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ÖgeExtensions of Z-fuzzy numbers and novel multi criteria decision making models(Graduate School, 2024-02-01)The ordinary fuzzy sets are based on the fact that the belonging of an element to a set can take values between 0 and 1, and they emerged due to the incapability of classical sets to describe uncertainty in human thought. After fuzzy sets were introduced to the literature, it began to propose more than one parameter to define uncertainty. For example, while ordinary fuzzy sets use only membership functions, intuitionistic and Pythagorean fuzzy sets use membership and non-membership functions; neutrosophic, picture, and spherical fuzzy sets use membership, non-membership and indeterminacy functions. Although all these fuzzy sets have different properties and conditions for defining uncertainty, they are unable to define the reliability degrees of judgments. Z-fuzzy numbers allow judgments to be defined not only with a restriction function but also with their reliability degrees. In this thesis, extensions of Z-numbers have been proposed to the literature by integrating fuzzy set extensions with Z-numbers. Thus, novel Z-numbers have been presented to the literature for defining uncertainty, and fuzzy sets have been given the ability to represent reliability under their own properties and conditions. In addition, multi-criteria decision-making (MCDM) methods have been expanded by using ordinary fuzzy Z-numbers and these new fuzzy Z-numbers. Thus, new Z-fuzzy MCDM methods have been introduced to the literature. For this purpose, in the first three chapters, new Z-fuzzy MCDM methods are presented such as Z-CODAS, Z-AHP and Z-EDAS methods. In other three chapters, decomposed fuzzy Z-numbers, picture fuzzy Z-numbers, and interval-valued spherical fuzzy Z-numbers have been developed and integrated with different MCDM methods. Each chapter is summarized below: In Chapter 2, the COmbinative Distance-based ASsessment (CODAS) method, which is a method based on Euclidean and Taxicab distances, is expanded with Z-numbers and introduced to the literature. The proposed Z-CODAS method has been applied to the supplier selection problem. For this purpose, firstly, decision criteria are weighed based on Z-pairwise comparison matrices. Then, the obtained criteria weights are integrated into the Z-CODAS method and used to rank alternative suppliers. The obtained results are compared with the ordinary fuzzy simple additive weighting (SAW) method. Chapter 3 presents a multi-experts MCDM method for evaluating social sustainable development factors. The proposed approach integrates Z-numbers and AHP method and may guide many sustainable development researches. In this study, Z-numbers have been used for the first time to evaluate social sustainable development factors. In addition, the other contribution of the study is presenting the Z-AHP method with multi-experts which can be useful for the solution of many MCDM problems containing uncertainty. The proposed Z-AHP method allows pairwise evaluations to be represented with their reliability degrees and integrated into the calculations. Chapter 4 extends the Evaluation based on Distance from Average Solution (EDAS) method to the Z-EDAS method. In this chapter, a decision making methodology is proposed by the integration of Z-AHP method and Z-EDAS method. The practicality of the proposed methodology is presented with an application on wind turbine selection problem. The comparative analysis conducted with Z-TOPSIS method demonstrates that the usefulness and competitiveness of the proposed methodology are provided. The results show that proposed methodology can both represent decision makers' judgments extensively, and reveal a logical ranking results related to alternatives by the usage of reliability information. In Chapter 5, decomposed fuzzy Z-numbers, which are the integration of decomposed fuzzy sets (DFSs) and Z-numbers, are introduced to model functional and dysfunctional judgments in a reliable decision environment. Collecting judgments under the circumtances of Z-numbers from experts using functional and dysfunctional questions can provide more consistent and reliable decision environment. In this chapter, a new decomposed fuzzy Z-linguistic scale and defuzzification formula are introduced. Then, decomposed fuzzy Z-TOPSIS method is developed for the solutions of MCDM problems under uncertainty. An application on transfer center location selection for a private cargo company in Marmara Region of Turkey is