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ÖgeTalep tahmini için gri temelli bir yaklaşım(Lisansüstü Eğitim Enstitüsü, 2022-01-11)Gri sistem teorisi ilk olarak 1989 yılında literatüre kazandırılmış bir çalışma alanıdır. Gri sistem teorisinin alt konularından biri olan gri tahmin ise, bu tez çalışmasının da temelini oluşturmaktadır. Talep tahmini çalışmaları literatürde sıklıkla karşılaşılan alanlardan olup, bu çalışmada talep tahmini için gri tahmin modellerinden yararlanılmıştır.Talep tahmininde ise enerji ve dolayısıyla elektrik talebi (tüketimi) tahmini çalışmaları günümüzde önem arz etmektedir. Kapasitenin doğru planlanması ve doğru fiyatlandırılma politikası, doğru tüketim tahminleriyle başarılı olacaktır. Tüm bu durumlardan yola çıkarak; bu tez çalışmasının temel amacı, talep tahmini için yeni bir model sunmaktır. Bu çalışmada gri modelin küçük boyutlu verilerle yapılan tahminlerde başarılı olmasının avantajına odaklanarak hata oranını daha da küçükleyecek melez modeller sunmak amaçlanmıştır. Tezde elektrik tüketimi tahmini üzerine yapılan uygulamada kullanılan ve önerilen modeller, literatürde ilk kez kullanılan hibrit modellerdir. Bu yönüyle çalışma literatüre önemli katkı sağlamaktadır. Literatür taraması ile gri tahmin modeller kullanılarak yapılan tahmin çalışmaları ve alanları, elektrik tüketim tahmininde kullanılan gri modeller ve Türkiye'de yapılan elektrik tüketimi tahmin çalışmalarında kullanılan modeller genel olarak incelenmiş ve bu bilgiler ışığında hibrit bir model olan bulanık GM (1,1) parametre optimizasyonu Güve- Işık Optimizasyonu Algoritması modeli önerilmiştir. Önerilen bu modele yuvarlanma mekanizması da eklenerek bir model önerisinde daha bulunulmuştur.Yapılan literatür taramasında gri modellerde parametre optimizasyonu çalışmaları da incelenmiş ve sıklıkla kullanılan metasezgisellerin olduğu çalışmalara yer verilmiştir. Çalışmada literatür taraması ve talep tahmini konusundan sonra literatürde en çok kullanılan Gri modellere ve bu tezin temel modeli olan Üçgen Bulanık GM(1,1) modeline detaylı biçimde yer verilmiştir. Sonrasında ise; metasezgisel algoritmalar konusu incelenmiş ve parametre optimizasyonunda kullanılan metasezgiseller detaylıca anlatılmıştır. Önerilen modellerin denklemleri ayrıntılı bir şekilde ifade edilmiş ve Türkiye'nin kısa dönem elektrik tüketimi tahmini konusunda bir çalışma yapılarak sonuçlar yorumlanmıştır. Uygulama, MATLAB 2018b programından yararlanılarak yapılmıştır. Önerilen modeller literatürde var olan gri model ile kıyaslanarak tahmin performansı başarımı ölçülmüştür. Kullanılan hata ölçütü literatürde sıklıkla kullanılan Ortalama Mutlak Yüzde Hatası (OMYH)'dır. Modellerde kullanılan ve literatürde yeni olan Güve- Işık Optimizasyonu Algoritması'nın performansı ise literatürde parametre optimizasyonu için sıklıkla kullanılan algoritmalarla kıyaslanarak ölçülmüştür. Söz konusu algoritma ile önerilen modeller Genetik Algoritma ile kıyaslandığında daha iyi bir tahmin performansı sergilemekte olup; PSO ile kıyaslandığında en az onun kadar güçlü olduğu sonucuna varılmıştır. Kıyaslama yapılırken programın çalışma süresi ve metasezgisellerin optimum noktayı yakaladıkları iterasyon sayıları dikkate alınmıştır. Uygulama sonuçlarına bakıldığında önerilen modellerin tahmin performansının geliştiği gözlemlenmiştir. GIOA, Genetik Algoritmaya göre süreler ve iterasyon sayıları açısından çok daha iyi sonuç verirken; PSO ile kıyaslandığında ise yakın süreler ve iterasyon sayıları vermekte olduğu gözlemlenmiştir. Son olarak, yapılan araştırma ve edinilen bilgiler ışığında tez çalışmasının sonuçları yorumlanmış ve gelecek çalışmalarla ilgili önerilerde bulunulmuştur.
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ÖgeEvolutionary mechanisms of imprinting in business clusters(Graduate School, 2021-03-10)Discussions on regional economic development, specifically on industrial districts and business clusters, since Marshall's (1920) seminal work has surged in numbers, especially after the popularization of cluster studies with Porter (1990), who assigns a prominent role to clusters in microeconomics of competition and competitive advantage of regions and nations. On the other hand, the geographic distribution of resources and potentials for development are shaped by historical factors and regions can be understood through the mechanisms by which geographical landscape evolves over time. In this sense 'Evolutionary turn' is both promising as a new way of thinking about uneven geographical development and presenting an opportunity for linking different concepts and theoretical approaches from different schools of thoughts. Aim of this thesis is to reveal how clusters evolve and in which conditions environmental forces leave their mark on the evolution pattern of business clusters. Instead of employing a nurturist view, this study explores how nature