Yüksek Lisans, Proje Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/215

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İskandinav Ceza İnfaz Sisteminin İstisnailiği
(MEF Üniversitesi, 2021) Özel, İpek; Akyürek, Güçlü
Hapsetmenin finansal maliyetinin çok ötesine geçen, bireylere, ailelere ve toplumlaraönemli sosyal, kültürel ve politik maliyetler getiren, karşılığında da sanıldığı kadargüvenli bir toplum yaratmayan pahalı bir yol olduğunu kanıtlayan birçok kanıt vardır.Gerek Türkiye gerekse dünya cezaevi nüfusu her geçen gün yükselirken, bir yaptırımolarak özgürlüğü bağlama /hapsetme suretiyle cezalandırma ve ıslah etme yönteminin,ne suçluları vicdanlarıyla buluşturup, onlara yeni bir kişilik kazandırmada ne deinsanları suç işlemekten alıkoymada tam anlamıyla başarılı olduğunu söylemekmümkün değildir.Bunun tam tersini söylemek, yani, ceza infaz ve güvenlik tedbirleri daha sertleştikçe,cezaevleri daha “cezalandırıcı” hale geldikçe, suç işleme oranlarının ve işlenensuçların boyutunun daha da arttığını öne sürmek çok daha mümkündür.Öte yandan şurası da bir gerçek ki, başka seçenek yaptırımların yeterli olamayacağınoktada özgürlüğü bağlayıcı cezalar kaçınılmaz olur. Cezaevlerinin hepten ortadankalkmasını dilemek hoş ancak bunun, en azından yakın gelecekte, olabileceğinisöylemek yeterince gerçekçi değildir.Seçenek yaptırım olarak cezaevi bir zaruret olduğunda, son derece yüksek olan direktve dolaylı, insani ve toplumsal maliyetlerine rağmen özgürlüğü bağlayıcı cezalarabaşvurmak kaçınılmaz olduğunda, bu yaptırımdan en düşük insani maliyetle enyüksek etkiyi alabilmek için cezaevi yönetimine ilişkin gerçek manada iyileştirmeleregidilmesi gerekir.İskandinav İstisnailiği (scandinavian exceptionalism) olarak anılan ve Kuzey AvrupaÜlkelerinde uygulanan yöntem bu anlamda değerli öğretiler ve önemli ilhamlariçermektedir. Bu tez bu yöntemden hareketle daha hümanistik bir ceza infaz sistemiolasılığını tartışmaktadır.
Location and Cluster Based Sales Channel Potential Analysis in Retail
(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Bilgin, Birtuğ; Adem Karahoca
This analysis project was conducted on the need to obtain new analysis and inferences for the existing traditional sales channels of company, which wants to progress in line with its omni-channel goals. In order to reach the customer with the same level of service in all channels it is necessary to analyze the dynamics of the channel well. In this project, I aimed to make sense of demographic data with the linear model and future selection model and to transform it into meaningful information that will guide sales strategies. Especially for diffusion strategies, in addition to traditional methods, data-based location analysis and analysis of sales weights of existing points are required. With the information to be provided, new dealer opening processes will also be based on data.
Segmentation With Unsupervised Learning: an Application Using the Walker's Data
(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Polat, Taylan; Özgür Özlük
In this project, the Walkers suitable for the service were filtered by using the dataset shared by the DogGo company. Then, unsupervised machine learning methods such as K-Means, Gaussian, Principal Component Analysis were used to score and cluster the most suitable walkers according to performance, willingness, and experience.DogGo is the first mobile application in Turkey that provides pet walking and grooming services to its customers in a safe and professional manner. DogGo provides a professional service where dogs are taken care of in dog families' own homes or at the caretaker's home for any need of dog families. DogGo Company wants to provide the best matching of walkers and animals, using Machine Learning algorithms, through a 5-step acquisition process for their walkers.While the results of the K-means models created on the unique sliders were compared with the help of the Elbow method and the Silhouette score, the results of the Gaussian models were compared with the AIC and BIC method. In addition, an RFM scoring in a classical structure has also been created. When the results of the study were examined considering the Elbow and Silhouette scores, it was shown that the model created with K-Means gave the best results, and the number of clusters was decided as 2.