presented. The effect of the reliability parameter on the results is analyzed. Chapter 6 presents a decision methodology that integrates the picture fuzzy Z-AHP (PF Z-AHP) method for weighting criteria and a novel PF Z-TOPSIS method for ranking the alternatives. Although the picture fuzzy TOPSIS methods are used to model decision makers' hesitancy in their evaluations, adding reliability degrees to these evaluations can provide better solutions and reliable decision environments for real-life applications. In order to analyze the utility of the proposed PF Z-AHP&TOPSIS methodology, it is applied for solar energy panel selection problem. The sensitivity and comparative analyses are also performed to analyze given decisions and the effects of Z-numbers on the results. In Chapter 7, a new interval-valued spherical fuzzy (IVSF) Z-number is developed combining the ability of SFSs to allow the assignment of membership degrees in a wider domain with the ability of Z-numbers to represent reliability. In addition, a novel Interval-valued Spherical Fuzzy Z-Analytic Hierarchy Process (IVSF Z-AHP) is proposed by integrating the IVSF Z-numbers and AHP method. Then, a new IVSF Z linguistic scale and a new defuzzification formula are proposed. The proposed IVSF Z-AHP method is applied for green supplier selection problem to show the practicality and applicability of the method. Comparative analysis and sensitivity analysis show the necessity of reliability information in decision making. In summary, in this thesis, new extensions of Z-numbers and new fuzzy MCDM methods integrated with these extensions are proposed to the literature. Then, the proposed method and methodologies have been applied to various decision-making problems to demonstrate their practicality. In order to show the importance of reliability information, this information has been ignored and the problems have been resolved with the same data and it has been investigated whether the rankings of the alternatives changed. The results and the analyzes provide evidence that reliability information has the potential to change the rankings of alternatives. Especially when the reliability degrees of experts' judgments are wanted to be considered in the decisions, managers or practitioners can use the proposed approaches in this thesis to produce more reliable and meaningful solutions to their problems. In further researches, many different extensions of Z-numbers can be developed and compared with the results of the methods proposed in this thesis.
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ÖgeCrew recovery optimization through disruption analysis and deep learning driven column generation(Graduate School, 2024-02-06)Thanks to globalization, new travel opportunities and economic development have increased the interest in the aviation industry and air transport. The increase in the number of passengers and compliance with the regulations on passenger rights make transformation inevitable for the aviation industry and airlines. Airlines are reorganizing and managing all their resources in line with this transformation. The most critical resources of commercial airlines are the crew and aircraft, which together with passengers are key components of operational efficiency. Unforeseen events such as bad weather conditions, aircraft malfunctions and crew absence may result in inefficiency in operations and thus in the utilization of aforementioned resources. These events are called disruptions. As disruptions such as delays are the primary and fundamental factor in passenger satisfaction and the airline's financial conditions, aviation companies devote valuable resources to analyzing disruptions and taking necessary actions. Actions known as recovery actions are the ones decided by the airline operations control center to overcome problems in the execution of plans due to disruptions. The crew recovery problem is a complex optimization problem in the airline industry that involves scheduling and assigning crews to flights while taking into account various constraints such as legal regulations, crew availability, and cost. Several methods have been proposed to solve this problem, including heuristic and metaheuristic algorithms, integer programming, and constraint programming. It is a type of optimization problem that aims to choose the best recovery strategies to overcome crew disruptions. The main goal is to find the minimum cost combination of assignments that solve the problems related with crew plans while considering all constraints, especially flight