Endüstri Mühendisliği Bölümü Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/204

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Admissible invariants of genus 3 curves
(Springer New York LLC, 2015) Cinkir, Zubeyir; AGÜ, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümü; Çinkir, Zübeyir
Several invariants of polarized metrized graphs and their applications in Arithmetic Geometry are studied recently. In this paper, we explicitly calculated these admissible invariants for all curves of genus 3. We find the sharp lower bound for the invariants φ, λ and ε for all polarized metrized graphs of genus 3. This improves the lower bound given for Effective Bogomolov Conjecture for such curves.
Analysis of the in vitro nanoparticle-cell interactions via a smoothing-splines mixed-effects model
(TAYLOR & FRANCIS LTD, 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND, 2016) Dogruoz, Elifnur; Dayanik, Savas; Budak, Gurer; Sabuncuoglu, Ihsan; AGÜ, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümü;
A mixed-effects statistical model has been developed to understand the nanoparticle (NP)-cell interactions and predict the rate of cellular uptake of NPs. NP-cell interactions are crucial for targeted drug delivery systems, cell-level diagnosis, and cancer treatment. The cellular uptake of NPs depends on the size, charge, chemical structure, and concentration of NPs, and the incubation time. The vast number of combinations of these variable values disallows a comprehensive experimental study of NP-cell interactions. A mathematical model can, however, generalize the findings from a limited number of carefully designed experiments and can be used for the simulation of NP uptake rates, to design, plan, and compare alternative treatment options. We propose a mathematical model based on the data obtained from in vitro interactions of NP-healthy cells, through experiments conducted at the Nanomedicine and Advanced Technologies Research Center in Turkey. The proposed model predicts the cellular uptake rate of silica, polymethyl methacrylate, and polylactic acid NPs, given the incubation time, size, charge and concentration of NPs. This study implements the mixed-model methodology in the field of nanomedicine for the first time, and is the first mathematical model that predicts the rate of cellular uptake of NPs based on sound statistical principles. Our model provides a cost-effective tool for researchers developing targeted drug delivery systems.
Analysis of under-five mortality by diseases in countries with different levels of development: a comparative analysis
(Prusa Medikal Yayıncılık, 2023) Sütçü, Muhammed; Güner, Pınar; Ersöz, Nur Şebnem; 0000-0002-8523-9103; 0000-0001-5979-0375; 0000-0003-3343-9936; AGÜ, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümü; Sütçü, Muhammed; Güner, Pınar; Ersöz, Nur Şebnem
Objectives: The right to health is critical for children because they are sensitive beings who are more susceptible to disease and health problems. It would be beneficial to compare child mortality rates in countries with different levels of development and to conduct studies to address them by taking into account their causes. This study aims to analyze the situation of developed, developing and least developed countries in terms of causes under-5 child mortality (U5CM) determined by World Health Organization and to identify the similarities or differences of under-five mortality. Methods: Child mortality rates per 1,000 live births between 2000 and 2017 years in between different age groups (0-27 days and 1-59 months) by causes (disease-specific) were obtained from World Health Organization for a total 15 countries including developed, developing and least developed countries. Regression analysis was performed to identify which causes have more impact on child mortality. In addition, the relationship between diseases was calculated using Euclidean distance, and diseases were clustered using k-means clustering algorithm for each country. Results: As a result of mathematical and statistical analysis, it was seen that causes of child mortality have a significant relation with the development level of country where a child was born. Conclusions: It has been observed that the causes of child mortality in countries with different levels of development vary depending on different factors such as geographical conditions, air quality population and access to medicine.
