Browsing by Author "Mizrak Ozfirat P."
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Item A fuzzy event tree methodology modified to select and evaluate suppliers(South African Institute of Industrial Engineering, 2020) Mizrak Ozfirat P.The supplier selection problem becomes more urgent as competition in the market increases. Quality, cost, and the timely delivery of a product mostly depends on the manufacturer’s suppliers and the materials supplied. Therefore manufacturers are very elaborate in selecting their suppliers and work hard to develop supplier selection strategies. In this study, event tree analysis (ETA) is used to solve a manufacturing firm’s supplier selection problem. ETA is a method that is traditionally used for risk analysis problems, combining the probabilities of risk occurrences subject to the necessary precautions. In this study, this structure is used to select and evaluate suppliers. An event tree is developed to analyse each possible supplier, with branching being used according to the supplier selection criteria. The probability of each branch is set as the performance value of the supplier according to the selection criteria. Finally, the supplier is evaluated by combining all performance values on an event tree basis. Fuzzy logic is also incorporated into the event tree methodology to decrease human error and the effect of uncertainty. Fuzzy triangular numbers are used to denote the performance values of suppliers, and fuzzy ranking is used to distinguish the suppliers into classes. The proposed methodology is applied to nine possible suppliers of a specific material. The results reveal that two of the suppliers dominate all the others in the fuzzy ranking. © 2020, South African Institute of Industrial Engineering. All rights reserved.Item Truck selection with the fuzzy-wsm method in transportation systems of open pit mines(Strojarski Facultet, 2021) Malli T.; Mizrak Ozfirat P.; Yetkin M.E.; Ozfirat M.K.Open pit mines gain width and become more complicated as they are deeper today, and it is inevitable to carry the produced material with a truck transportation system. Therefore, in large-scale businesses, truck selection has great importance for the transportation costs to be sustainable. This study investigates the main factors and corresponding criteria influential in selection of trucks, which are the most frequent used means of transportation in open pit mines. Analytic hierarchy process and fuzzy weighted sum model are employed to solve the selection problem. Six different truck types and 20 selection criteria are considered. As a result of technical analysis, most suitable trucks are found. © 2021, Strojarski Facultet. All rights reserved.Item An Experimental Design Frame for Active Dam Reserve Ratio Forecasting Using Neural Networks(EDP Sciences, 2024) Mizrak Ozfirat P.; Ari D.Today, one of the important and frequently spoken problems of the world is global warming and climate change. Due to these subjects, water drought and scarcity may become a trouble in the future. To prevent these problems, scientific studies are being carried out, solutions are being recommended and preventive applications are developing. In this study, to examine and foresee the decrease in water resources, active dam reserve ratio is considered and estimated using artificial neural networks. Time series analysis is performed using the active dam reserve ratio of Guzelhisar Dam, located in city of Izmir, Turkiye. Active reserve ratio data between 2012 and 2023 are considered on monthly basis. Since the data set displays high seasonality, this cyclic effect is extracted out of the data to get non-seasonal series. Then, using non-linear autoregressive artificial neural network method, both original seasonal data and non-seasonal data is forecasted. Three parameters are considered for neural network models: Input neurons, middle layer neurons and backpropagation algorithm. Results are compared according to mean absolute percent error. In the result, values of parameters to give minimum error are presented. In addition, performances of backpropagation algorithms are compared. © The Authors, published by EDP Sciences, 2024.Item Comparison of artificial neural networks and regression analysis for airway passenger estimation(Elsevier Ltd, 2024) Ari D.; Mizrak Ozfirat P.With the increasing demand in operations, time is getting more important. In order to use time and energy more effectively, it is becoming more important for airline companies and airport managements to make strategic plans for the future. To make beneficial and correct strategic plans for airways, one of the factors that is needed to be considered is future passenger numbers. With more accurate passenger number forecasts, airport managements can act more efficiently and reduce time, energy consumption and hence would be able to reduce costs. In this study, airway passenger number estimation is handled. Three metropolitan cities’ airport passenger numbers are considered. Artificial neural networks and regression analysis are carried out to estimate passenger number. In addition, data are handled in two different ways. Firstly, ANN and regression analysis are applied using original data series. In the second step, seasonal decomposition is applied on the data series and both approaches are repeated for deseasonal series. In Artificial Neural Networks approach, an experimental design is developed considering training algorithms, number of input nodes and number of nodes in the hidden layer which make up 960 design points. In the results of these experiments, performance of ANN approach is tested for three input factors and high-performance design points are identified. Furthermore, for benchmarking purposes, regression analysis is carried out. Linear, logarithmic, power, exponential, and polynomial models are developed. Finally, results of ANN and regression approaches are compared in terms of mean absolute percent error, and it is found that ANN overperformed compared to regression analysis. © 2024 Elsevier Ltd