Serkan Balli

Development of a fuzzy decision support framework for complex multi-attribute decision problems: A case study for the selection of skilful basketball players

The selection of skilful players is a complicated process due to the problem criteria consisting of both qualitative and quantitative attributes as well as vague linguistic terms. This study seeks to develop a decision support framework for the selection of candidates eligible to become basketball players through the use of a fuzzy multi-attribute decision making MADM algorithm. The proposed model is based on fuzzy analytic hierarchy process FAHP and Technique for Order Preference by Similarity to Ideal Solution TOPSIS methods. The model was employed in the Youth and Sports Center of Mugla, Turkey, with the participation of seven junior basketball players aged between 7 and 14. In the present study, physical fitness measurement values and observation values of technical skills were utilized. FAHP was used to determine the weights of the criteria and the observation values of technical skills by decision makers. Physical fitness measurement values were converted to fuzzy values by using a fuzzy set approach. Subsequently, the overall ranking of the candidate players was determined by the TOPSIS method. Results were compared with human experts opinions. It is observed that the developed model is more reliable to be used in decision making. The model architecture and experimental results along with illustrative examples are further demonstrated in the study.

A Fuzzy Multi-Criteria Decision Analysis Approach for the Evaluation of the Network Service Providers in Turkey

AbstractHeterogeneous networks are environments where networks having different topologies and technologies can be connected. In an environment including more than one heterogeneous access network, selection of a bad network may lead to emergence of negative results such as high cost and poor service experience for the users. Ensuring the use of the most effective access network for the personal needs of individuals is a complex decision-making process. In the present study, a multi-criteria decision-making system employing fuzzy logic was developed to evaluate and select network service providers in Turkey. Fuzzy logic was used for the criteria containing uncertain and unclear information. Parameter values of the candidate networks obtained from the real world were evaluated by using Fuzzy Analytic Hierarchy Process method and then results were discussed.

Application of a fuzzy expert system for failure load estimation of two serial pinned or bolted sandwich composite plates

Abstract The present paper reports about the application of fuzzy expert system for estimating failure loads of two serial pinned/bolted sandwich composite plates. Considered composite material for present application was produced by a glass fiber reinforced layer and aluminum sheets to create sandwich structures. Briefly outlines, the experimental data of a previous study were related to different geometrical boundary conditions and also numerous preload moments as 0 (pinned), 2, 3, 4 and 5 (bolted) Nm. Anyway, both a fuzzy expert system and a regression analysis were applied depending on mentioned geometrical parameters and pinned/bolted joint arrangements in this work. Obtained results point out that the fuzzy expert system was more suitable than regression analysis method for modeling and estimation of analyzed sandwich composite. Achievements of the fuzzy expert system and the regression analysis methods were considered in terms of error ratios and mean absolute deviations.

A data mining approach to the diagnosis of failure modes for two serial fastened sandwich composite plates

The aim of this study is to diagnose and classify the failure modes for two serial fastened sandwich composite plates using data mining techniques. The composite material used in the study was manufactured using glass fiber reinforced layer and aluminum sheets. Obtained results of previous experimental study for sandwich composite plates, which were mechanically fastened with two serial pins or bolts were used for classification of failure modes. Furthermore, experimental data from previous study consists of different geometrical parameters for various applied preload moments as 0 (pinned), 2, 3, 4, and 5 Nm (bolted). In this study, data mining methods were applied by using these geometrical parameters and pinned/bolted joint configurations. Therefore, three geometrical parameters and 100 test data were used for classification by utilizing support vector machine, Naive Bayes, K-Nearest Neighbors, Logistic Regression, and Random Forest methods. According to experiments, Random Forest method achieved better results than others and it was appropriate for diagnosing and classification of the failure modes. Performances of all data mining methods used were discussed in terms of accuracy and error ratios.