An ant colony optimisation algorithm for balancing two-sided U-type assembly lines with sequence-dependent set-up times
(SPRINGER INDIA, 7TH FLOOR, VIJAYA BUILDING, 17, BARAKHAMBA ROAD, NEW DELHI, 110 001, INDIA, 2018) Delice, Yilmaz; Aydogan, Emel Kizilkaya; Soylemez, Ismet; Ozcan, Ugur; 0000-0002-4654-0526; 0000-0002-8253-9389; AGÜ, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümü
Some practical arrangements in assembly lines necessitate set-up times between consecutive tasks. To create more realistic models of operations, set-up times must be considered. In this study, a sequence-dependent set-up times approach for two-sided u-type assembly line (TUAL) structures is proposed for the first time. Previous studies on TUAL have not included set-up times in their analyses. Furthermore, an algorithm based on the Ant Colony Optimization (ACO) algorithm, which is using a heuristic priority rule based procedure has been proposed in order to solve this new approach. In this paper, we look at the sequence-dependent set-up times between consecutive tasks and consecutive cycles, called the "forward set-up time'' and the "backward set-up time'', respectively. Additionally, we examine the "crossover set-up time'', which arises from a new sequence of tasks in a crossover station. In order to model more realistic assembly line configurations, it is necessary to include sequence-dependent set-up times when computing all of the operational times such as task starting times and finishing times as well as the total workstation time. In this study, the proposed approach aims to minimize the number of mated-stations as the primary objective and to minimize the number of total workstations as a secondary objective. In order to evaluate the efficiency of the proposed algorithm, a computational study is performed. As can be seen from the experimental results the proposed approach finds promising results for all literature-test problems.
Artificial Neural Network Modeling and Simulation of In-Vitro Nanoparticle-Cell Interactions
(AMER SCIENTIFIC PUBLISHERS, 26650 THE OLD RD, STE 208, VALENCIA, CA 91381-0751 USA, 2014) Cenk, Neslihan; Budak, Gurer; Dayanik, Savas; Sabuncuoglu, Ihsan; AGÜ, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümü;
In this research a prediction model for the cellular uptake efficiency of nanoparticles (NPs), which is the rate that NPs adhere to a cell surface or enter a cell, is investigated via an artificial neural network (ANN) method. An appropriate mathematical model for the prediction of the cellular uptake rate of NPs will significantly reduce the number of time-consuming experiments to determine which of the thousands of possible variables have an impact on NP uptake rate. Moreover, this study constitutes a basis for targeted drug delivery and cell-level detection, treatment and diagnosis of existing pathologies through simulating NP-cell interactions. Accordingly, this study will accelerate nanomedicine research. Our research focuses on building a proper ANN model based on a multilayered feed-forward back-propagation algorithm that depends on NP type, size, surface charge, concentration and time for prediction of cellular uptake efficiency. The NP types for in-vitro NP-healthy cell interaction analysis are polymethyl methacrylate (PMMA), silica and polylactic acid (PLA), all of whose shapes are spheres. The proposed ANN model has been developed on MATLAB Programming Language by optimizing a number of hidden layers (HLs), node numbers and training functions. The datasets are obtained from in-vitro NP-cell interaction experiments conducted by Nanomedicine and Advanced Technology Research Center. The dispersion characteristics and cell interactions with different NPs in organisms are explored using an optimal ANN prediction model. Simulating the possible interactions of targeted NPs with cells via an ANN model will be faster and cheaper compared to the excessive experimentation currently necessary.
Association Rules on Traffic Accident: Case Of Ankara
(EGE UNIV, FAC ECONOMICS & ADMIN SCIENCESDEPT BUSINESS ADMIN, BORNOVA, 35100, TURKEY, 2016) Soylemez, Ismet; Dogan, Ahmet; Ozcan, Ugur; AGÜ, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümü; Soylemez, Ismet
In this study, association rules analysis of the data mining techniques are used for data of traffic accidents in 2010 and some rules are obtained. With this rules, what is the possibility of accident which resulted anybody injured for "different weather conditions (snowy, rainy etc.)", "where the accidents occurred (street, road etc.)" and "way situations (separated road or not)". Different algorithms are used to analyze the association rules. Apriori algorithm is selected for this study and SPSS Clementine 12.0 is used for this algorithm. Firstly, frequency of items are found. Then, items are grouped. In this study, data preprocessing is done and missing values are filled or rejected. In the second phase, outliers are rejected and data type is converted type of 1-0 (binary). In the third phase, Apriori algorithm is applied and results are evaluated.
