Endüstri Mühendisliği Ana Bilim Dalı Tez Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/419

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Determination of the characteristics of building materials for optimal thermal insulation and selection of building material
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2023) Kılıçarslan, Mustafa Özgür; AGÜ, Fen Bilimleri Enstitüsü, Endüstri Mühendisliği Ana Bilim Dalı
The escalating urgency of climate change demands innovative approaches to energy conservation, particularly in the realm of building construction, known for substantial energy consumption and greenhouse gas emissions. This research delves into transformative strategies for enhancing energy efficiency in office buildings, with a concentrated analysis of the implementation of advanced building materials and state-of-the-art construction methodologies. Utilizing OpenStudio, a cutting-edge energy modeling software tool from the U.S. Department of Energy's National Renewable Energy Laboratory, this study quantitatively evaluates the energy-conserving potential of various avant-garde materials and construction techniques. The investigation is anchored around a case study of an office building in Ankara, Turkey, serving as a representative model for exploring diverse scenarios. These scenarios encompass the integration of high-performance framing, airtight construction, materials with superior thermal resistance properties, and advanced glazing systems. The research meticulously assesses each scenario with the aim of delineating the configurations that most significantly reduce energy consumption. The results reveal that specific combinations of advanced techniques and materials can lead to substantial reductions in energy use, thereby contributing profoundly to global efforts in mitigating climate change impacts. The conclusion emphasizes the necessity for widespread adoption and standardization of these energy-efficient practices in the construction industry, proposing them as pivotal contributors to the broader environmental sustainability movement.
Navigating BIST100 investments through symbolic aggregateapproximation clustering: Insights for investors / Sembolik toplam yaklaşım kümelemesi yoluyla BIST100 yatırımlarında yön bulma
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2024) Nalici, Mehmet Eren; 0000-0002-7954-6916; AGÜ, Fen Bilimleri Enstitüsü, Endüstri Mühendisliği Ana Bilim Dalı
Market stakeholders, including traders and investors, strive to forecast stock market returns for informed decision-making. Computational finance employs various tools such as machine learning techniques to analyse extensive financial datasets to provide predictive insights for investors. Among all those techniques, clustering is one of the most well-known and used machine learning methods to reveal hidden patterns from unlabelled data. This study aims to help investors make more robust decisions by autonomously identifying companies that may exhibit similar price movements. In our study, with the model developed based on the Symbolic Aggregate Approximation (SAX) method, BIST100 companies are divided into clusters of various numbers and various scenarios are developed for investors from different perspectives such as risk minimization and strategic investment. The SAX clustering method is employed for analysing share movements. Moreover, dendrogram tree graph is used to analyse the clustering of different SAX combinations.
Electric vehicle charging station location decision in Türkiye
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2023) Gülbahar, İbrahim Tümay; 0000-0001-9192-0782; AGÜ, Fen Bilimleri Enstitüsü, Endüstri Mühendisliği Ana Bilim Dalı
Electric vehicles are now regarded as one of the best and greenest replacements for internal combustion engine vehicles. For the widespread use of electric vehicles, the construction of the vehicle charging network and, in particular, the choice of the appropriate site for the charging stations, are viewed as critical issues. The majority of studies on the topic concentrate on well-known locations like city centers, shopping malls, and airports. Because there are so many alternative charging stations, even though these and comparable locations are regularly used in everyday life, they can usually provide an appropriate solution to the daily charging need. For intercity travel, it is impossible to find enough charging stations, especially on highways. To choose the position of electric vehicle charging stations on highways, a decision model has been suggested in this study. The anticipated number of electric vehicles in Türkiye over the next few years is projected in order to acquire a realistic approach to the location of charging stations, and this amount is employed as a significant input in the facility positioning model. The best places for charging stations on state highways that can meet customer demand were then identified using an optimization technique. The suggested model selects the most suitable locations for charging stations and the number of chargers that should be installed there while also making sure that drivers of electric vehicles on highways don't run into charging issues.
Pricing strategies under price protection, mid-life returns and end-of-life returns
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2022) Yıldız, Barış; AGÜ, Fen Bilimleri Enstitüsü, Endüstri Mühendisliği Ana Bilim Dalı
In this thesis, we examine a selling environment where a manufacturer-controlled retailer and an independent retailer sell a slow-moving A item. The manufacturer offers the independent retailer price protection against reductions in the wholesale price. The price set by the independent retailer is assumed to be determined by Retail Fixed Markdown (RFM) policy. The manufacturer adopts a periodic-review pricing strategy and each retailer observes price-dependent stochastic demand. We employ Multinomial Logit (MNL) models to forecast customers' preferences based on retail prices. We construct stochastic programming models to determine the manufacturer's pricing strategy in the presence of four distinct price commitment contracts which differ in the supplementary privileges combined with price protection. We also propose a variant Stochastic Dual Dynamic Programming (SDDP) algorithm to determine the manufacturer's approximately optimal pricing strategy by getting around three curses of dimensionality. We observe the impact of critically important contractual parameters on the price, the market shares and the expected true profits. We also evaluate the performance of the proposed algorithm and compare the price commitment contracts in terms of the contractual parameters for which it is crucial to choose a compromise value to ensure high enough profitability for both retailers.