of the cluster firms and nurturing of the environment interact during the variations observed in environments and tries to explain the dynamics of the imprinting processes. Even though spatial evolution of business clusters has been studied in business history and economic geography domains, coherent and holistic view of the phenomenon is still far from formulation. Contribution of this study is to bring an evolutionary model by extending multi-level imprinting theory (MLIT) with a selectionist view and to explain how particular features of business clusters are acquired under the influences of environmental forces. The study, in so doing, synthesizes MLIT and General Darwinism, which are built over analogies between evolutionary biology and evolutionary economic geography and lay the foundation of the basic evolutionary engine as variation, selection and retention processes. The thesis consists of seven sections. In the first section, aim and scope of the thesis is presented, and the flow of the thesis is framed. In the second section, theoretical perspectives on location, regional growth and local development are discussed, theoretical background of business clusters is summarized. In the third section the importance of 'Evolutionary turn' is highlighted, the concepts of evolutionary analysis within the economic landscape are discussed and imprinting theory is presented. In the fourth section, the conceptual model, explaining evolutionary mechanisms of imprinting and how environmental influencers work with evolutionary engine is described, evidences from the empirical literature are presented. In the fifth section, research design is explained, selection of sites and how data are collected and analyzed is justified. In the sixth section, findings related to footwear industry and footwear production clusters in Izmir and Konya are presented. In the last section findings are discussed in the light of conceptual model, how evolutionary mechanisms and engine explain the imprinting success or failure is presented, and new research opportunities for developing the model are offered. The proposed conceptual model aims to contribute to the evolutionary economic geography by extending MLIT in many ways; (i) exploring influence of heredity factors governing access to resources and legitimization forces, (ii) defining how "window of imprintability" works through evolutionary mechanisms, (iii) explaining emulation of imprints through retention process and the role of secondhand imprinting, (iv) describing the role of pioneering firms in the creating founder effect and lastly, (v) clarifying the role of institutional order within group of firms as a constraining factor with its implications on the imprinting process. This study follows a historical case study research which is suitable for exploring the cluster as a contextually bounded system over time. The research setting planned for this study is footwear clusters which is a traditional industry, geographically agglomerated and based on apprenticeship. Before preparing the research questions a preliminary site research is conducted and discussions with highly experienced professionals are made. This also enabled the researcher to be more informed about the research sites in Konya and Izmir. Interview questions were determined after the preliminary site research and compiled in accordance with the conceptual model and its constructs. Data collected by open-ended interviews are triangulated with other secondary data sources like sectoral reports, journals, academic papers and studies, archival records, photograph archives, direct observation and physical artifacts. In the study, data analysis is conducted in four stages. In the first stage background of shoe production in Turkey is explained and current context is clarified with historical facts and figures. In the second stage, sensitive periods related to major economical, technological and institutional changes are investigated. In the third stage, selected areas to be analyzed are justified. In the last stage, imprinting process and their action mechanisms are deeply investigated. Possible imprints on specialization, division of work and cooperation are analyzed, and findings are interpreted according to the conceptual approach. Findings towards specialization showed that the specialization of Izmir footwear cluster in women shoes was affiliated with Westernization efforts in which shoe consumption habits were shaped by liberalized clothing, social life and attendance of women in work life. On the other hand, this WOI did not turn automatically into an imprint. The environmental fit of producers shaped by the local demand enabled them to seize this opportunity and retention was mainly characterized by apprenticeship and spin-offs, which pushed the imprints to stamp a critical mass of the population. Findings