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Öğe Application of machine learning methods for pallet loading problem(MDPI-Multidisciplinary Digital Publishing Institute, 2021) Aylak, Batin Latif; İnce, Murat; Oral, Okan; Almasarwah, Najat; Singh, Manjeet; Salah, Bashir; Süer, GurselBecause of continuous competition in the corporate industrial sector, numerous companies are always looking for strategies to ensure timely product delivery to survive against their competitors. For this reason, logistics play a significant role in the warehousing, shipments, and transportation of the products. Therefore, the high utilization of resources can improve the profit margins and reduce unnecessary storage or shipping costs. One significant issue in shipments is the Pallet Loading Problem (PLP) which can generally be solved by seeking to maximize the total number of boxes to be loaded on a pallet. In many previous studies, various solutions for the PLP have been suggested in the context of logistics and shipment delivery systems. In this paper, a novel two-phase approach is presented by utilizing a number of Machine Learning (ML) models to tackle the PLP. The dataset utilized in this study was obtained from the DHL supply chain system. According to the training and testing of various ML models, our results show that a very high (>85%) Pallet Utilization Volume (PUV) was obtained, and an accuracy of >89% was determined to predict an accurate loading arrangement of boxes on a suitable pallet. Furthermore, a comprehensive analysis of all the results on the basis of a comparison of several ML models is provided in order to show the efficacy of the proposed methodology.Öğe Estimation of tree height with machine learning techniques in coppice-originated pure sessile oak (Quercus petraea (Matt.) Liebl.) stands(2023) Şahin, Abbas; Özdemir, Gafura Aylak; Oral, Okan; Aylak, Batin Latif; İnce, Murat; Özdemir, EmrahIn this study, in order to estimate total tree height, three di?erent model structures with di?erentinput variables were produced through the use of 872 tree data points obtained from di?erentdevelopment stages and sites in coppice-originated pure sessile oak (Quercus petraea [Matt.] Liebl.)stands. These models were ?tted with machine learning techniques such as arti?cial neuralnetworks (ANNs), decision trees, support vector machines, and random forests. In addition, themodel based on DBH was ?tted and its parameters were calculated using the ordinary nonlinearleast squares method and this model was selected as the best model in Model 1. In other modelstructures, ANN model was chosen as the best estimation method based on the relative rankingmethod in which the goodness of ?t statistics of the estimation methods were evaluated together.The inclusion of stand variables in addition to the DBH measurement in the model increased theR2 by about 36% and reduced the error rate by 55%.Öğe Estimation of wind speed probability distribution parameters by using four different metaheuristic algorithms(2022) Oral, Okan; İnce, Murat; Aylak, Batin Latif; Özdemir, Mehmet HakanThe inclusion of energy produced from renewable energy sources (RES) such as solar and wind energy into existing energy systems is important to reduce carbon emissions, air pollution and climate change, and to ensure sustainable development. However, the integration of RES into the energy system is quite difficult due to their highly uncertain and intermittent nature. In this study, considering three different probability density functions (PDFs) in total, the scale and shape parameters of the Weibull PDF, the scale parameter of the Rayleigh PDF, and the scale and shape parameters of the Gamma PDF were estimated for the wind speed data obtained from urban stations located in Istanbul by using the four different metaheuristic algorithms, namely Genetic Algorithm (GA), Differential Evolution (DE), Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) algorithms. Calculating the mean absolute error (MAE), root mean squared error (RMSE), and R2 values for each PDF at each station, the PDF that characterizes the wind speed probability distribution the best was identified.Öğe Installed solar power prediction for Turkey using artificial neural network and bidirectional long short - term memory(2020) Özdemir, Mehmet Hakan; İnce, Murat; Aylak, Batin Latif; Oral, Okan; Taş, Mehmet AliSürdürülebilir bir kalkınma için yenilenebilir enerji kaynakları önemli bir rol oynamakta ve yenilenebilir enerji kaynaklı enerji üretiminin payı tüm dünyada hızla artmaktadır. Ülkemiz, bulunduğu coğrafi konumu nedeniyle hem güneş hem de rüzgâr enerjisi açısından büyük bir potansiyele sahiptir. Bu potansiyeli kullanma konusunda henüz istenen düzeye ulaşılamamıştır. Yine de son yıllarda kurulu gücün artmasıyla birlikte güneş enerjisinden elektrik üretimi çalışmaları hız kazanmıştır. Bu çalışmada, Türkiye’nin 2009-2019 yılları arasındakikümülatif güneş enerjisi kurulu gücü verileri kullanılmıştır. Bu veriler ile2020 yılı için kümülatif kurulu gücü tahmin etmekamacıylaYapay Sinir Ağı (Artificial Neural Network - ANN) ve İki Yönlü Uzun-Kısa Vadeli Bellek (Bidirectional Long Short-Term Memory - BLSTM) yöntemleri kullanılmıştır. Kümülatif kurulu güç tahmin edilmiş ve sonuçlar karşılaştırılarak yorumlanmıştır.1.INTRODUCTIONThe energy needs of countries are increasing day by day. As a result of increasing consumption, fossil energy resources in the world are rapidly running out. Nevertheless,fossil energy resources still have a considerable share in primary energy consumption acrossthe world. Primary energy consumption by sourcesin 2018 and 2019is shown forthe entire worldin Figure 1 and Figure 2. As can be seen from the Figures, the primary energy consumption originating from fossil energyresourcesis over 80% in both years.Moreover, Turkey’s primary energy consumption bysources in 2018 and 2019 isshown in Table 1.Hydroelectric energy data are not given under renewable energy in the reference.