Graduate School of Science and Engineering

Permanent URI for this collectionhttps://hdl.handle.net/10679/9878

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Matheuristic for multi-period home healthcare routing and scheduling problem : a real life case study
(2023) Selçuk, Yağmur Selenay; Göktürk, Elvin Çoban; Koyuncu, Burcu Balçık; Göktürk, Elvin Çoban; Yıldırım, U. M.; Department of Industrial Engineering; Selçuk, Yağmur Selenay
The aging population's exponential growth has exerted considerable pressure on healthcare systems, necessitating the provision of enhanced healthcare services tailored to meet the unique needs of older adults, individuals with disabilities, and chronic patients. As a result, healthcare providers aim to offer varying healthcare services to patients in their homes, with the objective of improving the quality of care and optimizing the management of health systems. This thesis studies a home healthcare routing and scheduling problem (HHCRSP) over a multi-period planning horizon, considering caregivers' lunch breaks, prior service type, and patients' preferences. In this HHCRSP, some patients needing blood draw, and they have to be visited before noon, guaranteeing the corresponding caregiver's return to the hospital's lab before noon. Additionally, patients have preferred time windows for each day, corresponding to times due to reasons such as the need for someone to support them with the patients. In the thesis, the objective function is minimizing the total routing costs of caregivers' vehicles and the penalty costs incurred when patients cannot receive services within their preferred time windows. Our study is motivated by a real-life hospital that provides home healthcare service (HHCS). We develop a mixed-integer linear programming (MILP) model and propose a simulated annealingbased matheuristic algorithm (SAMA) that decomposes the original problem into two phases. Furthermore, we conduct a comparative analysis of the k-nearest-neighbour (KNN) algorithm, utilizing various k values to predict service times. The results of our study demonstrate significant improvements, up to 98.64% in cost-effectiveness achieved by the MILP in small-sized instances, and 98.58% by the SAMA in medium and large-sized instances, compared to the existing system in the motivational hospital. The numerical analysis provides insights for healthcare providers and policymakers in their efforts to optimize HHCS.
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Multi-compartment inventory routing problem with flexible product types and fixed compartment sizes
(2023) Artarslan, Sena; Ekici, Ali; Ekici, Ali; Özener, Okan Örsan; Yakıcı, E.; Department of Industrial Engineering; Artarslan, Sena
This thesis introduces a \emph{Multi-Compartment Inventory Routing Problem} (MCIRP). The addressed problem aims to minimize the total traveling costs while ensuring customers are not out of stock with multiple products over the given planning time horizon. The distribution is made with a homogenous fleet of vehicles with flexible product types and fixed compartment sizes, where each compartment can accommodate all product types and has a fixed capacity. The supplier manages customer inventory levels by creating a distribution plan respecting the capacity restrictions of the compartments and customers. We examine the application of this problem with liquid products that can be partially delivered to customers with compartments that have debit meters. To address this complex problem, we propose a mathueristic method that combines Adaptive Large Neighborhood Search (ALNS) with mathematical models. The success of the solution method has been demonstrated by comparing it against a lower bound, that is flow formulation from the literature. Our results show that with the generated comprehensive and large-scale instances, our solution algorithm achieves only 18.13\% worse solutions than the conservative lower bound.
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Strategic global supply network planning under disruptions
(2023) Ekşi, Aybüke; Teksan, Zehra Melis; Teksan, Zehra Melis; Kayış, Enis; Taşkın, Z. C.; Department of Industrial Engineering; Ekşi, Aybüke
The effective management of disruptions in global supply chain networks has become a critical aspect of strategic decision-making, particularly owing to the increasing frequency of worldwide disasters. This study aims to research and identify strategies that supply chain managers can employ to mitigate the impact of disruptions on their supply chains. Specifically, we focus on strategic disruption planning for a global supply chain network, analyzing the consequences of multiple types of disruptions within a finite planning horizon segmented into discrete time periods. To address this, optimal decisions related to procurement, production, distribution, and demand satisfaction are determined using Mixed-Integer Linear Programming (MILP) models. These models are classified in two types: deterministic model, uses expected values, and stochastic model, which allows us to analyze the impact of various disruption scenarios. To ensure the realism of our approach, a performance comparison is conducted by incorporating a simulation approach on the best solutions, considering both deterministic and stochastic models. In addition, hypothesis testing is integrated to the simulation results to compare and identify the most cost-efficient model. This rigorous statistical analysis provides valuable insights into the relative performance of distinct models across diverse disruption scenarios. By providing insights into effective strategies for managing disruptions in global supply chain networks, this thesis makes a valuable contribution to the existing literature on supply chain management.