Çok işçili montaj hatlarında istasyon ve kaynak yatırımı maliyetinin enküçüklenmesine yönelik tavlama benzetimi ve tam sayılı doğrusal programlamaya dayalı yeni bir algoritma
(Dicle Üniversitesi, 2018) Şahin, Murat; Kellegöz, Talip; Söylemez, İsmet; 0000-0002-8253-9389; AGÜ, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümü; Söylemez, İsmet
Standardize edilmiş ürünlerin üretiminde yaygın olarak kullanılan montaj hatları önemli bir akış tipi üretim şeklidir. Buhatların dengelenme problemleri üretim ve kaynak yatırımı maliyetlerinin enküçüklenmesi açısından büyük önem arzetmektedir. NP-zor bir yapıya sahip olan probleme endüstriyel ortamlarda yaygın olarak karşılaşılmasına karşın makulsüreler içerisinde kesin çözüm yöntemleri ile çözümü mümkün olmayabilmektedir. Bu çalışmada yenilenebilir kaynakyatırımı maliyetini de dikkate alan çok işçili montaj hattı dengeleme problemine yönelik yeni bir algoritma sunulmuştur.Önerilen algoritmada tamsayılı doğrusal programlama ile çözülecek olan alt problemler tavlama benzetimi yöntemi ilebelirlenmiştir. Literatürde montaj hattı dengeleme problemlerinde sıklıkla tercih edilen rassal sayılar dizisi kullanılarakgörevlerin hangi önceliklerle atanacağı belirlenmiştir. Tavlama benzetimi ve tamsayılı doğrusal programlamanın birliktekullanımına dayanan algoritmanın etkinliği test problemleri üzerinde ölçülmüştür. Tavlama benzetimi sezgiseli C#programlama dilinde kodlanmış ve oluşturulan her bir alt problemin tamsayılı doğrusal programlama modeli CPLEX10.2 çözücü kullanılarak 3.2 GHZ /4 GB Ram’a sahip bilgisayarda koşturulmuştur. Tavlama benzetiminde aynı altproblemler oluşturulması durumunda hafızada kaydedilen çözüm ve atamalar kullanmıştır. Bunun temel nedenimatematiksel model ile çözülen alt problemlerde aynı modelin oluşturulması durumunda elde edilecek sonuçlara dahaönceden ulaşılmış olmasıdır. Bu sayede algoritmanın daha hızlı bir şekilde çalışması gerçekleştirilmiş olup, çözülen vetekrarlanan matematiksel model sayıları özetlenerek sunulmuştur. Geliştirilen algoritmanın orta ve büyük boyutluproblem örneklerinde kabul edilebilir kalitede çözümler üretebildiği gözlemlenmiştir. Montaj hattı problemlerine ilişkinliteratürde bulunan çalışmalar dikkate alındığında, ilgili problem üzerindeki çalışmaların eksikliğine vurgu yapılmıştır.
Computation of polarized metrized graph invariants by using discrete laplacian matrix
(American Mathematical Society, 2015) Çinkir. Zübeyir; AGÜ, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümü; Çinkir, Zübeyir
Several invariants of polarized metrized graphs and their applications in Arithmetic Geometry have been studied recently. In this paper, we give fast algorithms to compute these invariants by expressing them in terms of the discrete Laplacian matrix and its pseudo inverse. The algorithm we give can be used for both symbolic and numerical computations. We present various examples to illustrate the implementation of these algorithms.