time limitations. One popular method for solving the crew recovery problem is the column generation algorithm, which involves generating and adding columns to the problem's LP relaxation until an optimal solution is obtained. Other optimization methods include simulated annealing, genetic algorithms, and ant colony optimization. However, despite the various optimization methods proposed, the crew recovery problem remains a challenging and computationally expensive task due to the large number of variables and constraints involved. Furthermore, real-world crew recovery problems are dynamic and uncertain, requiring the ability to adapt quickly to unexpected events. This is where machine learning (ML) can play a crucial role in developing an optimization method with AI support. By leveraging machine learning algorithms, we can learn from past data and experiences to make informed decisions and generate optimized solutions quickly and efficiently. Recovery strategies can be realized by using actions known as recovery actions. Especially during the preparation of recovery models, more effective strategies are produced by using the actions learned from the past disruptions as inputs in the model. The main motivation behind using actions as input is that learning-based approaches have the potential to generate more effective algorithms for large-scale and difficult optimization problems by inferring insights from historic datasets. Especially with the learning process, important points that people may miss in the solution process become easily noticeable and increase the success of the recovery process. In this study, recovery actions provided by a customized deep learning (DL) model are used as input to the proposed optimization model, in which the objective function minimizes the total assignment cost of crew. Crew disruption data including some of the flight disruptions from a large scale airline is analyzed. Based on the analysis of the data, features are generated and recovery actions are extracted. The recovery actions are used as label and supervised learning methodology is used to train a customized deep learning model. Our hypothesis is based on the assumption that deep learning can provide needed insights in order to solve the problem in a shorter time while preserving the optimality at a certain level. The fundamental insight that we derive from the deep learning model is the recovery actions, which will be used while generating new columns. The actions are used in order to configure the columns faster than the classical column generation by either directly modifying the column itself or narrowing down the solution space. The resource or resource groups including crew and aircrafts are filtered according to the information provided by the deep learning. This makes it possible to search for new columns in a narrower solution space, which makes the search time shorter compared to the classical column generation techniques. The primary goal of the study was to develop a model that balances solution quality and speed. Results indicate that the proposed method outperforms the reference model in terms of runtime while maintaining an acceptable level of optimality. This approach can be valuable for airline companies looking to efficiently address crew recovery challenges. Moreover, it contributes to the existing literature by introducing a new mathematical model and approximation method, demonstrating the potential of deep learning and optimization techniques for addressing complex aviation sector optimization problems and offering valuable insights for future research in the field of crew recovery.
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ÖgeLüks tüketimde satın alma davranışlarının neden esaslı olarak incelenmesi(Lisansüstü Eğitim Enstitüsü, 2024-05-21)Bu çalışma ulaşılabilir lüks tüketimde tüketicilerin satın alma davranışını incelemektedir. Son 20 yılda dünyada ve Türkiye'de sürekli büyüyen lüks ürün pazarında alım yapan tüketicinin satın alma davranışının etkenlerini, Davranışsal Neden Kuramından faydalanarak, neden esaslı olarak modellemekte, alım kararında tüketiciyi etkileyen algılanan değer, tutum, öznel norm, algılanan davranışsal kontrol ve niyet ilişkisini ve nedenlerin etkisini (düzenleyici, aracı ve direkt etki olarak) mercek altına yatırmaktadır. Nedenlerin de dikkate alındığı, lüks tüketimde satın alma davranışının modellenmesine yönelik kuramsal bir çerçeve oluşturan çalışma önerilen modeli veri toplayarak ampirik olarak sınamaktır.