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Seismic performance of self-anchored tanks located in high seismic regions in Turkey
(2023) Koçraş, İrsen Nihan; Erkmen, Bülent; Erkmen, Bülent; Yılmaz, Taner; Yazgan, U.; Department of Civil Engineering; Koçraş, İrsen Nihan
This study examines the seismic performance of three unanchored industrial liquid storage tanks that are located in the Kocaeli industrial area, which has a significant earthquake risk. One of the tanks is an existing tank in an industrial facility located in the region, while the other two were designed to have different anchorage ratios. The finite element analysis software ABAQUS was used to build 3-D finite element models in order to assess the seismic performance of the tanks. The seismic behaviour of the tanks was studied by performing time-history analyses using the recorded earthquake records that were scaled for the tank location. The spring-mass model, on which the developed tank models are based, models liquid content as two-point masses that were spring-adjusted to the tank wall. This approach was based on American Petroleum Institute (API 650). Tank-liquid interaction, base uplift, tank sliding off its foundation, damage to the tank's wall shell plates as well as bottom base plates, and the tank earthquake performance were all analysed. In addition, the effects of different friction coefficients between tank base and its foundation on tank seismic performance were studied. The seismic analyses were carried out using data related to eleven earthquakes. This data was scaled to meet the seismic design spectrum for the tank location and chosen in compliance with the Turkish Building Earthquake Code. Tank base sliding, base uplift and material damage were monitored to assess potential tank damages. The main objective of the study was to investigate the effects of tank anchorage ratio on seismic performance of unanchored tanks located in high seismic regions. This was achieved by performing time history analysis of the tanks using 11 earthquake records. Also, tank base uplift and sliding over the foundation failure modes were also evaluated on the basis of the effect of anchorage ratio on tanks. The second objective of the research was to examine the impact of friction between the tank base plate and tank foundation on tanks seismic performance. For this purpose, the tank's seismic performance was evaluated by considering two different friction coefficients. The findings of this study contribute new and valuable insights into the existing literature on the seismic performance of unanchored tanks and the effects of anchorage ratio on their seismic performance. In addition, the study provided a better understanding of the effects of unanchored tanks base friction on their seismic performance. Consequently, these findings will contribute to procedures used for seismic design of unanchored tanks and enhance the seismic performance of tanks at highly seismic regions.
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Mahalle ölçeğinde sosyal sürdürülebilirlik ve yer aidiyeti etkileşiminin; yaşam kalitesi, kamusal olanaklara erişim ve kentsel dönüşüm açısından araştırılması : Bahçelievler Merkez Mahallesi
Göksoy, Senanur Kevser; Hacıhasanoğlu, Orhan; Hacıhasanoğlu, Orhan; Özorhon, Güliz; Özsoy, A.; Department of Architecture
This study aims to explore the concepts related to social sustainability at the neighborhood scale and develop measurement parameters by examining the discovered concepts in a more general context. Focusing on the social sustainability concept that has a strong relationship with the place attachment identified through a literature review, the study is further deepened by associating it with quality of life, neighborhood public spaces and amenities, and accessibility parameters. Bahçelievler Merkez neighborhood, which was established during the rapid urbanization process of Istanbul in the 1960s and intensive urban transformation in recent years, was selected for field research. The field research consists of two parts. The first part used a methodology focused on social sustainability as well as related concepts such as social interaction, satisfaction and accessibility to amenities to explore the place attachment of people living in the neighborhood. This research was supported by questionnaires and in-depth interviews.The issue of urban transformation is investigated both in survey questions and in-depth interviews. In the second part of the research, new mappings were prepared to assess quality of life and evaluate accessibility and walkability. Transportation circles were drawn around public spaces, and the extent to which these circles cover the neighborhood fabric was investigated. In the second part of the research, new maps were created to assess quality of life, accessibility, and walkability. Public spaces were the focal point, and accessibility circles were drawn to determine the extent to which these circles covered the neighborhood fabric. This method was combined with integration maps, which tested the density of urban transportation networks, allowing for a comprehensive analysis of the accessibility of public spaces and quality of life in the neighborhood. Observations were conducted in fifteen parks within the neighborhood to obtain behavioral maps. The results of the thesis show that there is a high demand for urban transformation projects in the Bahçelievler Merkez neighborhood. Therefore, promoting in situ transformation is essential to strengthen social sustainability. The long-term residence of neighborhood residents increases their attachment to the neighborhood. In addition, accessibility of the neighborhood affects place attachment and place satisfaction. Surveys, in-depth interviews, and observations indicate a positive relationship between park use and sense of belonging - attachment in the neighborhood. The design of parks and urban furniture used is important for increasing social interaction.