Süt ve Süt Ürünleri Sektöründe Faaliyet Gösteren Şirketlerin 3pl (3. Parti Lojistik) Kullanım Tercihinin Satış ve Lojistik Departmanlarının Çalışma Alanları Açısından Swot Analizi
(MEF Üniversitesi Sosyal Bilimler Enstitüsü, 2021) Ayaz, Demet; Hande Karadağ
Şirketler, ürettikleri/sattıkları ürün ve/veya hizmetin, gerek üretim aşaması gerekse de nihai kullanıcıya ulaştırılması sürecinde lojistik şirketlerini kullanmayı tercih edebilmektedirler. Bu tercihlerinin farklı sebepleri olduğu gibi, bu sebeplerin şirketlerin farklı departmanları tarafından, önemlerinin boyutları da değişebilmektedir. 2020 Pandemi sürecinin başlamasıyla birlikte şirketlerin tedarik zinciri çözümlerinin doğru seçiminin yanı sıra çeşitlilik tercihlerinin ne kadar önemli olduğu ortaya çıkmıştır.Bu çalışmada, 3PL şirketiyle çalışma tercihi, bu kararı veren lojistik departmanlarının yanı sıra ürün ve/veya hizmetin satışından sorumlu olan satış departmanlarının sorumluluklarını da dikkate alınarak, detaylı bir şekilde SWOT analizi ile incelenmiştir. Bunun sonucunda kuşkusuz bir maliyet avantajı sağlayacak 3PL çalışma tercihinin, iyi bir planlama ve ciddi bir kontrolle desteklenmesi gerektiği, aksi takdirde orta ve uzun vadede sorunlar yaşanabileceği ortaya konulmuştur. Projenin, 3PL tercihinin Lojistik ve Satış departmanlarında yaşanabilecek çatışma konularına ve bu çatışmaların önüne geçilmesi için yapılması gerekenler anlamında literatüre katkı yapması beklenmektedir.
Online Shopping Purchasing Prediction
(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Kazezyılmaz, İdil; Evren Güney
This project aims to understand the purchasing behavior of the consumers and make predictions about purchasing according to website metrics such as page values, bounce rates.An existing dataset is used in this project. This dataset is available in the collection of data from an e-commerce website by Google Analytics, which consists of 10 numerical and 8 categorical attributes coming from 12,330 sessions. The 'Revenue' attribute is used as the class label. The attributes that have high impact on the prediction are; "Administrative", "Administrative Duration", "Informational", "Informational Duration", "Product Related" and "Product-Related Duration". They represent the number of different types of pages visited by the visitor in that session and the total time spent in each of these page categories.The "Bounce Rate", "Exit Rate" and "Page Value" features represent the metrics measured by Google Analytics for each page in the e-commerce site. The "Special Day '' feature indicates the closeness of the site visiting time to a specific special day (e.g. Mother’s Day, Valentine's Day) in which the sessions are more likely to be finalized with a transaction.Since the purpose of this project is to predict potential purchasing using existing data, in the prediction part several machine learning algorithms such as decision trees, random forests will be applied to compare the models. The most suitable model will be chosen among these algorithms.
Rfm Based Customer Segmentation for a Mobile Application
(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Baykan, Ozan Barış; Özgür Özlük
In this project, customer segmentation was made for Doggo, a mobile application that brings together trained dog walkers for people who are not able to provide daily needs of their dogs. The data was organized by obtaining the columns of recency, frequency, monetary and tenure, and RFM-based customer segmentation was made using machine learning algorithms such as K-means and Gaussian Mixture Model (GMM). Then, the model was built with the part of the dataset that includes recency, monetary and tenure columns using K-means. In addition, with a function developed, the RFM and tenure will be repeated at intervals determined by the Doggo operation team, and this tool is used to monitor the customer condition changing. Various marketing campaigns have been proposed according to the current situation and the transitions they have made.
Employee Performance Prediction
(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Sivas, Barış; Özgür Özlük
DogGo is a company that aims to provide safe and professional dog walking and grooming services to dog owners through the mobile application. Thanks to the DogGo application, dog owners and people who is employee of company and wants to walk their dogs (to be called Walkers) can meet on the same platform on the mobile application interface. The problem was determined by company that they needed to be able to accurately predict the performance of the walkers in the upcoming dog-walker matches, thus ensuring the correct dog walker match. This study will be planned to serve to this company for calculating their current walkers’ performance in an accurate way. The relevant machine-learning model will first be based on the manual scoring system made by the company for the performance of existing employees, and then the model will be developed in the light of the gains obtained from this. For the performance of the model, the employees and their characteristics are important for the first time.
