M.S. Theses
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Item Constructing edge extremal triangle-free graphs with bounded maximum degree and matching number using integer programming(Thesis (M.S.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2023., 2023) Banak, Ali Erdem.; Taşkın, Zeki Caner.; Ekim, Tınaz.The maximum number of edges in a graph with matching number m and maximum degree d has been determined in [1], and a structure for an extremal graph has been provided in [2]. Then, finding the edge count of an extremal graph with forbidden subgraphs emerged as a new problem. Ahanjideh, Ekim, and Yıldız [3] worked on triangle-free graphs and solved the cases for d ≥ m and for d < m with either d ≤ 6 or Z(d) ≤ m < 2d, where Z(d) is approximately 5d/4. They also provided a structure for an extremal graph for the rest. Using the structure provided, we develop an integer programming formulation for constructing an extremal graph. The formulation is highly symmetric. We use our implementation of orbital branching and objective perturbation to reduce symmetry. We also propose an exact iterative algorithm that partitions the feasible region based on upper bounds and works iteratively on a limited space. Using a combination of the orbital branching and iterative approaches, we expand the solution into d < 11 instead of d < 7 for m > d and show that conjectures proposed in [3] hold, thus strengthening them.Item Planning bundling and marketing efforts for a digital distribution platform(Thesis (M.S.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2023., 2023) Genel, Medine Zelina.; Güllü, Refik.The literature on supply chain coordination is extensive, and there are numerous methods for coordinating these systems. There may be various types of associated costs in these systems related to production, quality, or sales effort, and various approaches and extensions may be integrated to such systems. Product bundling is one of the common practices that supply chain members can use to expand the market. Our study combines supply chain coordination and product bundling by incorporating marketing effort. While application developers (ADs) decide on price and marketing effort, the distribution platform (DP) decides whether to bundle developers’ products and the commission rate he will charge them. This thesis adds to the literature on optimal platform bundling strategy when producers have the option to increase demand with marketing efforts. When the distribution platform implements the bundling approach, how application developers set their prices when the distribution platform offers a bundle option, and the effects of bundling on supply chain members are just a few of the questions we try to address. As a result of this study, it is shown that DP’s optimal strategy on product bundling is dependent on the price ADs would announce. Secondly, ADs optimal pricing decisions may depend on the presence of bundling option provided to DP when there is a constraint on commission rate. Thirdly, it is shown that while the existence of the bundling option always benefits the distribution platform, it might benefit or harm application developers depending on the model settings. We also find that the total supply chain revenue is higher in the centralized supply chain compared to the decentralized supply chain. NOTE Keywords : Supply chain coordination, Product bundling, Pricing, Marketing.Item Sequence-dependent setup time estimation in tactical production planning using machine learning(Thesis (M.S.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2023., 2023) Cüceloğlu, Erhan.; Ünal, Ali Tamer.Tactical production planning with sequence-dependent setup times is a frequently studied topic in literature. Production schedule needs to be known to determine the total sequence- dependent setup time required in a period, which accounts for the capacity loss due to setup operations. Determining the schedule with minimum capacity loss and lot sizes for a multi- period tactical plan simultaneously is the main challenge in literature. This study aims to introduce a tactical production planning model to determine production amount and inventory levels with more accurate capacity loss estimation due to sequence-dependent setup times. Production amounts in tactical model are discretized into equally sized buckets to generate production mixes. Production mixes are used to estimate the sequence-dependent setup times by taking which products are produced and the amounts they are produced in to account. A scheduling model is used to determine the optimal setup times for a set of randomly generated production mixes and machine learning methods are used to estimate for the rest of the production mixes due to computational complexity. The estimation methodology introduced in this study is compared with a baseline and a basic estimation model based on the accuracy of estimating the total capacity loss due to sequence- dependent setup times.Item Multiobjective trees for forecasting(Thesis (M.S.