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ÖgeData driven optimization and applications in complex real-life problems(Graduate School, 2024-06-12)Data-driven optimization (DDO) is a methodology that uses data analysis and problem modeling techniques together, whose popularity has increased rapidly in recent years and is expected to continue to increase rapidly in the next 10 years. In DDO, information is obtained from past data by using data analysis and this information is used in modeling the problem and solving the model. Thanks to this method, the assumptions made while modeling the problem are reduced and the model is closer to the problem encountered in real life. In addition, the information obtained allows the model to be restructured to get results in shorter time. The popularity of DDO has increased in recent years and therefore the number of studies in the field of DDO has increased in the literature. However, it is seen that the boundaries of DDO and the conceptual framework of this methodology have not been drawn since this is a new research field. For this reason, in this thesis, the literature in the field of DDO was first examined and a conceptual framework was presented. It is aimed that the proposed conceptual framework will guide studies in the field of DDO and contribute to determining the boundaries of DDO area in the literature. The proposed DDO methodology was applied to four different problems in this thesis study and the results were discussed. The first stage of DDO is obtaining information from data. At this stage, machine learning methods, data mining methods, fuzzy approaches and statistical methods are used. The first problem addressed in this thesis is the problem of predicting picking travel times, which is the most basic model parameter in solving operational warehouse problems. In this thesis study, the time required to pick the items in the orders received in the warehouse was predicted by using the historical picking data of a large automobile spare parts supplier warehouse. In the problem studied, the picking process is carried out by many different pickers and the picking times of each picker vary. Therefore, which picker does the picking directly affects the picking time. For this reason, first of all, pickers were grouped according to their picking characteristics using historical data, and pickers with similar picking characteristics were grouped under the same group. Fuzzy c-Means approach was used for this grouping process. Then, the data set was updated according to the cluster information obtained and picking travel times were predicted using the artificial neural networks method. Predicted picking travel times can be used as model parameters in all operational warehouse problems such as order picking problems, picker assignment problems and picker routing problems. The second problem addressed is the joint order batching, assigning and routing (JOBAR) problem, which is complementary to the first problem studied. This problem is a complex problem in which order batching, picker assigning and picker routing problems, which are three problems encountered in the order picking process in a warehouse, are addressed simultaneously. In fact, all of the lower three problems that make up the problem are NP-Hard in nature, that is, they are problem types where the solution time increases exponentially according to the problem size. Meta-heuristic methods are often used to solve such problems. In this study, the JOBAR problem is addressed in accordance with the DDO methodology. First of all, the picking travel time, which directly affects the outcome of each problem, was predicted using historical picking data with the artificial neural network method. In order to increase the accuracy of this prediction, the pickers' past picking characteristics were taken into consideration and included in the prediction process. The pickers were grouped into four groups according to their past picking performances, and this group information was used as input in the training of artificial neural networks. In the modeling phase, which is the second stage of DDO, the problem was first modeled with the mathematical modeling method and a mathematical model of the JOBAR problem was proposed, covering all three sub-problems. In the proposed mathematical model, picker group information reflecting the picking characteristics of the pickers and piking travel times obtained from artificial neural networks were used as model parameters. However, due to the complex structure of the JOBAR problem, it was not possible to