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An assembly line balancing problem with identical parallel stations and worker capability
Bilgiç, Tunahan; Albey, Erinç; Albey, Erinç; Önal, Mehmet; Güler, M. G.; Department of Industrial Engineering
This study focuses on the balancing problem of the assembly lines in Vestel Electronics' TV production facility. TV production includes numerous tasks, and complex precedence relationships increase the problem's complexity. In assembly lines, the production is made in a given cycle time, and parallel stations can be opened to increase the line's efficiency. The tasks of the stations are performed by the workers. This study aims to assign the workers to the stations depending on their capabilities and not exceeding the cycle time of the line, and increasing the production rate by opening identical parallel stations. A mathematical model is formulated to solve the assembly line balancing problem type-1, which includes opening parallel stations and assigning the workers to the stations based on worker skills. Moreover, company-specific constraints are added to the model to satisfy the production conditions. Depending on the company's requirements, an alternative meta-heuristic algorithm is developed to receive more rapid solutions than the mathematical model. Computational experiments show that the developed algorithm can be used as a decision-support tool to satisfy the production metrics.
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Drone routing for post-disaster damage assessment in a remote communication setting
Yücesoy, Ecem; Koyuncu, Burcu Balçık; Koyuncu, Burcu Balçık; Adam, Evşen Yanmaz; Göktürk, Elvin Çoban; Albey, Erinç; Yıldırım, U. M.; Department of Industrial Engineering
After a disaster event, obtaining fast and accurate information about the damaged built-in structure is crucial for planning life-saving search and rescue operations. This study focuses on determining drone routes for damage assessment in disaster-affected areas, aiming to maximize information gathering within a specified time frame while minimizing response time. Here, response time refers to the duration required for the collected data from a specific area to reach the operation center, where crucial decisions regarding disaster response are made. Drones can remotely transmit collected data from grids at recharge stations, allowing for quick assessment and route optimization. In order to construct the routes, we propose a path-based multi-objective mathematical model. However, since obtaining good quality solutions quickly is not possible for large instances with the mathematical model, we propose neighborhood search-based metaheuristics, namely, variable neighborhood descent, variable neighborhood search, and adaptive variable neighborhood search. The proposed methods consider grids' criticality levels, drones' limited battery capacity, and communication-related factors. In the multi-iteration setting that we consider, the transmitted information plays a crucial role in predicting damage levels of unvisited grids, influencing subsequent route construction by re-prioritizing these areas accordingly. The comprehensive numerical analysis compares algorithm performance under different settings and instances, demonstrating the potential of this approach in optimizing drone routing for effective disaster response operations.