A novel approach based on bagging and boosting for imbalanced classification problems
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2022) Pınar, Muhammed Şafak; 0000-0002-9022-0829; AGÜ, Fen Bilimleri Enstitüsü, Endüstri Mühendisliği Ana Bilim Dalı
Classification algorithms are employed in a wide range of real-world problems such as obstacle detection, fraud detection, medical diagnosis, spam detection, speech recognition, image processing, intrusion detection, and so forth. However, it is not always an easy task to propose a legitimate classifier. For a classification task, there are numerous limitations of datasets. One of the most confronted limitations in real-world classification tasks is skewed class distribution, also called the class imbalance problem. When learning is employed in class imbalanced datasets without incorporating appropriate adjustments into the existing algorithms, minority classes are mostly misclassified. This study introduces a novel classification algorithm that outperforms previous studies on benchmark datasets used for the class imbalance problem. The presented novel algorithm, namely, BagBoost, involves aggregating modified bagging and modified boosting algorithms to increase the visibility of minority class instances. The state-of-the-art algorithms in the classification of imbalanced datasets are investigated. The results of the best existing algorithms are compared with the proposed algorithm using benchmark datasets. Results show that BagBoost is a better classifier than commonly used classification algorithms in the literature for benchmark datasets according to F-measure and G-mean scores.
Development of models and solution methodologies for tree of hubs location and arc capacitated hub location problems
(Abdullah Gül Üniversitesi Fen Bilimleri Enstitüsü, 2022) Kayışoğlu, Betül; AGÜ, Fen Bilimleri Enstitüsü, Endüstri Mühendisliği Ana Bilim Dalı
In this dissertation, we study two different extensions to hub location problems, namely, Multiple Allocation Tree of Hubs Location Problem (MATHLP) that result from incorporating a tree topology requirement for the hub network and Multiple Allocation Arc Capacitated Hub Location Problem (MACHLP) that result from imposing capacities on the arcs. We consider both problems in a multiple allocation framework and try to minimize total flow cost by locating p hubs. Unlike most studies in the literature that use complete networks with costs satisfying the triangle inequality to formulate the problems, we define the problems on non-complete networks and develop a modeling approach that does not require any specific cost and network structure. Our proposed approach provides more flexibility in modeling several characteristics of reallife hub networks. We solve the proposed models using CPLEX-based algorithm and Gurobi-based algorithm with NoRel heuristic. For MATHLP, we develop Benders decomposition-based heuristic algorithms and for MACHLP, we develop a heuristic algorithm based on simulated annealing. We conduct computational experiments using problem instances defined on non-complete networks with up to 500 and 400 nodes for MATHLP and MACHLP respectively. The results indicate that the proposed solution methodologies are especially effective in finding good feasible solutions for large instances.
PARALLEL MACHINE SCHEDULING IN THE FACE OF PROCESSING TIME UNCERTAINTY
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2017) BEKLİ, Rahime Şeyma; AGÜ, Fen Bilimleri Enstitüsü, Endüstri Mühendisliği Ana Bilim Dalı
Competition in today’s business and production world leads the companies to generate schedules that increase productivity and decrease manufacturing cost. However, most of the schedules cannot be executed exactly because of the unexpected disruptions such as machine breakdowns, order cancellations and so forth. In order to develop disruption resistant schedules, robust scheduling subject has gained interest among researchers. In this study, we consider a parallel machine environment with processing time uncertainty. The performance measure is taken as the completion time of the last job. The uncertainty is modeled by discrete set of scenarios. An integer programming model that can handle small problems is proposed. We observe that this model cannot manage large problems. To alleviate this difficulty, we propose to decrease number of scenarios selected for model. Next, we apply dual decomposition method in order to solve many smaller problems rather than a large problem. Large problems cannot be handled by this method either. This is why; we alter dual decomposition method by relaxing and develop a new heuristic. Also we propose a hybrid tabu search algorithm to solve the large problems.The results show that, the proposed heuristics; selecting scenario approach and tabu search algorithm perform well for the parallel machine scheduling problems.
PRIORITY REGIONS FOR DEVELOPMENT ASSISTANCE FOR HEALTH: AN EVIDENCE-BASED APPROACH
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2021) Chbani, Zakaria; AGÜ, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümü
Eligibility and allocation criteria of development assistance for health have received much attention in the last years. Critical issues have been raised on the usage of GNI per capita (GNIpc) as a sole indicator for this task. The major critics emphasize the GNIpc overlooks the changes in characteristics of middle-income countries (MICs). These countries now have the highest proportion of poor people and disease burden, combined with significant inequalities. Various alternative frameworks have been suggested that tried to avoid the issues GNIpc failed to take into account. This thesis attempts to build on previous works and introduce a data-driven methodology of developing a framework that guides eligibility and aid allocation decisions. The framework combines health status measures (estimating the level of wellness and illness of a population) and measures of capacity of response to the disease burden. We use Disability Adjusted Life Years (DALYs) as a measure of health status. To determine the measures of capacity, the starting point was to assemble relevant indicators in the literature. Using these indicators, feature selection then allowed to choose a minimal set of discriminative ones. Finally, an aggregate of chosen indicators enables ranking countries by order of priority. Comparing the framework with GNIpc and other frameworks show its potential usefulness. It is better than most other frameworks in targeting countries with a high disease burden and populations in extreme poverty. Moreover, it integrates some concerns other frameworks failed to address.
