmetic operations to calculate results. However, general fuzzy DEMATEL model has not really handled fuzzy cause and effect relationships due to the accumulating phenomenon of fuzziness of the a-cut arithmetic. In the numerical example, using novel T ω fuzzy DEMATEL find out real cause and effect relationships with fuzzy bounds among criteria of GSCM. The T ω fuzzy DEMATEL can provide more credible information/results and analysis of across quadrants to evaluate cause and effected relationships, because the decision-maker usually wants to accurately grasp uncertain influential factors in the GSCM.

Long-term business cycle forecasting is a very important issue in economic evaluation. This study presents a novel intuitionistic fuzzy least-squares support vector regression (IFLS-SVR) model for accurately forecasting long-term index of business cycle. Traditional support vector regression (SVR) and least-squares support vector regressions (LS-SVR) have been successfully applied in forecasting problems especially complex/nonlinear system. In this paper, the prediction model adopts two least-squares support vector regressions with intuitionistic fuzzy sets to approach fuzzy upper and lower bounds respectively, and then approach the crisp predict values. Genetic algorithms (GAs) are also employed in order to select two parameters of IFLS-SVR models. In this study, IFLS-SVR, LS-SVR and SVR are employed for the Taiwan business index forecasting. Empirical results indicate that the IFLS-SVR has better performance in terms of forecasting accuracy. Therefore, the IFLS-SVR model can efficiently provide credible long-term prediction for the Taiwan business index forecasting

Fuzzy weighted average, as functions of fuzzy numbers, happens with the problem of multiple occurrences of fuzzy parameters (weights). Additional fuzziness may be introduced in the cut arithmetic. This paper based on an improved algorithm, called IFWA, applies orthogonal arrays method that is a fractional factorial experiment, to analysis weights adjustment. We will show a numerical example and demonstrate the usefulness of the orthogonal arrays method.

This paper develops a vendor managed inventory (VMI) dynamics based on the fuzzy arithmetic. The VMI has been used to reduce the "Bullwhip Effect" in supply chain. This research observes and analyzes the phenomena of VMI by system dynamics based on fuzzy arithmetic of the weakest t-norm (TW). The traditional crisp system dynamics has been widely used. However, it is observable that some variables/parameters may belong to the uncertain factors. Therefore, traditional crisp system dynamics may be necessary to be extended to treat the vague variables or parameters. In this paper the research examines the fuzzy arithmetic of TW in VMI and compares the performance of the traditional crisp system dynamics and system dynamics with fuzzy TW arithmetic. The system dynamics of fuzzy arithmetic can provide the extended information regarding the system behavior uncertainties for the decision-makers.

In this paper a novel way to construct pairwise alignment of protein sequence is proposed. Currently in protein sequence alignment the vital problem is having too many uncertain factors and causes significant data loss while using crisp data. For this important premise, fuzzy concept is introduced and fuzziness is implemented in the matrix for 250 point accepted mutations (PAMs) in sequence aligning, and integrated with the genetic algorithm (GA). The purpose for this implementation is to reduce the effects of uncertain factor, avoid making use of crisp values or weights resulting in significant data loss, and increase solution accuracy and method suitability. Experimental results of fuzzy matrix for 250 PAM can find more continuous and identical protein sequence after sequence alignment by GA.

In capacity-planning systems, various sources of uncertainty and imprecision are encountered. In most cases, the uncertainty is determined by the subjective beliefs of managers linguistically. However, the measurement of mangers’ judgments is difficult and vague. Therefore, a fuzzy logic-based approach is proposed to deal with capacity-planning problems in the presence of the uncertain demand, set-up resources, and the capacity constraints. Firstly, fuzzy numbers are used to represent uncertain data. Secondly, fuzzy if-then rules are employed to model vaguely defined relations between fuzzy numbers. Then, the computational aspects of fuzzy models and interpretations of inference results are illustrated by a numerical case. Finally, three examples are used to verify the proposed representation and inference mechanism.