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Stock trend prediction and portfolio optimization
Pekşen, Deniz; Özener, Okan Örsan; Özener, Okan Örsan; Özener, Başak Altan; Çelikyurt, U.; Department of Industrial Engineering
This thesis aims to predict trend movement of closing price of stock and to maximize investment portfolio by utilizing the predictions. In this context, the study aims to define a stock portfolio strategy from models created by using Logistic Regression, Gradient Boosting and Random Forest Methods. Recently, predicting the trend of stock price has gained a significance role in making buy and sell decisions and generating returns with investment strategies formed by machine learning basis decisions. There are plenty of studies in the literature on the prediction of stock prices in capital markets using machine learning methods but most of them focus on closing prices instead of the direction of price trend. Our study differs from literature in terms of target definition. Our target definition is a classification problem which is focusing on the market trend in next 20 trading days. To predict trend direction, fourteen years of data were used for training. Following three years were used for validation. Finally, last three years were used for testing. Training data are between 2002-06-18 and 2016-12-30. Validation data are between 2017-01-02 and 2019-12-31. Testing data are between 2020-01-02 and 2022-03-17. We determine Hold Stock Portfolio, Best Stock Portfolio and USD-TRY Exchange rate as benchmarks which we should outperform. We compared our machine learning basis portfolio return on test data with return of Hold Stock Portfolio, Best Stock Portfolio and USD-TRY Exchange rate. We assessed our model performance with the help of roc-auc score and lift charts. In our study, we use logistic regression, Gradient Boosting and Random Fores with grid search approach to fine-tune hyper-parameters. As a result of the empirical study, the existence of uptrend and downtrend of five stocks could not be predicted by the models. When we use these predictions to define buy and sell decisions in order to generate model-based-portfolio, model-based-portfolio fails in test data-set. It was found that Model-based buy and sell decisions generated a stock portfolio strategy whose returns can not outperform non-model portfolio strategies on test data-set.
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Considering arguments in human-agent negotiations
Doğru, Anıl; Aydoğan, Reyhan; Aydoğan, Reyhan; Yıldız, Olcay Taner; Özgür, A.; Department of Computer Science
Autonomous negotiating agents, which can interact with other agents, aim to solve decision-making problems involving participants with conflicting interests. Designing agents capable of negotiating with human partners requires considering some human factors, such as emotional states and arguments. For this purpose, we introduce an extended taxonomy of argument types capturing human speech acts during the negotiation and propose an argument-based automated negotiating agent that can extract human arguments from a chat-based environment using a hierarchical classifier. Consequently, the proposed agent can understand the received arguments and adapt its strategy accordingly while negotiating with its human counterparts. We initially conducted human-agent negotiation experiments to construct a negotiation corpus to train our classifier. According to the experimental results, it is seen that the proposed hierarchical classifier successfully extracted the arguments from the given text. Moreover, we conducted a second experiment where we tested the performance of the designed negotiation strategy considering the human opponent's arguments and emotions. Our results showed that the proposed agent beats the human negotiator and gains higher utility than the baseline agent.
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District morphology impact on environmental control, energy performance, indoor and outdoor comfort
Dal, Ayşe Özlem; Aşrafi, Turaj Arda; Aşrafi, Turaj Arda; Sağlam, Neşe Ganiç; Taşçı, G. G.; Department of Architecture
Technological developments have provided significant architectural changes in the developing world in the last century, as in all other sectors. Nowadays, it cannot be denied that all the changes cause challenges like increasing carbon emissions, climate change, scarcity of resources, depletion of fossil fuels, population growth, and urbanization. Therefore, providing an efficient living environment, economy, and society are essential current targets for countries. The European Commission, which provides regulations to decrease the negative impacts of the building sector, is shifting the attention from the building level to the urban level. Since urbanization and the growth rate of the population is increasing rapidly, all countries like Turkey, need to take the responsibility of decreasing the adverse impacts of building stock. This study aims to evaluate the impact of district morphology on environmental control, energy performance, and comfort levels in different climatical regions of Turkey. The prominent uniqueness of the thesis is the integration of three key parameters. These are examining environmental control parameters for the urban morphologies' sun and wind potential, analyzing indoor and outdoor thermal conditions, also indicating energy performance calculations. Therefore, it is aimed to do inclusive research by considering the environment, energy, and human being together. In the methodology, a case study urban block is selected to analyze in Ankara, Turkey. It has 76,183 m² and includes approximately 2,400 dwellers. The case study urban block investigated two different climatical conditions. Ankara has a temperate-dry climate, and İzmir is selected as the representative city of a hot-humid climate. The research methodology consists of three main steps: arranging parametric modeling, conducting performance simulations, and making a comparative assessment. The parametric modeling includes data collection of the site, determining urban scenario variables, and organizing alternative urban block models. Regarding the urban scenario parameters, 28 alternative urban models are arranged. The performance analysis and comparative assessment process are conducted in Grasshopper/Rhinoceros software using a variety of plug-ins like Ladybug 1.4.0, Honeybee 1.4.0, Butterfly 0.0.05, and ClimateStudio1.1.9. The results show that all the beneficial alternatives have a 135° orientation regarding solar radiation analysis, energy performance simulations, computational fluid dynamic analysis outdoor and indoor comfort simulations. The best optimum urban scenario for Ankara belongs to the alternatives of Irregular X-Axis. The model produces the highest electricity thanks to solar streets and provides the lowest final district T.P.E. The final district T.P.E. of Ankara's best district model is 33% lower than the current value. The best optimum urban model for İzmir belongs to the Detached category. The model provides the lowest final district T.P.E, nearly half of the existing situation's final district T.P.E. Eventually, comprehensive research is needed to ensure the best optimum district morphology regarding the urban context and climate. Conducting all important parameters as much as possible highlights the best solution regarding environment, energy, comfort, and renewables.