Scheduling the Turkish Super Football League
(Abdullah Gül Üniversitesi, 2019) BAYRAK, HASAN; AGÜ, Fen Bilimleri Enstitüsü, Endüstri Mühendisliği Ana Bilim Dalı; BAYRAK, HASAN
Sports entertainment was usually casual in the past, however, today’s sport has to be organized, marketed, and administered as a business because of economic size. Teams are making big investments to transfer new players. Broadcast rights of some competitions are being sold for hundreds of millions of dollars. The schedule is important for the organization's safety, game attendance, public interest, broadcasters, and advertisers and fairness. However, creating a fair and appropriate schedule is not easy because of the different needs of the stakeholders and the needs often conflict with each other. When the number of teams in the tournament increases, it may not be possible to reach the optimal solution with traditional methods. In this thesis, we have found a number of pattern elimination methods that we call “ladder patterns”. Moreover, with the help of ladder patterns, we developed a 5-Step approach that can able to solve a very complicated problem easily and quickly. At the result, a better schedule created for the Turkish Super Football League
The effect of game-based learning in lean production and lean six sigma training
(Abdullah Gül Üniversitesi, 2019) KURT ÖZDEN, BURCU; AGÜ, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümü; KURT ÖZDEN, BURCU
Nowadays, business leaders and managers are highly concerned about the sustainability of their success. In the business world, there are many leaders, companies, products, and even industrial areas that have a short-term reputation. What is the key to be at the top constantly and to keep the competition ability at a high level in such an environment? Most small and big companies try to find answers to this question. For this reason, they try to implement different strategies and innovations to improve their process and standards. Accordingly, we face the Lean Production System, Six Sigma, and Lean Six Sigma concepts. Japanese employees of Toyota Company developed the Lean Production system after the Second World War. It is a methodology “that is based on the elimination of all wastage in the enterprise and respect for human,” The leading position of Japanese companies with their works has attracted the attention of American companies in particular. The Six Sigma method, which includes quality improvements to meet the expectations of the customer, was implemented under the leadership of Motorola, which was an American Company. In the 2000s, the Lean Six Sigma management system, which simultaneously used Lean Production techniques with Six Sigma techniques, has emerged. Lean Six Sigma is a management philosophy that aims to reduce waste, increase productivity, and improve product quality in line with customer demands and expectations. One of the most critical elements in Lean Production and Lean Six Sigma systems is to respect human beings and to value people. For Lean Production and Lean Six Sigma systems to be successful, when these techniques are no longer mandatory and become a company culture, success is sustainable, the right techniques should be provided with the ii right training for these systems to be able to become the culture. Since companies implementing Lean Production and Lean Six Sigma system cannot make the right choice in training and cannot make their employees adapt to this culture, new improvement systems can cause misfortune of the companies. This study, which aims to solve the difficulties of the companies in the selection of training with a technical point of view, will contribute to both the applications in the production facilities and the academic literature. In this study, the Lean Production System, Six Sigma Method, Lean Six Sigma Method are explained in general, new lean game is designed and the effect of using gamebased learning techniques on Lean Production and Lean Six Sigma training is discussed. The aim of this thesis is: 1. To analyze the effects of game-based learning in training that use Lean Production and Lean Six Sigma management system on learning and 2. To guide the companies on selecting the right training techniques.
Flow-based p-hub median interdiction problem
(Abdullah Gül Üniversitesi, 2017) BENLİ, ABDULKERİM; AGÜ, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümü; BENLİ, ABDULKERİM
There are two players in a network interdiction problem: a network user who wishes to operate a system optimally, and an opponent/interdictor who tries to prevent the system from operating optimally. Interdiction problems can be modeled as a bi-level min-max or max-min problem in the Stackelberg Game logic. In this thesis, we handle the interdiction problem within the context of the p-hub median problem. The network user solves the problem of locating p hubs to minimize the cost associated with operating the network. In response to the network user, the interdictor tries to maximize network user’s cost by removing hub characteristics of effective hubs with its limited resources. The p-hub median problem of the network user is modeled on the flow-based networks. The model we develop in this study, unlike the previous literature, does not require the complete network and enables one to find the correct solution in cases that do not provide triangle inequality between nodes. Therefore, this new model provides significant advantages regarding the solution times and modeling capabilities compared to the facility interdiction models offered by the literature.

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