In this paper, a novel risk-based, two-objective (technical and economical) optimal reactive power dispatch method in a wind-integrated power system is proposed which is more consistent with operational criteria.  The technical objective includes the minimization of the new voltage instability risk index. The economical objective includes cost minimization of reactive power generation and active power loss. The proposed voltage instability risk employs a hybrid possibilistic (Delphi-Fuzzy)-probabilistic approach that takes into consideration the operator’s experience, the wind speed and demand forecast uncertainties when quantifying the risk index. The decision variables are the reactive power resources of the system. To solve the problem, the modified multi-objective particle swarm optimization algorithm with sine and cosine acceleration coefficients is utilized. The method is implemented on the modified IEEE 30-bus system. The proposed method is compared with those in the previously published literature, and the results confirm that the proposed risk index is better at estimating the voltage instability risk of the system, especially in cases with severe impact and low probability. In addition, according to the simulation results compared to typical security-based planning, the proposed risk-based planning may increase the security and economy of the system due to better utilization of system resources.

This paper aims to design a Fuzzy inference system to evaluate the green supply chain of export manufacturing companies. This research has been applied from the point of view of purpose. The statistical population of this study included export manufacturing companies in the northwest of the country. The statistical sample is targeted, and 143 companies are determined. A research questionnaire based on the research literature was used to collect the data. In order to examine the validity of the questionnaire, while using formal validity, the validity of the structure has been used based on confirmatory factor analysis. Cronbach's alpha coefficient was also used to evaluate the reliability of the questionnaire. The research questionnaires were distributed among the statistical sample members of the research after confirming the validity and reliability. In order to evaluate the green supply chain of companies, a Fuzzy inference system has been used based on triangular membership functions and Mamdani inference. The results show that the designed system is able to show how green the supply chain of companies is based on numerical values and linguistic terms.

The main purpose of this research is to identify the factors affecting the successful of implementing talent management in Knowledge-based companies. The present research is an applied research in terms of purpose, and in terms of the research design, it is a descriptive-survey research. Also, it is a cross-sectional, in term of time and in term of the nature of data is quantitative. In order to analyze the data and modeling, the Fuzzy inference system was used and the Fuzzy inference model was designed in MATLAB software. Statistical population of the study include experts that consisting of managers, senior executives and HR managers knowledge-based companies in Rasht Which their opinions were used to formulate Fuzzy inference rules and the construction of these rules was based on the opinions of 25 experts. The findings of this research indicate that three factors, such as employer brand, organizational culture and transformational leadership style, have been identified as the underlying and effective factors on the successful implementation of talent management in knowledge-based companies. based on the results of this study, among these factors, the transformational leadership style has the greatest impact on the successful implementation of talent management in knowledge based companies. Similarly, the employer brand and organizational culture were ranked accordingly.

In recent years, using Fuzzy sets theory in modeling of complex and uncertain hydrological phenomena has attracted research workers. For this reason, in this research for river flow forecasting, we have used models of FIS and ANFIS which are based on Fuzzy logic. Data of daily flow discharges were provided from Lighvanchay watershed for 6 years. For considering the randomness of data, return points test was used. Then correlogram of data was employed to determine the input optimum models and finally 5 models of discharge forecasting designed based on previous days' discharge. The results showed that ANFIS was more precise and less disperse (RMSE=0.0234) with compare to FIS (RMSE=0.1982). The ANFIS was also more precise in peak discharges simulation than FIS.

Introduction: Classification and prediction are two most important applications of statistical methods in the field of medicine. According to this note that the classical classification are provided due to the clinical symptom and do not involve the use of specialized information and knowledge. Therefore, using a classifier that can combine all this information, is necessary. The aim of this study was to design a decision support system for classification of thyroid disorder using Fuzzy if and then classifier. Materials and Methods: The data consisted of 310 patients, including 105 healthy people, 150 hypothyroidisms and 55 hyperthyroidisms, who referred to Shahid Beheshti Hospital and Imam Khomeini Clinic of Hamadan (Iran) in order to investigate the status of their thyroid disease. In this Fuzzy system variable including age and BMI, as well as laboratory tests such as TSH, T4, and T3, the score of hyperthyroid and hypothyroid symptoms used as input and the output variable includes individual health status. The max-min Mamdani inference system along with center of gravity deffizifier have been used in the Fuzzy toolbox of MATLAB software. Results: The Fuzzy rule-based classification model had a great performance for predicting thyroid disorder in the both test and train sets. Conclusion: Fuzzy rules-based classifier by using overlapping sets, had a high potential for managing the uncertainty associated with medical diagnosis. Also, by enabling the use of linguistic variables in the decision making process and design, the interpretation of the results has improved for doctors who are not familiar with modeling concepts.