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Design of BalanSENS: Sensory, anterior and feedback balance analysis, rehabilitation, and support robotic platform
Ersoy, Tuğçe; Ünal, Ramazan; Hocaoğlu, E.; Hocaoğlu, E.; Uğurlu, Regaip Barkan; Barkana, D. E.; Tarakçı, D.; Ünal, Ramazan; Department of Mechanical Engineering
In the thesis, the design and development evaluation of BalanSENS toward the realization of the Integrated Balance Rehabilitation (I-BaR) framework is presented. BalanSENS is designed to encourage active participation by integrating multi-sensory information with the co-improvement of sensory and motor functions. Moreover, it can offer individual rehabilitation design with the integration of three phases. The first phase provides foot-ankle muscle activation and movement sensation development. In the second phase, sensory weighting skills and upper extremities independence can be improved by using multi-sensory input. In the last/stepping phase, walking parameters are aimed to be improved with modulated distance. The parallel manipulator is designed through simulations to determine actuation properties and analyze the load-bearing capacity and feasibility of the materials. Drawing from simulation outcomes, an operational 3 DoF platform is constructed to demonstrate their design suitability for the I-BaR framework. Furthermore, design evaluations demonstrated promising results in quantifying force and real-time data monitoring during the passive ankle preparation phase.
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High throughput photon matching algorithm for QKD and its RTL implementation
İpek, Seçkin; Uğurdağ, Hasan Fatih ; Uğurdağ, Hasan Fatih; Durak, Kadir; Serif, T.; Department of Computer Science
While means to communicate increase with the technological advancements, at the same time the methods to steal information during communication also advance. With these advancements, the need for a method to communicate without anyone stealing the shared information increases. Most of the communication methods used today are classical information channels, which involve sending data that can be very easily eavesdropped. On the other hand, quantum information channels use the principles of quantum mechanics that make the information secure even if a third party tries to listen using the best technology. One of the quantum communication methods is Quantum Key Distribution (QKD). With this method, an entangled photon source generates a few million photon pairs in a second and sends the photons of each pair to two terminals. Afterwards, these two terminals match the photons they received. This thesis explains the development process of a high throughput matching algorithm and its implementation in Python and Verilog. The algorithm developed has 3 parts, namely, finding entangled photon starting time for each terminal, entangled photon start time difference for the two terminals, and matching the photons. Our algorithm's Verilog implementation can match 30 million photon pairs per second and can be easily tuned for different protocols.