Öğe Predicting world electricity generation by sources using different machine learning algorithms(2024) Özdemir, Mehmet Hakan; Aylak, Batin Latif; Oral, Okan; İnce, MuratElectrical energy plays a crucial role in both social and economic growth. It is thought to be an essential part of industrial manufacturing. In addition to its contribution to industry, electrical energy is essential for addressing the needs of people on a daily basis. Therefore, electricity generation prediction is crucial for accurate electricity planning and energy usage, with machine learning (ML) algorithms becoming popular for their ability to extract complex relationships and make precise predictions. With the data from the period 2000-2022, this study predicts world electricity generation for 2023 by different energy sources employing seven different ML algorithms, namely long short-term memory (LSTM), artificial neural network (ANN), linear regression (LR), support vector regression (SVR), decision tree regression (DTR), random forest regression (RFR) and eXtreme gradient boosting (XGBoost). The algorithms were also contrasted in the study, and it was discovered that LSTM produced the most accurate predictions. [Received: June 16, 2023; Accepted: August 19, 2023]Öğe Prediction of Turkey's electricity generation by sources using artifical neural network and bidirectional long short - term memory(2021) Aylak, Batin Latif; Özdemir, Mehmet Hakan; İnce, Murat; Oral, OkanIt is an indisputable fact that energy plays a big role in the development of countries. Electrical energy has a great share in the development. Electricity is a secondary energy source, i.e. it is obtained by transforming primary energy sources. Although the desired level has not yet been reached, Turkey’s installed power has increased by years and a wide variety of energy sources such as coal, oil, natural gas, hydroelectric energy, wind, solar and other renewable energy sources are used in electricity generation. At this point, it is observed that the share of renewable energy sources in total electricity generation has increased from year to year. It should be underlined that this increase is very important for the country’s economy. In this study, Turkey’s electricity generation by sources for the years 2020 and 2021 was predicted with artificial neural network (ANN) and bidirectional long short - term memory (BLSTM) methods using the data for electricity generation by sources in the years 2010-2019. The share of electricity generated from renewable energy sources in total electricity generation for 2020 by ANN and BLSTM methods was calculated as 18.08% and 18.6% respectively. For 2021, the share of electricity generated from renewable energy sources in total electricity generation was calculated as 21.95% and 21.68% respectively. These results show that the share of electricity generated from renewable energy sources in total electricity generation will increase. Finally, suggestions were made on what kind of roadmap should be followed in the field of investments in renewable energy resources.Öğe Yapay Zeka ve Makine Öğrenmesi Tekniklerinin Lojistik Sektöründe Kullanımı(2021) Aylak, Batin Latif; Oral, Okan; Yazıcı, KübraThe logistics sector in Turkey and the world is growing and the sector's potential is better understood over time. It is known that the logistics sector is very open to development and has to keep up with the innovations that occur with technology. Businesses are trying to be successful in competition by keeping up with these innovations. Industry 4.0 has influenced the sectors where competition is at the forefront, especially logistics. In recent studies, it has been observed that a significant increase in the use of artificial intelligence techniques. As a result of the use of artificial intelligence in the logistics sector, changes in operations and dynamics have started to occur. Artificial intelligence models the physiological and neurological structure of human intelligence with the help of various technologies and transfers them to machines. Options such as driverless vehicles emerging with artificial intelligence, robots used in storage and shelves, and the easy use of big data in the system ensure that the errors in the logistics sector are minimized and convenience is provided in this way. Thanks to the use of artificial intelligence in the logistics sector, businesses create more efficient jobs. In this study, it is aimed to examine the artificial intelligence and machine learning applications used in the logistics industry with a broad perspective. In the study, firstly, the concepts of artificial intelligence and machine learning are explained and then, the concepts of industry and logistics are mentioned, and the applications of artificial intelligence and machine learning used in logistics are included. It is seen that artificial intelligence improves day by day and facilitates logistics processes in global logistics and supply chain management.