obtain results from the mathematical model within the required time, and therefore a new heuristic algorithm based on the k-nearest neighbor algorithm was proposed for the JOBAR problem. The performance of the proposed heuristic algorithm is compared both with the results obtained from the mathematical model within a certain time limit and with the Clark&Wright algorithm, which is a frequently used savings algorithm in the literature. It has been observed that the proposed new heuristic algorithm is quite successful for JOBAR in terms of both performance and solution time. The third problem addressed with the DDO method in this thesis study is the scheduling crude oil operation (SCOO) problem. As in JOBAR, the SCOO problem is also a complex NP-Hard problem. Although there are different mathematical and meta/heuristic methods proposed for the SCOO problem in the literature, the number of studies dealing with a real-sized SCOO problem is very few. In this study, the SCOO problem was approached and solved with DDO methodology. First of all, by using an event-based mathematical model in the literature, the small sizes SCOO problem was solved for a limited planning period. Then, the result obtained from the mathematical model was analyzed using statistical methods and the Apriori algorithm, one of the most frequently used data mining methods in the literature. A new heuristic method has been proposed for the SCOO problem using the parameters obtained as a result of the data coming from previous stage analysis. The proposed heuristic method has been applied for different planning periods and its performance has been tested. The results obtained show that the proposed approach according to DDO methodology is successful. The last problem addressed in this thesis is the data-driven multi-criteria group decision making (MCGDM) problem. In MCGDM problems, two parameters directly affect the outcome of the decision process. These are the individual effects of the evaluations made by group members on the group decision, that is, the weights of the decision makers and the weights of the evaluation criteria. In this study, a flow is proposed in which the weights of decision makers will be obtained from historical data in accordance with the proposed DDO methodology. In the proposed approach, first of all, the performance of the group members in the past was evaluated and their weights were estimated for the current decision problem. Artificial neural networks were used in this determination process. The proposed methodology was applied on the created synthetic data set and the results were shared. In summary, in this thesis study, a new methodology has been proposed that determines the limits and flow of the DDO approach in the literature. It is aimed to use this proposed methodology in the analysis of the existing literature and to guide future DDO studies. In the continuation of the thesis, the proposed DDO methodology was applied to picking travel time prediction, joint order batching, assigning and routing, crude oil operation planning and multi-criteria group decision making problems, respectively, and the obtained results were shared. The results obtained show that successful results are obtained when the proposed DDO methodology is used as a solution approach for problems.
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ÖgeÇevik yazılım geliştirme projelerinde kritik başarı faktörlerinin modellenmesi: Çevik projelerin Türkiye'deki uygulamaları(Lisansüstü Eğitim Enstitüsü, 2024-06-04)Son yıllarda hızla gelişen teknoloji ile değişen müşteri ihtiyaçlarına kaliteden ödün vermeden hızlı bir şekilde cevap vermek çok önemlidir. Günümüzde, piyasada her ihtiyaç duyulan ürünün muadili bulunmaktadır. Ürünleri, muadillerinden farklılaştıran bir çok özellik örneğin müşteri odaklılık, ihtiyaçlara hızlı cevap verebilme, kalite ve güvenilirlik gibi kavramlar sadece müşteri memnuniyetini artırmakla kalmaz aynı zamanda tüm paydaşların memnuniyetini artıracaktır. Yazılım geliştirmede kullanılan proje yönetimi metodolojileri de teknolojik gelişmelerle değişeme uğramaktadır. Önceki yıllarda daha çok şelale yöntemleri diye adlandırdığımız klasik yazılım geliştirme metodolojileri kullanılırken günümüzde çevik yazılım proje yönetimi yaklaşımları tercih edilmektedir. Çevik proje yazılımı, hızla değişen gereksinimlere uyum sağlayabilmek için esnek ve dinamik bir yaklaşım sunan bir proje yönetim metodolojisidir. Kısa geliştirme döngüleriyle çalışan ekipler, müşteri geri bildirimlerini doğrudan kullanarak yazılım geliştirme sürecini