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Finsentiment : predicting financial sentiment and risk through transfer learning
Ergün, Zehra Erva; Sefer, Emre; Sefer, Emre; Yıldız, Olcay Taner; Yeniterzi, R.; Department of Computer Science
There is an increasing interest in financial text mining tasks. Significant progress has been made by using deep learning-based models on generic corpus, which also shows reasonable results on financial text mining tasks such as financial sentiment analysis. However, financial sentiment analysis is still a demanding work because of insufficiency of labeled data for the financial domain and its specialized language. General-purpose deep learning methods are not as effective mainly due to specialized language used in the financial context. In this study, we focus on enhancing the performance of financial text mining tasks by improving the existing pretrained language models via NLP transfer learning. Pretrained language models demand a small quantity of labeled samples, and they could be enhanced to a greater extent by training them on domain-specific corpora instead. We propose an enhanced model FinSentiment, which incorporates enhanced versions of a number of recentlyproposed pretrained models, such as BERT, XLNet, RoBERTa to better perform across NLP tasks in financial domain by training these models on financial domain corpora. The corresponding finance-specific models in FinSentiment are called Fin-BERT, Fin-XLNet, and Fin-RoBERTa respectively. We also propose variants of these models jointly trained over financial domain and general corpora. Our finance-specific FinSentiment models in general show the best performance across 3 financial sentiment analysis datasets, even when only a subpart of these models are fine-tuned with a smaller training set. Our results exhibit enhancement for each tested performance criteria on the existing results for these datasets. Extensive experimental results demonstrate the effectiveness and robustness of especially RoBERTa pretrained on financial corpora. Overall, we show that NLP transfer learning techniques are favorable solutions to financial sentiment analysis tasks. Financial risk is empirically quantified in terms of asset return volatility, which is degree of deviation from the expected return of the asset. Under risk management in finance, predicting asset volatility is one of the most crucial problems because of its important role in making investment decisions. Even though a number of previous studies have investigated the role of natural language knowledge in enhancing the quality of volatility predictions, volatility estimation can still be enhanced via recent deep learning techniques. Specifically, extracting financial knowledge in text through transfer learning approaches such as BERT has not been used in risk prediction. Here, we come up with RiskBERT, the first BERT-based transfer learning method to predict asset volatility by simultaneously considering both a broad set of financial attributes and financial sentiment. In terms of language dataset, we utilize transcripts from the annually occurring 10-K filings of the publicly trading companies to train our model. Our proposed model, RiskBERT uses attention mechanism to model verbal context and remarkably performs better than the state-of-the-art methods and baselines such as historical volatility. We observe such outperformance even when RiskBERT is finetuned with a smaller training set. We found RiskBERT to be more effective in risk prediction after the Sarbanes-Oxley Act of 2002 has passed since such legislation has made the annual reports more effective. Overall, we show that NLP transfer learning techniques are favorable solutions to financial risk prediction task. Our pretrained models, and source code will be publicly available once the review is finished.
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Towards transparent recommenders : an explanation-based negotiation approach
Buzcu, Berk; Aydoğan, Reyhan; Aydoğan, Reyhan; Kıraç, Mustafa Furkan; Aydemir, F. B.; Department of Computer Science
As more and more recommendation systems are used in different areas and they are exposed to more ethical concerns, there is a growing demand for transparent and persuasive interactions with these systems. Toward this end, incorporating explainability in recommendation systems has emerged as a promising approach to enhance sociability and user trust. This thesis focuses on recommendation systems that utilize explainability techniques to foster sociability by providing precise and understandable explanations for their recommendations. The proposed recommendation system utilizes a combination of data-driven transparent mechanisms and human-agent negotiation approaches. The system generates personalized recommendations based on individual preferences and other similar user-tailored factors and engages in a negotiation with the users via discussions through explanations and real-time feedback mechanisms. The system reacts to user responses online, tailoring subsequent recommendations and explanations to convince the user. This thesis encompasses Nutrition Virtual Coach (NVC) agents that generate personalized food recommendations based on individual factors like allergies, eating habits, lifestyles, and ingredient preferences. It mainly focuses on explanation generation techniques to enhance the transparency and trustworthiness of the system by improving the NVC agent's sociability in multiple steps. Ultimately, we incrementally conducted multiple experiments with participants from various backgrounds to evaluate the acceptability and effectiveness of the system. The findings from the experiments generally indicate that most participants appreciate the opportunity to provide feedback and receive explanations for the given recommendations. The participants prefer receiving information tailored to their specific needs and expectations. Additionally, the participants expressed their thoughts on various forms of explanations. The findings indicate that comparative explanations are not appreciated as much as informative explanations. The users seem to prefer direct and simple explanations that explain items respectively.