Game Recommendation System for Steam Platform
(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Bayram, Serhan; Semra Ağralı
Increasing number of choices and competition in the markets, force companies to differ in services they provide to their customers. Offering better services have a positive impact on customer loyalty, and to do so, companies should understand their customers’ interests and act accordingly. One popular method for this purpose is building recommendation engines to make personalized suggestions. In this project, collaborative filtering methods with implicit feedback are used to make recommendations to users of theSteam platform. The recommendation systems are built using two different matrix factorization techniques, Alternating Least Squares and Bayesian Personalized Ranking. Different models are created with implicit playtime data of the users and the results are evaluated by using Precision at k metric. Additionally, similar items that are offered by the models are analyzed. Results show that the models are considerably successful at finding personal choices and similar items. The best model finds the item in the libraries of 33% ofthe users.
Prediction of Credit Card Default
(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Akalın, Selçuk; Utku Koç
As profitable customer acquisition becomes more and more critical for the banking sector in terms of competition, the requirement to predict customer defaults with different machine learning algorithms is increasing. Thanks to similar practices, possible damages can be prevented. Due to the rapid change of machine learning with the changing technology, the fields of application and development in different sectors are also changing and developing rapidly. In this study, the aim is to make a comparison over model outcomes and making observations on outcomes to determine the areas that can be developed or researched with running different supervised and unsupervised machine learning algorithms on the final dataset gathered by doing following methods such as key points discovered in exploratory data analysis on an imbalanced credit card dataset, generating different features according to learned key points, eliminating imbalance with different oversampling and undersampling methods.
Ad Click Prediction Using Machine Learning Algorithms
(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Uncu, Nazlı Tuğçe; Hande Küçükaydın
Online advertising has a great potential to boost business’ revenue. One of the key metrics that defines the success of online ad campaigns is click through rate (CTR) which indicates the total number of clicks received in relation to the total impression. Therefore, the click prediction systems, which have the aim of increasing the click through rates of online advertising campaigns by predicting the clicks, have become essential for businesses. For this reason, predicting whether an advertisement will receive a click fromthe user or not attracts the attention of researchers from the both industry and academia. In this capstone project, the click prediction is studied by using Avazu’s click logs dataset. The effects of having high cardinality categorical features and imbalanced data are examined during data preprocessing phase and then relevant features are selected to be used in modeling. The methods that are used for this classification problem are decision trees, random forest, k-nearest neighbor, extreme gradient boosting, and logistic regression. According to the results of the study, extreme gradient boosting shows the best performance.
The Effect of Bert-Based Grammatical Analysis on Google Search Results
(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Çolak, Oğuz; Özgür Özlük
This study aims to study the BERT, namely Bidirectional Encoder Representations from Transformers model, which is introduced by Google and is of great importance in content analysis, and to examine the role of grammatical accuracy in the process of content quality measurement and Search Engine Results Pages (SERP). BERT has an important role among the algorithms used by Google in order to maintain the quality of search results and to provide more relevant content to users by understanding the content more effectively.In this study, CoLA data, which is accepted as the most reliable data in this field and therefore used frequently in similar BERT studies, is used. The main purpose here is to make a BERT-based grammatical evaluation of sentences in a content and then examine these results on pages with optimal ranking values, to examine the connection between search results and grammatical accuracy and the importance of this parameter.In this context, the project consists of two phases. In the first phase, the content of the pages that are visible in the first 20 in 50 different queries are scored with the pre-trained BERT model. In the second phase, a dataset that includes different SEO-focused metrics of the same pages is created manually, and the importance of the BERT score among these features is investigated.
Search Engine Optimization Tool With Web Crawler, Page Density Checker, Search Density Checker, and Similar Page Checker
(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Körpe, Yiğitalp; Berk Gökberk
For this project, I built an SEO tool. I am working in digital marketing, and we are using various tools, and services frequently. But due to budget constraints or a variety of tools, we cannot reach each tool all the time. Even these tools are not easy to reach, some of their features are fundamental for jobs we are handling every day. Therefore, we needed to find their free / less expensive versions or use them within their free limits. But since I learned some coding and saw programming possibilities, I see that some must-have features are not that hard to code or complicated. Therefore, I created a small program to help my career and my budget. This script helps generally SEO reporting.This script has 4 main features. Web crawler feature can crawl the website and provide website’s page details. Page density checker feature can report the word density of the page. Search density checker searches the input query on Google, reports top 10 results and their word density. Finally similar page checker crawls the website and runs cosine similarity test for each page of the website.