yönlendirir. Böylece yazılımın kalitesi, müşteri memnuniyeti ve proje verimliliği artar. Sürekli iyileştirme ve işbirliğini teşvik eden bu metodoloji, özellikle belirsizlik ve sürekli değişimle karşılaşılan sektörlerde projelerin başarılı bir şekilde yürütülmesine yardımcı olmaktadır. Bu nedenle çevik yazılım geliştirme metodolojileri son yıllarda müşteri ve iş gereksinimlerini hızlı ve etkili bir şekilde karşılamak amacıyla giderek daha popüler hale gelmektedir. Yazılım gereksinimlerindeki belirsizlikler ve değişiklikler, şirketleri yazılım geliştirme projelerinde daha çevik olmaya zorlamaktadır. Günümüzde çevik bir ortamda şirketler ilerleyebilmek için projelerine çevik yazılım geliştirme metodolojilerini entegre etmektedir. Ancak çevik metodolojinin uygulanma şekli projenin başarısını belirleyebilir. Bu tez kapsamında, çevik uygulayıcıların bakış açısından çevik projelerin başarısını etkileyen kritik başarı faktörleri ve çevik başarı ölçütlerinin belirlenmesi amaçlanmaktadır. İlk olarak Çevik Manifesto, Çevik İlkeler ve Scrum Kılavuzu'nun detaylı incelenmesiyle kapsamlı bir sistematik literatür taraması yapılarak Çevik Yazılım Proje Başarı Modeli geliştirilmiştir. İkinci olarak altı çevik proje uygulayıcısı ile bire bir görüşmeler yapılarak kritik başarı faktörleri ve çevik başarı ölçütleri güncellenmiş ve ardından model üzerinde fikir birliğine varmak için katılımcılarla grup toplantısı yapılarak nihai Çevik Yazılım Proje Modeli geliştirilmiştir. Modelde çevik başarı ölçütleri olarak süreç verimliliği, sürdürülebilir yazılım ürün kalitesi ve paydaş memnuniyeti belirlenmiştir. Kritik başarı faktörleri ise müşteri faktörleri, ekip faktörleri, organizasyonel faktörler, çevik süreç faktörleri, teknik faktörler ve proje faktörleri olarak altı ana başlıkta tanımlanmıştır. Sonrasında kritik başarı ölçütleri ile çevik başarı ölçütleri arasındaki ilişki literatürden desteklenerek Çevik Yazılım Proje Modeli tanımlanmıştır. Ayrıca kritik başarı faktörlerinin ve çevik başarı ölçütlerinin alt başlıkları da tanımlanmış ve bu alt başlıkları tanımlayan soru ölçekleri geliştirilmiştir. Tasarlanan Çevik Yazılım Proje Anketi çevik proje başarısını etkileyen kritik başarı ölçütlerinin belirlenmesinde kullanılabilecektir. Geliştirilen modeli test etmek için çevik yazılım proje yönetimi deneyimi olan 596 katılımcıdan veri toplanarak proje başarısına etki eden faktörler belirlenmiştir. Veri analizi için yapısal eşitlik modeli kullanılmıştır. IBM Amos 20.0 ve IBM SPSS Statistics versiyon 28 yazılım programlarından faydalanılmıştır. İlk olarak açıklayıcı faktör analizi kullanılarak kritik başarı ölçütlerinin faktör yapısı ortaya çıkarılmış ve modelde belirlenen faktör sayısı ile aynı sayıda faktör elde edilmiştir. Daha sonraki adımda doğrulayıcı faktör analizi ile belirlenen faktörlerin teorik yapısının geçerliliği gösterilmiştir. En son adımda ise kritik başarı faktörleri ile çevik başarı ölçtüleri arasındaki ilişki yapısal eşitlik modellemesi ile test edilmiştir. Sonuçlara göre müşteri faktörleri, ekip faktörleri, çevik süreç faktörleri ve proje faktörlerinin süreç verimliliği, sürdürülebilir yazılım ürün kalitesi ve paydaş memnuniyeti açısından çevik proje başarısının önemli belirleyicileridir. Kritik başarı faktörleri arasında müşteri faktörleri ve çevik süreç faktörleri diğer faktörlere kıyasla süreç verimliliği, sürdürülebilir yazılım ürün kalitesi ve paydaş memnuniyetini etkileyen güçlü belirleyicilerdir. Bu çalışma ile teorik ve pratik çıkarımlar sunulmuş ve olası gelecek çalışmalar için önerilerde bulunulmuştur. Geliştirilen Çevik Yazılım Proje Başarı Modeli, projelerin başarılı bir şekilde yürütülmesi için kritik olan faktörlerin belirlenmesinde önemli bir rol oynamaktadır. Bu model, çevik yazılım geliştirme süreçlerinin daha iyi anlaşılmasını sağlamakta ve çevik metodolojilerin uygulanmasındaki etkinliği artırmak için yol gösterici olmaktadır. Araştırmanın bulguları, özellikle müşteri ve süreç odaklı yaklaşımların çevik projelerde başarıya ulaşmada kilit olduğunu vurgulamaktadır. Bu faktörler, projelerin zamanında ve bütçe dahilinde tamamlanmasını, aynı zamanda yüksek kaliteli yazılım ürünlerinin teslim edilmesini sağlamakta büyük bir rol oynamaktadır. Ayrıca çevik metodolojilerin daha etkili bir şekilde uygulanabilmesi için çevik uygulayıcılar ve proje yöneticileri için somut adımlar önermektedir. Bunun yanı sıra tasarlanan Çevik Yazılım Proje Anketi, gelecek çalışmalarda çevik proje yönetimi uygulayan farklı sektörlere de uygulanarak sektöre özel kritik başarı faktörleri belirlenmesine yardımcı olacaktır.