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Cukurova's farm buildings : a syntactic analysis of historical evolution and contemporary adaptation
Fırat, Fırat Ali; Ünlü, Alper; Ünlü, Alper; Özgün, Kaan; Garip, E.; Department of Architecture
In Turkey, numerous studies have focused on the traditional architectural heritage in rural areas; however, there is a notable lack of architectural research centred on farms. The agricultural sector has undergone significant transformations in the past three decades due to advancements in production methods, reduced labour requirements, and changing product demands. As a consequence, traditional farm spaces have been compelled to transform. In this study, the region of Cukurova was selected as the research area due to its high economic significance and its importance in understanding agricultural practices in Turkey. The agricultural transformations occurring in Cukurova are evident in the spatial elements of farms, space syntax, and human behaviour. Fifteen active farms, constructed between 1850 and 1950, were chosen for analysis to identify traces of technology and human behaviour within the farm spaces. The objective was to delineate the boundaries between shared and private areas. Two sets of data were collected to test the research hypothesis's validity. The first set comprised behavioural data gathered through direct observations, while the second set consisted of plans generated from photogrammetric models created using drone photographs. Space syntax analyses were conducted to explore the farm layouts based on these plans. The findings of this research illustrate how the utilization of farms has evolved in response to technological advancements and changing living conditions. Recent shifts have defined and allocated private and production areas within farm layout plans. These morphological changes hold significant implications for modern farming practices, underscoring the need to examine the spatial adaptation processes within farm areas in Cukurova. Consequently, this thesis aims to shed light on the progressive spatial planning of farms over time and offer insights into sustainable spatial planning practices, particularly in contemporary and modern agricultural spaces.
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Bioclimatic architecture and advanced improvement strategies towards high energy efficiency at districts level
Avgan, Gizem; Aşrafi, Turaj Arda; Aşrafi, Turaj Arda; Sağlam, Neşe Ganiç; Taşçı, G. G.; Department of Architecture
Today's construction sector is taking a major part in worldwide energy consumption and its contribution to climate change. It is crucial to take proactive measures in architecture to mitigate the harmful effects of carbon emissions and pollution on our planet. Architectural strategies should focus on reducing energy consumption to align with this objective. In the context of climate change and unpredictable weather patterns, it is crucial to minimize reliance on active systems in architecture. By integrating passive design strategies, we can achieve a more sustainable approach. Active systems have their limitations, while passive systems offer greater flexibility and environmental friendliness. Bioclimatic architecture refers to the design of buildings that align with local climate conditions, aiming to enhance energy efficiency and reduce environmental impact. Implementing bioclimatic design on a larger scale, such as at the district level, has the potential to significantly increase energy efficiency. Bioclimatic architecture strategies have a substantial positive impact on districts' energy efficiency. Energy consumption and carbon emissions can be lowered by designing buildings according to the local climate conditions. Moreover, bioclimatic architecture may provide the residents with more comfortable living and working conditions, which can also positively impact health. The strategy of bioclimatic architecture in generating energy-efficient districts is determined by several parameters such as building geometry, materials, and district scale methods. The thesis aims to guide future district designs by providing assumptions towards ecologically sustainable and resilient neighborhoods using the assistance of bioclimatic strategies and advanced improvement strategies. The research investigates the districts' energy performance levels by integrating passive and advanced strategies to the buildings towards energy-efficient neighborhoods in the hot-humid and cold climate regions of Turkey. In order to assess the influence of bioclimatic parameters on district-level energy efficiency in two specific locations, a district energy model is created using Grasshopper and Rhinoceros. The district energy simulation results are obtained and analyzed. The bioclimatic and passive design variations' impacts under different climate conditions are discussed and compared in detail.
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RPC maintenance and upgrade studies of the atlas muon spectrometer and a muon tomography design using large area particle detectors
Renklioğlu, Ahmet; Akdoğan, Taylan; Akdoğan, Taylan; Safkan, Yaşar; Uğurdağ, Hasan Fatih; Doğangun, O.; Çetin, S. A.; Department of Physics
This thesis describes the spacing studies of BM/BR chambers to be installed on the ATLAS Detector at Long Shutdown 3 (LS3), the development of a new repair technique, and its implementation in the ATLAS Detector to repair and prevent gas leaks that cause critical problems in many aspects in ATLAS RPC System, and the crude design of a Muon Tomography with Large Area Particle Detectors.