Credit Card Froud Detection Using Machine Learning
(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Erdoğan, Tibet; Duygu Taş Küten
This project aims to find the most efficient machine learning models to detect fraudulent transactions on credit cards. The dataset used for this project consists of credit card transactions made by European cardholders in September 2013. This dataset presents transactions that have occurred in two days, where there are 492 frauds out of 284,807 transactions. Machine learning methods, such as decision trees, logistic regression and random forest classifier are used to predict the fraudulent transactions. Performance of these machine learning models are compared to achieve the highest accuracy. According to the results, it is found that the random forest classifier is the most effective model, and the SMOTE technique used to overcome the data imbalance performs better than the under-sampling technique. It is also observed that the models employed with the under-sampled data misclassify large number of non-fraud transactions as fraud. Lastly, by means of the random forest with the over-sampling technique (SMOTE), it is observed that the feature “V13” has the most important role in detecting fraud.
Binaların Söküm - Yıkım Sürecinde Atık Yönetimi
(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Gümrükçü, Barbaros; Seyyit Ümit Dikmen
Yapısal atıklar, yapı ve altyapıların bünyelerinde barındırdığı yapı ürünlerinin ve yapı alanı malzemelerinin, yapım, yenileme, onarım, söküm – yıkım, afet ve alan çalışması gibi etkinlikler sonucunda biçim değiştirerek atık durumuna dönüşmüş biçimidir. Yapısal atıklar yapının yapım, kullanım ve yok edilme süreçlerinde ve bu süreçler sonrasında sürekli olarak çevre ile doğrudan ya da dolaylı olarak etkileşim içindedirler. Yapısal atıklar yapı üretimi, kullanımı ve yok edilme süreçlerinin bilinçsiz, yönetimsiz, denetimsiz bir şekilde yürütülmesi, çalışmalar sonrasında doğaya gelişigüzel bir biçimde dökülmesi, doğal çevrede biriktirilmesi, yapısal atık yönetiminin uygun eylem adımları ile yürütülmemesi, tehlikeli atıkların denetim altına alınmaması, yapısal atıkların doğru değerlendirme seçenekleri ile değerlendirilememesi vb. gibi nedenlerle tüm çevre yapısal atıklardan olumsuz bir biçimde etkilenmektedir. Bu nedenle yapısal atıkların çevreyi olumsuz bir biçimde etkilemesinin önlenebilmesi için yapısal atıkların yönetilmesi ve denetlenmesi gerekmektedir. Yapısal atık yönetimi süreci içerisinde yapısal atıkların büyük boyutlara ulaşmadan önlenmesi, aynen ve ikincil olarak yeniden kullanımı, geri dönüşümü ve uygun tekniklerle yok edilmesi işlemlerini barındırır. Yapı yaşam sürecinin son aşaması olan söküm – yıkım, tüm yapının yok edilmesinden kaynaklı olarak var olan yapıda tüm ürünlerin atığa dönüşmesi işlemidir ve yapı ürünlerinin hacmi kadar yapısal atık üretimine neden olur. Bu nedenle oluşan bu büyük boyutun yönetiminin yapılması gerekmektedir. Yapısal atık yönetiminin yapılabilmesi için sökümü – yıkımı yapılacak olan her yapının bir “SORUN” olarak görülmesi, bu soruna çevreye olumsuz etkilerde bulunmadan bir “ÇÖZÜM” geliştirilmesi ve bu çözümün uygun koşullarda “UYGULAMA” ile gerçekleştirilmesi gerekmektedir. Önerilen rehber çalışma ile yapı söküm – yıkım öncesi aşamasında yapı, çevre etki değerlendirme ve yapı çevresi analizleri ile var olan yapıda sorunun boyutu ve içeriği belirlenmesi, yönetim kararları ve planlama ile belirlenmiş olan soruna doğru çözüm eylem adımları oluşturulması gerekmektedir. Yapı söküm – yıkım anı ve yapı söküm – yıkım sonrası aşamalarında ise yapı söküm – yıkım çalışması öncesi aşamasında oluşturulan çözüm eylem adımlarının doğru bir biçimde gerçekleştirilerek uygulanması gerekmektedir.