Dynamic rolling horizon control approach for a university campus
(Elsevier Ltd, 2022) Yoldas, Yeliz; Goren, Selcuk; Onen, Ahmet; Ustun, Taha Selim; 0000-0002-5320-4213; 0000-0001-7086-5112; 0000-0002-2413-8421; AGÜ, Mühendislik Fakültesi, Elektrik - Elektronik Mühendisliği Bölümü; Yoldaş, Yeliz; Gören, Selçuk; Önen, Ahmet
An energy management system based on the rolling horizon control approach has been proposed for the grid-connected dynamic and stochastic microgrid of a university campus in Malta. The aims of the study are to minimize the fuel cost of the diesel generator, minimize the cost of power transfer between the main grid and the micro grid, and minimize the cost of deterioration of the battery to be able to provide optimum economic operation. Since uncertainty in renewable energy sources and load is inevitable, rolling horizon control in the stochastic framework is used to manage uncertainties in the energy management system problem. Both the deterministic and stochastic processes were studied to approve the effectiveness of the algorithm. Also, the results are compared with the Myopic and Mixed Integer Linear Programming algorithms. The results show that the life span of the battery and the associated economic savings are correlated with the SOC values.
Electricity Load Forecasting Using Deep Learning and Novel Hybrid Models
(Sakarya University, 2022) Sütçü, Muhammed; Şahin, Kübra Nur; Koloğlu, Yunus; Çelikel, Mevlüt Emirhan; Gülbahar, İbrahim Tümay; 0000-0002-8523-9103; 0000-0001-9786-6270; 0000-0001-6198-569X; 0000-0001-9264-4345; 0000-0001-9192-0782; AGÜ, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümü; Sütçü, Muhammed; Şahin, Kübra Nur; Koloğlu, Yunus; Çelikel, Mevlüt Emirhan; Gülbahar, İbrahim Tümay
Load forecasting is an essential task which is executed by electricity retail companies. By predicting the demand accurately, companies can prevent waste of resources and blackouts. Load forecasting directly affect the financial of the company and the stability of the Turkish Electricity Market. This study is conducted with an electricity retail company, and main focus of the study is to build accurate models for load. Datasets with novel features are preprocessed, then deep learning models are built in order to achieve high accuracy for these problems. Furthermore, a novel method for solving regression problems with classification approach (discretization) is developed for this study. In order to obtain more robust model, an ensemble model is developed and the success of individual models are evaluated in comparison to each other
Families of Metrized Graphs with Small Tau Constants
(SPRINGER BASEL AGPICASSOPLATZ 4, BASEL 4052, SWITZERLAND, 2016) Cinkir, Zubeyir; AGÜ, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümü; Cinkir, Zubeyir
Baker and Rumely's tau lower bound conjecture claims that if the tau constant of a metrized graph is divided by its total length, this ratio must be bounded below by a positive constant for all metrized graphs. We construct several families of metrized graphs having small tau constants. In addition to numerical computations, we prove that the tau constants of the metrized graphs in one of these families, the hexagonal nets around a torus, asymptotically approach to 108 which is our conjectural lower bound.
Forecasting of the Unemployment Rate in Turkey: Comparison of the Machine Learning Models
(MDPI, 2024) Güler, Mehmet; Kabakçı, Ayşıl; Koç, Ömer; Eraslan, Ersin; Derin, K. Hakan; Güler, Mustafa; Ünlü, Ramazan; Türkan, Yusuf Sait; Namlı, Ersin; 0000-0002-1201-195X; AGÜ, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümü; Ünlü, Ramazan
Unemployment is the most important problem that countries need to solve in their economic development plans. The uncontrolled growth and unpredictability of unemployment are some of the biggest obstacles to economic development. Considering the benefits of technology to human life, the use of artificial intelligence is extremely important for a stable economic policy. This study aims to use machine learning methods to forecast unemployment rates in Turkey on a monthly basis. For this purpose, two different models are created. In the first model, monthly unemployment data obtained from TURKSTAT for the period between 2005 and 2023 are trained with Artificial Neural Networks (ANN) and Support Vector Machine (SVM) algorithms. The second model, which includes additional economic parameters such as inflation, exchange rate, and labor force data, is modeled with the XGBoost algorithm in addition to ANN and SVM models. The forecasting performance of both models is evaluated using various performance metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). The findings of the study show how successful artificial intelligence methods are in forecasting economic developments and that these methods can be used in macroeconomic studies. They also highlight the effects of economic parameters such as exchange rates, inflation, and labor force on unemployment and reveal the potential of these methods to support economic decisions. As a result, this study shows that modeling and forecasting different parameter values during periods of economic uncertainty are possible with artificial intelligence technology.