Accommodating needs and supplies and presenting superior value to the customers are among the main goals of customer relationship management (CRM) and their effectiveness has a crucial role in gaining competitive advantage. Accordingly, the aim of this paper is presenting a model to evaluate the effectiveness of CRM through Fuzzy approach. The statistical population consists of the customers of Tabriz-Kar Machine Manufacturing Industry. To do this, the researcher designed a questionnaire according to Kim & Kim (2009) viewpoint, and its validity and reliability were measured and approved. To conduct the research, a five-stage model was designed based on Fuzzy logic. In the first phase, a Fuzzy system was designed with the four dimensions of effective CRM as its inputs and the CRM effectiveness score as its output. In the second phase, the inputs and outputs were converted to Fuzzy numbers after the classification. In the third phase, inference rules were explained, and in the fourth phase, defuzzification was carried out. In the final stage the model was tested and the results showed the validity of the model. Finally, by using the designed model, the effectiveness rate of CRM in Tabriz-Kar Machine Manufacturing Company was evaluated. The results show that the effectiveness of CRM in the company with the membership degree of 0.748 is on average rate.

Environmental pollution has become a green motivation to control the pollution increase in countries which its purpose is to reduce the negative effects of environmental pollution; hence, green chain supply management has an important role in the environmental impact of organizations. Therefore, the purpose of this study is to evaluate the green supply chain of small and medium manufacturing companies based on green productivity indicators. This research is based on practical purposes and quantitative research approaches. The statistical sample was designated by 297 small and medium manufacturing companies in East Azerbaijan province. In order to data collection, a researcher-made questionnaire based on the research literature has been used. The validity of the questionnaire was determined based on the validity of the structure and its reliability using Cronbach's alpha coefficient. To evaluate the green supply chain through green productivity indicators, a Fuzzy inference system based on triangular membership functions, Mamdani inference and dependency rules has been used. The results show that the designed inference system based on green productivity indicators to evaluate the green supply chain with 43 dependency rules is able to evaluate the greenness measure of the supply chain of companies based on numerical values and linguistic words

Objective: Applying the system dynamics approach in businesses requires specialized knowledge, in particular, of defining mathematical relationships among variables. This stud seeks to make the use of this approach easier by providing a method for using linguistic variables and the Fuzzy inference system in the systems dynamics approach. To evaluate the ease of use and efficiency of the presented method, this method would be used to define the relationship among variables in the purchasing department of a distribution company. Methods: To carry out this research, a literature review was first conducted in the field of Fuzzy logic and system dynamics. Next, with the cooperation of an expert from the purchasing department of the distribution company under study, some Fuzzy linguistic variables as well as their rules were determined. Finally, the SD model was obtained by using the Fuzzy inference system. Results: The proposed approach can reflect the business dynamics of the distribution company in accordance with what is happening in practice. According to the feedback model feedback and based on the modified linguistic variables, appropriate values were obtained for decision making. In order to evaluate the hybrid approach, a Fuzzy inference system was used to calculate the purchase rate according to the two factors of inventory and base sales. These two factors were expressed through linguistic variables by the words "low", "medium", and "high", while the purchase price, as the output of the inference system, was expressed through the five words "very low", "low", "medium", "much", and "too much", according to the expert. After implementing the model, the presented approach (by modifying the Fuzzy linguistic variables) was found capable of changing the output to achieve the desired results, as the expert confirmed. Conclusion: The combined approach can be used in simulating similar cases (where human factor perception and decision-making play a significant role) and can easily reduce the complexity of the required formulas in the system dynamics approach. An important function of the hybrid approach used in this study was to model and simulate the real world in accordance with what is happening in practice.

In this study a new type of Takagi-Sugeno-Kang (TSK) type Fuzzy system with dimension reduction section at the input stage called Semi-polynomial data Mapping Fuzzy inference system (SPMFIS) is proposed. In the proposed method a semi-polynomial feature map is used to transform the input variables to new extracted features with low dimensions. At the next step, these new features are used as the input vector of ANFIS structure. Also gradient descent algorithm is chosen for training parameters of ANFIS and SPM parts of the proposed method. In order to evaluate the capability of the proposed method, its applications in classification of some different benchmark data sets, system identification, and time series prediction have been studied. The results show that the proposed method performs better than the conventional models in classification, identification and time series prediction.