Suppliers are one of the most vital parts of supply chain whose operation has significant indirect effect on customer satisfaction. Since customer's expectations from organization are different, organizations should consider different standards, respectively. There are many researches in this field using different standards and methods in recent years. The purpose of this study is to propose an approach for choosing a supplier in a food manufacturing company considering cost, quality, service, type of relationship and structure standards of the supplier organization. To evaluate supplier according to the above standards, the fuzzy inference system has been used. Input data of this system includes supplier's score in any standard that is achieved by AHP approach and the output is final score of each supplier. Finally, a supplier has been selected that although is not the best in price and quality, has achieved good score in all of the standards.

The Fuzzy Sets Theory has recently been widely and successfully used in engineering problems with complexity, ambiguity, or lack of enough data. The Fuzzy Inference System (PIS) is among these techniques. The main advantage of this technique over traditional methods is that it works based on IF-THEN rules and appoints the relation between input and output variables accordingly. In this study the monthly discharge, temperature, and rainfall are used in the Fuzzy Inference System context as continuous series in order to forecast the river flow discharge for the next months. The effect of each variable in previous time step is determined on the flow discharge in the upcoming month and the best combination and suitable lag time is obtained.

Milk ultrafiltration is a membrane process where it is subjected to a hydrodynamic pressure difference across a porous membrane that causes the process to be highly complex innature. In this paper, fuzzy inference systems have been used to model and simulate the crossflow ultrafiltration of milk in a dynamic manner. The primary aim of this work was to predict permeate flux, fouling and the milk components rejection (i.e., protein, fat, lactose, and total solids), using Mamdani and Takagi-Sugeno models. The results shows that the permeate flux and the fouling resistance vary with time for each variables. Furthermore, the protein rejection is almost constant for each parameter with time; however, the rejection of other components has been raised significantly with time. The findings of this work also revealed that by applying fuzzy inference systems we can predict the permeate flux and the total hydraulic resistance and compute the system errors. Furthermore, the comparison of the two models also demonstrates that the Mamdani model exhibits a better result than that of the Takagi-Sugeno model.

Synthetic aperture radar (SAR) imaging widely used for remote sensing and military applications; because this kind of imaging is independent from the weather and has high resolution in day and night. In this paper, a new method based on fuzzy logic is proposed for the edge extraction of SAR images. Edge detection is one of the most important techniques used for data analysis and decision-making on the image for various applications. Edge detection is a scope of research in image processing and feature extraction. A serious problem concerning SAR images is that they are intrinsically disturbed by speckle noise. The existence of speckle noise, intensely impedes the interpretation and analysis of SAR images. So, traditional techniques can not provide good edge detection results for SAR images. The method, proposed in this manuscript, by optimization of the fuzzy inference system (FIS) i.e using proper kernels and membership functions, the edge extraction of SAR image is done. The inputs for FIS are image gradients (in both horizontal and vertical directions) and a heuristic self-tuning edge detector parameter which is shown by. This self-tuning parameter is variable for different images and by controlling the shape of Gaussian membership function leads to automatization of proposed algorithm. Finally, the proposed algorithm is compared with other edge detectors to demonstrate the superiority of the proposed method. In this work, in addition to structural similarity criterion, the Mean squared error (MSE) and Peak signal-to-noise ratio (PSNR) are used to numerical Analysis.

One of the methods used to measure and evaluate employees is the assessment center. Assessment centers usually have good validity at the instrumental level, but have weaknesses at the summation level. This research seeks to provide a fuzzy system for estimating AC final scores based on competency, intelligence and personality scores by modeling mentality of experts in this field. For this purpose, the data of the assessment center and expert’, s opinion have collected and finally a model has been presented to combine these three categories of concepts. The developed model has three sets of trapezoidal fuzzy numbers and 27 rules. In order to validate this model, the data of 116 managers of the country and the simulation of Monte Carlo were used, and it is clear that competence, intelligence and personality are the most important parameters, respectively, in the final score of the evaluation center.