Karadeniz Bölgesi’nde Kurak ve Nemli Dönemlerin SPI Yöntemi Kullanılarak Belirlenmesi
(Artvin Çoruh Üniversitesi/Artvin Çoruh University, 2024) Öztürk, Yasemin Deniz; Ünlü, Ramazan; 0000-0002-1201-195X; AGÜ, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümü; Ünlü, Ramazan
Karadeniz bölgesi Türkiye’nin en çok yağış alan bölgesidir. Ancak Karadeniz Bölgesi’nde yağışlar hem yıllar arasında hem de bölge içerisinde önemli farklılıklara sahiptir. Bu durum bölgede kuraklıkların yaşanabilmesine ve kurak-nemli dönemlerin birbirini takip etmesine neden olmaktadır. Bu çalışmada yıllık ve 12 aylık SPI değerlerine göre Karadeniz bölgesinde yaşanan kurak ve nemli dönemlerin belirlenmesi amaçlanmıştır. Bölge genelinden seçilen 26 istasyonun 1960-2020 yılları arasındaki ortalama yağış verilerine göre standardize yağış indeksi (SPI) değerleri hesaplanmıştır. Tespit edilen kurak ve nemli dönemlerin eğilimleri MannKendall trend analizi kullanılarak tespit edilmiştir. Ayrıca ısı haritası kullanılarak Karadeniz Bölgesi kıyı ve iç kesimleri olarak ayrılıp kurak ve nemli dönemleri saptanmıştır. Analiz sonuçlarına göre 1966, 1969, 1974-1977, 1984-1986, 1993-1994, 2006-2007 ve 2019- 2020 yıllarının normalden daha az yağış aldığı ve birçok istasyonun kuraklığı şiddetli şekilde olduğu saptanmıştır. 1967, 1988, 1996- 1997, 1999, 2009 ve 2016 yıllarının ise normalden fazla yağış aldığını yani nemli karakterde olduğunu göstermektedir. Mann-Kendall trend analiz sonuçlarına göre Batı Karadeniz Bölgesinin kıyı kesimlerinde azalma eğilimde olduğu saptanmamıştır. Fakat azalışta anlamlılık bulunamamıştır. Orta ve Doğu Karadeniz bölgesinde ise artış eğilimi göstermekle birlikte bu eğilim bazı istasyonlarda anlamlı bulunmuştur. Bölgenin yer şekilleri dolayısıyla genel bir kurak ve genel bir nemli dönem olmadığı, doğu-batı doğrultusu ve kıyı-iç kesimlerde kurak ve nemli dönemlerin farklılık gösterdiği saptanmıştır.
Movie Recommendation Systems Based on Collaborative Filtering: A Case Study on Netflix
(Erciyes Üniversitesi, 2021) Sütçü, Muhammed; Erdem, Oğuzkan; Kaya, Ecem; 0000-0002-4634-7638; 0000-0002-8547-7929; 0000-0002-8523-9103; AGÜ, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümü; Sütçü, Muhammed; Erdem, Oğuzkan
User ratings on items like movies, songs, and shopping products are used by Recommendation Systems (RS) to predict user preferences for items that have not been rated. RS has been utilized to give suggestions to users in various domains and one of the applications of RS is movie recommendation. In this domain, three general algorithms are applied; Collaborative Filtering that provides prediction based on similarities among users, Content-Based Filtering that is fed from the relation between item-user pairs and Hybrid Filtering one which combines these two algorithms. In this paper, we discuss which methods are more efficient in movie recommendation in the framework of Collaborative Filtering. In our analysis, we use Netflix Prize dataset and compare well-known Collaborative Filtering methods which are Singular Value Decomposition, Singular Value Decomposition++, KNearest Neighbour and Co-Clustering. The error of each method is calculated by using Root Mean Square Error (RMSE). Finally, we conclude that K-Nearest Neighbour method is more successful in our dataset.
