Nowadays companies measure suppliers on the basis of a variety of factors and criteria that affect the supplier's selection issue. This paper intended to identify the key effective criteria for selection of green suppliers through an efficient algorithm callediterative process mining or i-PM. Green data were collected first by reviewing the previous studies to identify various environmental criteria. Then, the suppliers were evaluated and ranked on the basis of those criteria. The score table derived for the green criteria was one of the inputs to the algorithm. Moreover, membership functions and minimum support values ​​were specified for each criterion as another input to the algorithm. The supplier ranking index was also obtained based on the score assigned to supplier's performance. Then, the hidden relationships between data were discovered and association rules were achieved and analyzed to identify the most important green criterion for selecting green suppliers.

http://www.qjie.ir/article_676303_9e8ee2ef2d56748c7a872d59a0a60fea.pdf

Identifying and Evaluating Effective Factors in Green Supplier Selection using Association Rules Analysis

The classification of fuzzy data is considered as the most challenging areas of data analysis and the complexity of the procedures has been obstacle to the development of new methods for fuzzy data analysis. However, there are significant advances in modeling systems in which fuzzy data are available in the field of mathematical programming. In order to exploit the results of the researches on fuzzy mathematical programming, in this study, a new fuzzy data classification method based on data envelopment analysis (DEA) is provided when fuzzy data are imported as a stream. The proposed method can classify data that changes are created in their behavioral pattern over time using updating the criteria of predicting fuzzy data class. To reduce computational time, fuzzy self-organizing map (SOM) is used to compress incoming data. The new method was tested by simulated data and the results indicated the feasibility of this technique in the face of uncertain and variable conditions.

http://www.qjie.ir/article_539596_cfb559d3a9ec791d9652298d323335b4.pdf

Classification of Streaming Fuzzy DEA Using Self-Organizing Map

Advancements in industry and increased energy demands have led to the construction of onshore and offshore platforms. The stability of these platforms is a major challenge for geotechnical engineers. Expandable anchors have recently been proposed to address this issue. Researchers have investigated the bearing capacity of anchors with different shapes and plate numbers using experimental and numerical methods. The use of a suitable method for numerical modeling of expandable anchors is necessary to determine their performance. The current study evaluated the bearing capacity of multi-plate anchors using the coupled Eulerian-Lagrangian, discrete element, and finite element methods and compares their results. The effect of an increase in the number of plates (to three), the distance between plates, the plate shape (square or circular), and the soil density (80% or 60%) on the bearing capacity of the anchors was determined. The results showed that the Eulerian model was in very good agreement with the experimental results and that the square anchor exhibited a higher bearing capacity than the circular anchor. An equation is proposed for the calculation of the bearing capacity of expandable anchors. 

Amine Tohidi

Papers

Classification of Streaming Fuzzy DEA Using Self-Organizing Map

Comparing pull-out capacity of expandable anchors using discrete/coupled Eulerian element methods versus finite element technique