Predicting the Price of Bitcoin: Using Machine Learning Time Series Methods
(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2020) Ulutaş, Sezer; Utku Koç
Cryptocurrencies have greatly increased their Bitcoin-led popularity in recent years due to increased trading volumes and massive capitalization in the market. These cryptographic forms of money are not just utilized for exchanging nowadays, they are additionally acknowledged for fiscal exchanges. It appears to be evident that financial specialists, dealers and people, in general, are progressively intrigued by bitcoin and altcoins as costs rise and the arrival on ventures made increments. This examination centres around applying estimate models that will make precise value forecasts forcryptographic forms of money. The data were taken from two different exchanges and evaluated as combined dataset. As a result of the evaluation, it was determined that the prices were close to each other in terms of value and the data were combined. We obtained the daily time series data by determining the Bitcoin weighted price as a dependent variable and Open, Close, High, Low and Volume as independent variable. We predicted the next 6 months with ARIMA, LSTM and XGBoost methods. We compared these estimates using MSE, MAE, MAPE and R squared performance metrics. LSTM is the model with the best R squared value of 29.7%. In the process performed by taking the average of LSTM, XGBoost and ARIMA performed with the name of Average ML method, the R square value was found to be 41.6% as a much better result than LSTM.
Convolutional Neural Network for Facial Emotion Recognition With Geometrical Features of Face
(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Arslan, İlker; Arslan, İlker; Tuna Çakar
One of the recent challenging machine learning problems is to make predictions on image datasets. The aim of the project is to construct a convolutional neural network to guess emotions for a face of a human given in an image file considering the face. After the geometrical features are extracted using pretrained models, we construct five models which are convolutional networks fed with handcrafted geometrical features extracted. The last model uses the outputs of other four models to predict more accurately.
An Examination of the Effect of Monetary Expansion Policies Implemented by Four Large Central Banks After the 2008 Global Financial Crisis and the Covid-19 Crisis on Developing Countries on the Example of Turkey
(MEF Üniversitesi Sosyal Bilimler Enstitüsü, 2021) Özbey, Sakine Gülşah; Nazlı Karamollaoğlu
After 1980, financial markets took a share due to globalization trends in the world. In literature, many studies exist which show us that the financial crisis and financial globalizations started to appear more often than it did in the past. The market’s mood is reflected in the data when risks and incalculability increase in financial markets. Financial liberalization and the removal or significant reduction of inspections have increased the fragility of markets. A number of decisions were made and interfered with by many authorities after the financial crisis which was felt all around the world for a long time. Before the 2008 economic crisis price stability was a focus for central banks. However, the importance of financial stability came into prominence after thecrisis. The negative effects of Covid-19 crisis, which was not originated from economic reasons at the same time, which created a supply and demand shock, were seen fast. Like in every crisis politicians interfered in order to reduce the effects of the crisis. The connection between the 2008 global financial crisis and Covid-19 crisis is the need to increase declining total demand. By the reason of reduced economic activity on a global scale, monetary and fiscal policies and inventions that increase economic activity have been involved. The concept of globalizations has multifaceted effects ondeveloping countries. By the entering of funds into enhanced market economies, it helps developing countries to meet the need for financing that will provide economic growth and development, while reducing production and increasing dependence on external financing. With financial globalization direction and momentum of the movement of fund is changing according to countries’ macroeconomic appearance. Particularly development and decisions taken in countries like the United States and England, which have the right comment on world trade, have influenced all around the world. The policies implemented by these countries in times of crisis are closely followed by economic actors. In this study FED (Federal Reserve Bank), ECB (European Central Bank), BOJ (Bank of Japan), BOE (Bank of England), monetary easing policies implemented by central bank after the global crisis in 2008 and the Covid-19 crisis were examined and how developing countries are affected by these crisis and policies are discussed and the example of Turkey was examined.
QPICAR Deep Learning
(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Beğde, Özge; Tuna Çakar
The aim of the project is to train a smart tool kit named "Sunfounder Raspberry Pi Robot Car" to move without hitting the walls in a closed area. The goal is to maximize the driving time without crashing by reducing the number of hits. Ultrasonic sensor data collected from the vehicle are processed with reinforcement learning and deep reinforcement learning algorithms and the results are compared. In this study, Python programminglanguage is used. In this study, firstly, the Q-Learning method, which is a reinforcement learning algorithm based on Markov decision processes, is used. The method basically relies on a memory table, Q-Table, in which the Q-values of the agent moving from one state to another are kept. This table is updated according to the results of the Bellman equation in every action of the agent, and as a result of this iterative process, it is optimized to provide that the agent moves to maximize its rewards. Deep Q-Learning (DQN) is used as a deep reinforcement learning algorithm. This algorithm was developed by the DeepMind Technologies team in 2013. Basically, it is based on the use of the Bellman equation, which is an element of the Q-Learning method, incombination with neural networks. This method is often used for training agents in complex and multidimensional environments such as video games. Due to the different type of the data used on the algorithm, minor changes were made to adapt it to the study. RElu and Softplus are used as activation functions. The results of the training process show that the DQN algorithm has an important advantage in terms of training the agent in a short time. At this point, the results are in accordance with other academic studies demonstrating the success of the DQN algorithm for complex environments.For future work, by differentiating the equipment that collects data on the vehicle, different data types such as image, temperature value, oxygen value can be collected and processed. At the same time, with changes to the reward setup in the algorithm, the agent can be trained to move to a specific target or to take actions to avoid a specific target.