A Multi-Objective Mathematical Programming Model for Transit Network Design and Frequency Setting Problem
(MDPI, 2023) Benli, Abdulkerim; Akgün, İbrahim; 0000-0003-2550-7679; 0000-0001-6325-7741; AGÜ, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümü; Benli, Abdulkerim; Akgün, İbrahim
In this study, we propose a novel multi-objective nonlinear mixed-integer mathematical programming model for the transit network design and frequency setting problem that aims at designing the routes and determining the frequencies of the routes to satisfy passenger demand in a transit network. The proposed model incorporates the features of real-life transit network systems and reflects the views of both passengers and the transit agency by considering the in-vehicle travel time, transfers, waiting times at the boarding and transfer stops, overcrowding and under-utilization of vehicles, and vehicle fleet size. Unlike previous studies that simplify several aspects of the transit network design and frequency setting problem, the proposed model is the first to determine routes and their frequencies simultaneously from scratch, i.e., without using line and frequency pools while considering the aforementioned issues, such as transfers and waiting. We solve the proposed model using Gurobi. We provide the results of what-if analyses conducted using a real-world public bus transport network in the city of Kayseri in Türkiye. We also present the results of computational tests implemented to validate and verify the model using Mandl benchmark instances from the literature. The results indicate that the model produces better solutions than the state-of-the-art algorithms in the literature and that the model can be used by public transit planners as a decision aid.
A NEW RATIONAL CLASSIFICATION APPROACH BY THE NEW MIXED DATA BINARIZATION METHOD
(Süleyman Demirel Üniversitesi, 2023) Sütçü, Muhammed; Gülbahar, İbrahim Tümay; 0000-0001-9192-0782; AGÜ, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümü; Sütçü, Muhammed; Gülbahar, İbrahim Tümay
Classification algorithm is a supervised learning technique that is used to identify the category of new observations. However, in some cases, quantitative and qualitative data must be used together. With this approach, we tried to overcome the problems encountered in using quantitative and qualitative data together. In this paper, we model a new classification technique by converting all types of data to binary data because in the real world, data are classified in different types such as binary, numeric, or categorical. By this way, we develop a more accurate and efficient mixed data binarization approach for multi-attribute data classification problems. First, we determine the classes from available dataset and then we classify the new instances into these predetermined classes by using the new proposed data binarization approach. We show how each step of this algorithm could be performed efficiently with a numeric example. Then, we apply the proposed approach on a well-known iris dataset and our model show promising results and improvements over previous approaches.
A novel integration of MCDM methods and Bayesian networks: the case of incomplete expert knowledge
(SPRINGER, 2022) Kaya, Rukiye; Salhi, Said; Spiegler, Virginia; AGÜ, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümü; Kaya, Rukiye
In this study, we propose an effective integration of multi criteria decision making methods and Bayesian networks (BN) that incorporates expert knowledge. The novelty of this approach is that it provides decision support in case the experts have partial knowledge.We use decisionmaking trial and evaluation laboratory (DEMATEL) to elicit the causal graph of the BN based on the causal knowledge of the experts. BN provides the evaluation of alternatives based on the decision criteria which make up the initial decision matrix of the technique for order of preference by similarity to the ideal solution (TOPSIS). We then parameterize BN using Ranked Nodes which allows the experts to submit their knowledge with linguistic expressions. We propose the analytical hierarchy process to determine the weights of the decision criteria and TOPSIS to rank the alternatives. A supplier selection case study is conducted to illustrate the effectiveness of the proposed approach. Two evaluation measures, namely, the number of mismatches and the distance due to the mismatch are developed to assess the performance of the proposed approach. A scenario analysis with 5% to 20% of missing values with an increment of 5% is conducted to demonstrate that our approach remains robust as the level of missing values increases.