in division of work uncovered three cases for deeper analysis. In the case of stitcheries, small stitching workshops found to be sustained since the traditional production era despite the change in the economic conditions. These workshops persisted and attempts to replace this model failed due to unregistered work practices and traditionalization forces of within group fitness, characterized by irregular working hours, seasonality and underdeveloped managerial capacities. These findings showed the importance of both environmental fitness and within group fitness in the imprinting process. The case of Sumerbank was an example of Wrightian-drift. During state led industrialization period, Turkish Republic founded Sumerbank and consolidated all state production facilities including the shoe factory in Beykoz, which was a vertical production giant of its time. The favorable selection niche created by the government did not breed into new mass production facilities and transform into a successful imprint on the industry. The barriers blocking diffusion of vertical production model seems not only to indicate the lack of capital accumulation of producers but also presence of a parallel fit in the crossing niche and lack of physical proximity disabling knowledge dissemination. Moreover, the sensitive period characterized by state led development logic closed in less than two decades. Therefore, retention mechanism did not work, and imprinting attempt was failed. On the contrarily, the socialist imprint in Timişoara footwear cluster which was coeval with the Sumerbank initiative had successfully persisted even after the firms were privatized. This points attention to the importance of retention mechanism and reaching a critical threshold for solidification and persistence of the imprint. Lastly, findings on the Ottoman guilds and their Akhsim roots, which are mostly referred in the discussions of collaboration efforts in Konya, showed that both functions and values possessed by the ancestor of modern collaboration institutions have been mostly vanished. In this sense three sensitive periods are analyzed, foundation period, industrialization and mechanization with liberalization. The evidence supports the view that with the upsurge in competition, individualistic behavior has intensified, and individual fit became more important than group fit after the liberalization and mechanization period. Our conceptual model supports us to propose that liberalization changed the course of social norms and relations and this may lead to an imprint in the future. These findings contributed to the imprinting theory by highlighting the importance of the nature of business clusters, incorporation of a selectionist view and evolutionary mechanisms. The accumulated heredity factors and window of imprintability operating in the imprinting process are both conceptually and empirically explained. Sensitive periods do not automatically result in the evolution of a cluster. Instead, the model affirms that during a sensitive period, the operations in the variation-selection-retention engine, overcoming the pressures of within group fitness and reaching a critical mass in the local populations are prerequisites for successful imprinting.
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ÖgeFuzzy clustering based ensemble learning approach: Applications in digital advertising(Lisansüstü Eğitim Enstitüsü, 2021)Although the history of machine learning algorithms is quite old, it has been popularly used in the last ten years. The main reason for this situation is that it has become possible to run these algorithms even on our personal computers with the developing computer hardware technology. In addition, the size of the data generated in the internet environment is increasing exponentially with each passing day, with digitalization and internet usage becoming more widespread. Therefore, the need for technologies such as big data and machine learning is increasing day by day. In line with the increasing demands, machine learning has become an indispensable need in academia and the private sector. Thanks to machine learning, companies make predictions about their future processes, thus aiming to eliminate future uncertain situations and create more effective process management. E.g., A company seeks to use its marketing budget more effectively by using machine learning technologies for its marketing processes and thus maximizing its profitability rate. In recent years, there have been many studies in the literature on the development of machine learning algorithms and the elimination of the weaknesses of traditional machine learning methods. Regardless of the type of problem in the prediction process, the aim is to predict with a minimum error rate. In this context, many methods have been tried. The ensemble learning approach is one