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2023., 2023) Arıca, İrem.; Aras, Necati.; Baydoğan, Mustafa Gökçe.Time series forecasting is a significant task, and it can be used in various fields such as education, finance, medicine, and manufacturing. The wide use of temporal data has resulted in many researches and studies in the field of data mining. Besides time series analysis models, tree-based methods are commonly used for time series forecasting. Tree-based models provide accurate results in complex datasets, and they can explain nonlinear relationships. However, time series data is obtained by measuring a variable at certain time intervals and often contains predictable fluctuations. In the use of tree- based modeling, the temporal relationship and seasonality in time series data should be included in the modeling process. Regression trees consider all data points independently of each other. Thus, preprocessing or postprocessing methods are needed for their use in time series forecasting. However, temporal and seasonality effects can be incorporated into learning process of trees. In this thesis, Multiobjective Trees for Forecasting (MTF) Model is proposed, which is a tree-based ensemble approach that makes changes in the learning phase of decision trees. In addition to objective of decision trees, two new considerations are added to be taken into account during partition. These are temporal closeness in nodes and the seasonal distance between observations. A single decision metric is created by combining these three objectives in different weights, and splitting is done according to this decision metric. In the experiments, five different synthetic datasets and three time series datasets are used. Adding these objectives yields better results than the traditional approach, when the weights are selected with parameter tuning. Finally, multiobjective trees are tested in randomized setting similar to random forest models and compared with the traditional time series forecasting models. More competitive results are obtained when only one decision tree is fitted compared to randomized setting.Item Inventory planning of perishable items using reinforcement learning(Thesis (M.S.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2023., 2023) Say, Ahmet Sualp.; Bilgiç, Taner, 1965- .Managing perishable inventory effectively is vital in diverse sectors like grocery, pharmaceuticals, composite materials, agriculture, blood. The challenge lies in reducing costs while handling items with limited shelf lives and changing demand. This study delves into the potential of computational techniques, notably the reinforcement learning methods Q- learning and SARSA, to tackle this intricate issue. We turned to reinforcement learning because traditional approaches struggle with increased complexity as the problem grows. We began our exploration with backward dynamic programming for a basic perishable inventory model. This model covered 10 periods and 3 age classes under a deterministic demand. We then expanded this framework to address more unpredictable demand patterns, both stationary and non-stationary, crafting optimal value functions for each. Our study also ventured into the Q- value approach, where transition probabilities were predefined, comparing the results to traditional dynamic programming. We further evaluated Q-learning and SARSA to see how close they converge to optimal. As the problem’s complexity rose, especially with advanced demand scenarios like Advance Demand Information and more age classes beyond three, traditional methods fell short. In contrast, reinforcement learning proved nimble, especially in tackling more intricate inventory challenges. Our findings underline that reinforcement learning methods can approximate the near-optimal results achieved by dynamic programming in simpler scenarios. More remarkably, as the problem’s intricacy grew, reinforcement learning continued to offer solutions, suggesting its promise in addressing even more complex inventory challenges.Item Expression dynamics of minority opinion in homophily-oriented networks(Thesis (M.S.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2023., 2023) Koçak, İrem Betül.; Yücel, Gönenç.Minorities may remain silent for fear of social isolation. According to the spiral of silence theory, minority groups underestimate their numbers in society. This underestimation leads minorities to speak up less and other minorities to feel less supported, so minorities get caught in a spiral of silence. They can break out of this spiral by uniting with each other. People seek similarity in their relationships, and this is referred to as homophily in the literature. Under homophily, people can end their relationships with different people and establish new relationships with like-minded people. These dynamic networks are called homophily-oriented (H-O) networks. We investigate the expression dynamics of the minority opinion in H-O networks using the agent-based modeling approach, where we represent people as agents and the relationships between them as links. In a case where agents end their relationships with the opposing side but build relationships with anyone, we found that the minority can oppress the majority by distorting the perceptions of majority members because minorities increase their opinion expression probability by lowering their expectations in the first place. When agents connect directly with like-minded agents; they create echo chambers, and both majorities and minorities can be fully expressive. We then introduce new agent types into the model, representing three types of people: hardcore agents are always expressive minorities, loyal agents do not change their social circle but their expression decisions, and valiant agents are both expressive and loyal minorities. Minorities’ loyalty to their social environment sabotages their expression in H-O networks but reduces echo chambers. Hardcore agents and valiant agents increase minority expression in static networks; in H-O networks, minority expression is already at its maximum, so they cannot increase it but valiant agents can decrease segregation in societies.Item Measuring and managing risks in supply chain planning(Thesis (M.S.