Optimal location determination of electric vehicle charging stations: A case study on Turkey's most preferred highway
(Bitlis Eren Üniversitesi, 2022) Sütçü, Muhammed; Gülbahar, İbrahim Tümay; 0000-0002-8523-9103; 0000-0001-9192-0782; AGÜ, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümü; Sütçü, Muhammed; Gülbahar, İbrahim Tümay
Today, electric vehicles are seen as one of the most suitable and environmentally friendly alternatives to internal combustion engine vehicles. An important issue related to the dissemination of electric vehicles is the location of the vehicle charging network and specifically the optimum location selection of the charging stations. Generally, most of the studies focus on popular destinations such as city centers, shopping areas, bus stations, and airports. Although these places are often used in normal life, they can usually provide an adequate solution for daily charging needs due to the number of alternative charging stations. However, finding adequate charging stations is not possible in intercity travels especially in highways. In this paper, we proposed a decision model to determine the location of electric car charging stations in highways. We create an optimization model to decide the optimum locations for the charging stations that can meet the customer demands on the Istanbul-Ankara highway. The proposed model determines optimum charging stations that enable passengers traveling with their electric vehicles to travel in Istanbul-Ankara highway in the shortest time.
Optimizing Electric Vehicle Charging Station Location on Highways: A Decision Model for Meeting Intercity Travel Demand
(MDPI, 2023) Gulbahar, Ibrahim Tumay; Sutcu, Muhammed; Almomany, Abedalmuhdi; Ibrahim, Babul Salam K. S. M. Kader; 0000-0001-9192-0782; 0000-0002-8523-9103; AGÜ, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümü; Gulbahar, Ibrahim Tumay; Sutcu, Muhammed
Electric vehicles have emerged as one of the top environmentally friendly alternatives to traditional internal combustion engine vehicles. The development of a comprehensive charging infrastructure, particularly determining the optimal locations for charging stations, is essential for the widespread adoption of electric vehicles. Most research on this subject focuses on popular areas such as city centers, shopping centers, and airports. With numerous charging stations available, these locations typically satisfy daily charging needs in routine life. However, the availability of charging stations for intercity travel, particularly on highways, remains insufficient. In this study, a decision model has been proposed to determine the optimal placement of electric vehicle charging stations along highways. To ensure a practical approach to the location of charging stations, the projected number of electric vehicles in Türkiye over the next few years is estimated by using a novel approach and the outcomes are used as crucial input in the facility location model. An optimization technique is employed to identify the ideal locations for charging stations on national highways to meet customer demand. The proposed model selects the most appropriate locations for charging stations and the required number of chargers to be installed, ensuring that electric vehicle drivers on highways do not encounter charging problems.
Probabilistic assessment of wind power plant energy potential through a copula-deep learning approach in decision trees
(CELL PRESS, 2024) Şahin, Kübra Nur; Sutcu, Muhammed; 0000-0001-9786-6270; AGÜ, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümü; Şahin, Kübra Nur
In the face of environmental degradation and diminished energy resources, there is an urgent need for clean, affordable, and sustainable energy solutions, which highlights the importance of wind energy. In the global transition to renewable energy sources, wind power has emerged as a key player that is in line with the Paris Agreement, the Net Zero Target by 2050, and the UN 2030 Goals, especially SDG-7. It is critical to consider the variable and intermittent nature of wind to efficiently harness wind energy and evaluate its potential. Nonetheless, since wind energy is inherently variable and intermittent, a comprehensive assessment of a prospective site's wind power generation potential is required. This analysis is crucial for stakeholders and policymakers to make well-informed decisions because it helps them assess financial risks and choose the best locations for wind power plant installations. In this study, we introduce a framework based on Copula-Deep Learning within the context of decision trees. The main objective is to enhance the assessment of the wind power potential of a site by exploiting the intricate and non-linear dependencies among meteorological variables through the fusion of copulas and deep learning techniques. An empirical study was carried out using wind power plant data from Turkey. This dataset includes hourly power output measurements as well as comprehensive meteorological data for 2021. The results show that acknowledging and addressing the non-independence of variables through innovative frameworks like the Copula-LSTM based decision tree approach can significantly improve the accuracy and reliability of wind power plant potential assessment and analysis in other real-world data scenarios. The implications of this research extend beyond wind energy to inform decision-making processes critical for a sustainable energy future.

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