of the most successful methods in the literature, proving its success for this purpose. The purpose of ensemble learning is to combine multiple algorithms to close each other's weaknesses and increase the success rate in prediction. Observations on the dataset to be estimated may be characteristically similar or very different from each other. In this case, in many studies in the literature, the clustering process is performed before applying machine learning algorithms, and then the modelling stage is started. In such approaches, hard clustering approaches are used. Hard clustering approaches assign each observation value to only one cluster due to their working principles. Therefore, the sizes of the subsets to be modelled in some cases do not reach the size of the training set required for higher prediction success to occur. Considering that an observation value contains the characteristics of more than one cluster simultaneously, it is seen that the soft clustering approach is used to eliminate this problem. Although there are many studies in the literature on the fuzzy clustering method, which is a part of the soft clustering approach, there are not many examples in the literature regarding the use of the machine learning approach as an intermediate method in terms of improving its results. In this thesis, after the fuzzy clustering approach applied to the observation set with three published essays, it is aimed to ensemble the most successful models of each cluster, taking into account the error rates and thus improving the model performances. To test the validity of this approach, different studies were carried out for both regression and classification problems with datasets obtained from different sectors. In the first study, click and sales predictions were realised using digital advertisement performance data and reservation data in metasearch engines of an online travel agency operating in Turkey. This prediction is crucial for the company's short, medium and long-term financial goals. In this study, the traditional regression method and the proposed fuzzy clustering approach were used together and the results were compared with the results of the traditional methods. Machine learning algorithms were applied directly to the dataset, which had been applied data preprocessing and feature engineering within the framework of traditional methods. Then the modelling study was carried out again after the hard clustering and soft clustering approaches were applied to the dataset. As a result, although the processing load increased due to the inclusion of the clustering approach in addition to the modelling stage, more effective results were obtained than applying machine learning algorithms directly to the dataset. At the same time, the results obtained after the hard clustering approach and fuzzy clustering approaches were compared. It was observed that the success rate of the predictions made after the fuzzy clustering approach was higher. In the second study, the approach proposed in the first article is tested for solving a different problem with different sector data. In this study, it has been tried to predict the lifetime value of the customers by using the game data and session information of the users of a mobile crossword puzzle game published in more than thirty languages and more than thirty countries. Ensemble learning algorithms, which were not used in the first article, were examined in more depth and focused on algorithms that could achieve higher prediction success rates when used together with fuzzy clustering. Different hyperparameter combinations of Catboost, Extreme Gradient Boosting and Light Gradient Boosting algorithms, which are seen in the literature to be generally more successful than traditional machine learning algorithms, were tested separately for each cluster after the clustering phase performed with the Fuzzy C-Means clustering algorithm. The prediction values of the three most successful of these combinations were weighted to be inversely proportional to the error rates, and the error rates of the resulting predictions were compared with the results of other model-parameter combinations. It has been determined that the model established with the proposed method has a lower error rate than other models, thus making a more efficient prediction. In the third study, customer retention rate prediction was carried out with a different dataset collected in the gaming industry. Unlike the first two studies, in this study, a classification problem was tried to be solved with the proposed method, at the same time, different cluster initial parameters and different fuzziness parameters were tested. The aim is to obtain a more optimal clustering in the Fuzzy C-Means clustering approach, and the clustering process was the most successful combination. Since the nature of the