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Thermal request optimization of a smart district heating system
Karasu, Mehmet Berk; Yanıkoğlu, İhsan; Yanıkoğlu, İhsan; Önal, Mehmet; Yavuz, T.; Department of Industrial Engineering
This thesis proposes a solution approach to manage the heating plans of tenants served by a district heating plant located in Sweden to decrease the carbon footprint of the residents. To do that, the daily temperature request of each household in the associated pilot region is obtained, and the daily temperature profile of each household is optimized with the help of a decision support system and smart valves. The hot water inflow rates of radiators are remotely controlled via smart valves at each flat to minimize the total energy consumption, carbon emission and cost associated with the energy consumption of the district heating plant. We aim to shave the peak demands while fully satisfying the temperature requests of households without violating their thermal comfort. Peak demand shaving is achieved by generating preheating schedules via mathematical optimization and using the thermal storage potential of the insulated flats. The resulting mathematical optimization model presents significant computational challenges that cannot be efficiently solved using optimization solvers within a reasonable time limit. To this end, we adapt three genetic algorithm approaches that are computationally scalable for realistically-sized instances and verified to yield near-optimal solutions for the test instances. Extensive numerical analyses how the effectiveness of the proposed approach and the genetic algorithm since we yield significant carbon emission decrease and cost savings compared with the method that the experts of the utility company propose.
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A metaheuristic approach for multiple-item economic lot sizing problem with inventory dependent demand
Balpınarlı, Duru; Önal, Mehmet; Önal, Mehmet; Albey, Erinç; Atan, S. T.; Department of Industrial Engineering
In this study, we consider a multiple-item Economic Lot Sizing problem where the demands for items depend on their stock quantities. The objective is to find a production plan such that the resulting stock levels (and hence demands) maximize total profit over a finite planning horizon. The single-item version of this problem has been studied in the literature, and a polynomial time algorithm has been proposed when there are no bounds on production. It has also been proven that the single-item version is NP-hard even when there are constant (i.e, time-invariant) finite capacities on production. We extend this capacitated single-item model by considering multiple-item. Since the single-item capacitated version is NP-hard, the multiple-item capacitated version is NP-hard as well. In the context of this research, we propose a Lagrangian Relaxation method to find an initial solution to the problem, and a Tabu Search algorithm to find better solutions. The performance of the proposed metaheuristic model is compared with the performance of a standard commercial software that works on a mixed integer programming formulation of the problem. We show that our metaheuristic algorithm finds better solutions within a predetermined time limit.
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City information modeling for water management in climate adaptive campus design
Bitik, Doğa; Aman, Doğa Dinemis; Aman, Doğa Dinemis; Hacıhasanoğlu, Orhan; Aytaç, G.; Department of Architecture
Cities must deal with increasing disasters due to global climate change. University campuses are socio-ecological systems that can act like urban prototypes in which we can test self-sufficiency and resilience in their physical context. Current studies commonly look for smart city indicators and how to demonstrate them on university campuses, but most of them do not offer resilient planning and design strategies. This thesis focuses on proposing a resilient and sustainable smart campus design index. The proposed research methodology involves the determination of the index by integrating resilience and sustainability indicators, followed by a case study assessment with City Information Modeling (CIM) and climate analysis simulation with a particular attention on water. Simulation and modeling tools, including computer simulations, 3D modeling, and digital twins as part of "Smart," is used to evaluate the effectiveness of existing campus masterplan design guidelines and strategies for water related disaster resilience and sustainability. The simulation method created for this thesis can be extended and used for offering climate change adaptive design strategies of different typologies, capable of functioning for different types of hazards. The potential use of digital twin and smart city technologies in disaster-resilient university buildings and campus design is explored, and potential challenges and limitations are identified. Integrating smart city principles and resilient thinking is a major demand for the city's future sustainability. This research methodology contributes to the literature on adaptive and smart disaster-resilient urban design index. The proposed study can assist in climate change adaptation and help decision-makers support further comprehensive planning and design approaches.

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