Türk İş Hukuku Hakkında Kısa Çalışma
(MEF Üniversitesi Sosyal Bilimler Enstitüsü, 2021) Suner, Uğur; Ercan Akyiğit
Dünyamızda yaşanan teknolojik ve ekonomik gelişmeler akabinde çalışma yaşamında değişikler yaşanmaktadır. Teknoloji çağında sözgelimi dünyamızda artık ticari olarak sınırlar kalkarak rekabetçi ortam daha da büyümüştür. Bu gelişmeler ışığında, ilk olarak etkilenen çalışma yaşamı olmaktadır. 4857 sayılı Kanun’un 65. Maddesi ile sadece İş Kanunu’na bağlı olarak çalışan1 işçilere uygulanan kısa çalışma kavramı literatürümüze girerek çalışma hayatına olumlu yönde etki edilmek istense de, uygulamada bu pek mümkün olmamıştır. Uygulamada yaşanan sıkıntılar nedeni ile kısa çalışma düzenlemesi 4857 sayılı İş Kanunundan çıkartılarak, İşsizlik Sigortası Kanunun ek 2. Maddesinde uygulanma alanı bulmuştur. Kısa çalışma; genel ekonomik kriz, sektörel ve bölgesel kriz ile zorlayıcı sebeplerin varlığı halinde çalışma süresinin azaltılması ya da işin tamamen durdurulması olarak tanımlanabilir. Çalışmamızda, kısa çalışmanın koşul ve şartlarından, başvuru şekli ile sona ermesine ilişkin bir değerlendirme yapılmıştır.
Bir Otoyol Projesi Yapım İşlerinin Maliyet ve Zaman Açısından Proje Yönetim Teknikleri ile İncelenmesi
(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Dilek, Gülşen; Seyyit Ümit Dikmen
Küreselleşen dünyada rekabetin artmasıyla ile birlikte harcanan zamanın değeri ulaştırma sektörüne en etkili şekilde yansımaktadır. Yolcu ve yüklerin belirli bir mesafeye belli koşullarda taşınması olarak tanımlanan, sosyal ve ekonomik gelişmenin temel öğesi olan ulaşım; karayolları, demiryolları, denizyolları, havayolları ve boru hatları ile sağlanmaktadır.1950 sonrası dönemde gerek Marshall yardımları gerek otomotiv sanayi vb. etkenlerin körüklemesiyle diğer taşıma türlerine göre karayolu yük ve yolcu taşımacılığının artışı geçmişten günümüze yansıyan ulaşım politikalarının bir neticesidir.Sonraki dönemlerde, yüksek maliyetli yatırımlar olan Otoyol projeleri, Kamu-özel işbirliği olan YİD modeli ile yürütülerek, çeşitli kamu altyapı yatırımlarının sadece yapım işi değil, bakım ve işletme hizmetlerinin de bir veya birden fazla özel sektör firması tarafından uzun dönemli yaptırılması sağlanmıştır.Bu çalışmada YİD Metodu ile yaptırılan Otoyol projeleri büyük ölçekli ve ileri teknik bilgi gerektiren projeler olduğu için proje yönetim sürecinde zaman, maliyet ve kalite başarısı açısından ortaya çıkabilecek belirsizliklerin, hatta risklerin etkin bir şekilde Proje yönetim teknikleri ile yönetilmesinin projenin başarısına etkilerinin anlaşılması hedeflenmiştir.YİD kapsamındaki otoyol projelerinde ön maliyet tahmini yapılırken karşılaşılan zorlukların nedenleri, ön maliyet tahmininde veri ve kaynak ihtiyaçlarının doğru bir şekilde tespit edilmesi için yeterli zaman ayrılmaması, yatırımcıların beklenti ve taleplerini değiştirmesi, enflasyon oranına bağlı fiyat değişiklikleri, döviz kurundaki dalgalanmalar, inşaatın doğası gereği ortaya çıkan öngörülemeyen maliyetler olarak saptanmıştır.

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