problem is a classification problem, the prediction was carried out by weighting the accuracy results instead of the error rates of the algorithms at the stage of combining the results of the algorithm-parameter combinations. As a result of this study, it has been observed that the results of the method applied on different clusters clustered with the fuzzy clustering approach produce more effective results than applying machine learning algorithms directly to the dataset. As a result, this thesis provides the opportunity to make more successful predictions in datasets with different characteristics by strengthening the concept of ensemble learning, which has an important place in developing machine learning approaches with fuzzy clustering approaches. In addition, it allows identifying observation sets that contain the characteristics of more than one cluster simultaneously and to model in separate clusters during the modelling phase to create more effective prediction results. In this constantly developing field, new studies can progress from many branches. First of all, in the fuzzy clustering stage, instead of the Fuzzy C-Means clustering method, other alternative fuzzy clustering approaches in the literature can be tried again during the modelling stage. And a different fuzzy clustering algorithm can be preferred according to the efficiency result. At the same time, it may be possible to change the weight coefficients with different methods or shapes at the stage of combining the results of the most successful models. Beyond all this, this method will enable to produce more effective results by using it together with new machine learning algorithms that will be introduced to the literature in the future.
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ÖgeKonteyner kapasite ve taşıma planlama politikalarının sistem dinamiği yaklaşımı ile modellemesi( 2020)Hammadde, yarı mamul ya da ürünlerin depolanması, taşınması, saklanmasında kullanılan, yeniden kullanılabilir taşıyıcı ekipmanların sürdürebilirlik açısından önemi günden güne artmaktadır. İlgili ekipmanların sağladığı kazanımlar ile birlikte, işletmeler bu ekipmanların etkin yönetimi ile ilgili birçok problem ve karar süreci ile karşı karşıyadır. Stratejik seviyeden operasyonel seviyedeki karar süreçlerine kadar doğru politikaların üretilmesi ve/veya seçilmesi işletmelere rekabet avantajı kazandıracaktır. Bu yüzden tedarik zincirinde farklı karar hiyerarşilerindeki problemler araştırma konusu olmuştur. Bu çalışmada da göbek ve ispit ağı yapısına sahip konteyner servis sağlayıcılarının, taktiksel karar hiyerarjisi içerisinde yer alan konteyner servis kapasite planlama süreci için kazanç seviyesini en fazla iyileştirecek kapasite ve taşıma politika kümelerinin belirlenmesi amaçlanmıştır. Konteyner kapasite planlama sürecinin dinamik yapısı, diğer konteyner yönetim süreçleri ile etkileşim içerisinde olması, bünyesinde birçok geri bildirim döngüsünü barındırması sebebiyle ilgili politika kümesi analizleri için üç ana faz ve altı ana aşamadan oluşan bir sistem dinamiği modelleme yaklaşımı önerisinde bulunulmuştur. Kapasite ve taşıma planlama süreçlerini barındıran konteyner servis kapasitesi konusu, konteyner sistemi içerisinde birçok alt süreç ile etkileşim halinde olup; çalışma kapsamında konteyner sistemi kavramsal modellemesi nedensel döngü diyagramlarından yararlanılarak; filo planlama, sevkiyat planlaması, sipariş yönetimi, bakım yönetimi ve finansal yönetim süreçlerini içerecek şekilde yapılmıştır. Kavramsal modelleme aşamasından sonra vaka çalışması olarak göbek-ispit ağı dağıtım yapısının yaygın bir şekilde kullanıldığı bir sektör olan otomotiv sektöründe, ana sanayi ve tedarikçisi arasında konteyner akış süreci seçilmiş ve belirlenen amaç doğrultusunda benzetim modellemesi yapılmış ve ardından ilgili modelin gerçek hayattaki sistem davranışlarını yansıtıp yansıtmadığının belirlenmesi için model güvenilirlik testleri yapılmıştır. Simülasyon çalışmaları yardımıyla farklı hedef kapasite oranı, sevkiyat doluluğu, değişime tepki hızı vb. parametrelerin temsil ettiği kapasite ve taşıma politika kümeleri, kazanç oranını en fazla iyileştiren politika kümesini belirlemek amacıyla tekrarlı bir şekilde geliştirilmiş ve analiz edilmiştir. Tekrarlı bir şekilde politika kümesi geliştirme faaliyetleri, çalıştırılan simülasyonlar içerisinde maksimum kazanç elde edilen politika kümesi üzerinden yürütülmüştür. Ardından karar parametrelerinin iyileştirimesi çalışmaları ile en iyi politika kümesi belirlenmiş ve politika tercihlerinin hangi koşullarda değişeceği duyarlılık analizleri ile belirlenmiştir. Bu yaklaşım sayesinde bütünsel bir bakış açısıyla ve birbiriyle etkileşim içerisinde olan konteyner sistemi süreçlerini içerecek şekilde, kapasite kullanım oranı ve müşteri hizmet seviyesi arasındaki dengeyi sağlayacak politika kümeleri geliştirilmiştir.