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2023., 2023) Kara, Emre.; Ünal, Ali Tamer.In today’s globalized world, supply chains have global characteristics as well. Instead of geographically separated supply chains, there exists a global single supply chain in the world. Because of this circumstance, disruptions even at geographically distant locations can have severe impacts on all businesses globally. This nature of business increases the importance of supply chain risk management (SCRM) more than ever. Manufacturing plants require accurate tools to measure and manage risks effectively and profitably. In this study, an enhancement to Material Requirements Planning (MRP) type production planning is proposed such that supply chain risk can be measured precisely and risk management policies can be developed accordingly. Initially, supply chain disruptions are defined and categorized and buffers used against these uncertainties are introduced to the MRP optimization model. With this new model and a risk function defined in terms of backlogs, an SCRM framework in the form of buffer management is proposed. Afterwards, the performance of the model with different settings is observed with simulation experiments and the statistical analysis is conducted on the data. Although the most significant buffering policy was found to be the supplier lead times, many findings were discovered on the effects of buffers on the supply chain (SC) risk and the profitability of the business.Item Using lagrangean relaxation for solving the minimum spanning tree problem with conflicts(Thesis (M.S.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2022., 2023) Kağıt, Abdulsamet.; Altınel, İ. Kuban.This thesis studies minimum spanning tree problem with conflicts, which is known to be NP-hard. Given an edge weighted graph and a set of conflicting edge pairs, the goal is to find a minimum cost spanning tree which does not contain any conflicting edge pair. In this study, a Lagrangean Relaxation scheme is proposed to obtain lower bound on the optimum objective value and Lagrangean dual problem is solved via subgradient algorithm. A Lagrangean heuristic is also devised which is combined with a simple local search in order to obtain upper bounds from infeasible solutions obtained throughout the iterations of the subgradient algorithm. Extensive computational experiments show that the proposed Lagrangean relaxation scheme and the Lagrangean heuristic outperforms the ones proposed in the literature either in terms of time efficiency or bound quality. The Lagrangean relaxation scheme and the Lagrangean heuristic are then embedded in a branch-and-bound algorithm, along with a preprocessing procedure, an infeasibility test procedure and valid inequalities to create an exact solution algorithm. The exact solution algorithm proposed in this study is also compared with the ones in the literature and state of the art commercial solver Gurobi 9.5.2. Positive and negative aspects of using Lagrangean relaxation in a branch-and-bound algorithm are also discussed.Item Dynamic pricing for free floating car sharing systems(Thesis (M.S.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2023., 2023) Şimşir, Mısra.; Güllü, Refik,In recent years, the importance of sharing platforms has increased as a result of globalisation and networking. As the world has become more globalized, the sharing economy has spread, with people from all corners of the globe embracing collaborative consumption patterns. One of the emerging markets in the sharing economy is car sharing. Car sharing platforms have been studied in the literature as station-based and free- floating. Free-floating Car Sharing (FFCS) model is the most flexible among the car sharing models because customers are free to park vehicles anywhere in the service area. FFCS offers flexibility and convenience to its customers. However, this flexibility can create some problems such as uneven distribution of vehicles between stations, parking challenges and fleet management. Imbalanced distribution of vehicles leads to customer loss and dissatisfaction, which in turn leads to loss of profit for car sharing companies and damages long-term sustainability. In order to create customer loyalty to such a service, it is important that vehicles are available where customers are looking for them. In this thesis, combined vehicle relocation and trip pricing strategy is developed to solve imbalanced vehicle distribution and to increase the profitability of the companies. Mixed integer nonlinear mathematical model is formulated to solve this problem. Computational experiments are performed using both synthetic and real data. Due to the complexity of the model, the proposed model is tested with smallscale instances only. It is observed that by implementing a trip pricing strategy, a profit of $128087.4 per day is achieved, whereas by applying a fixed pricing strategy without any relocation, the system is able to achieve a profit of only $6947.9 per day. The improvement of the system is achieved by increasing the prices and decreasing the reasonable quantity of demand.Item Revenue optimization based on price, delivery time, and capacity(Thesis (M.S.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2023., 2023) Şimşek, Burak.; Bilgiç, Taner, 1965- .The intention of this thesis investigate the optimum price, guaranteed delivery time, and capacity expansion level of a single firm to maximize its profit per time in the make-to- order system under a deterministic environment. A log-linear demand model that depends on the sales price and guaranteed delivery time of a single product is used. In order to satisfy customer demand, guaranteed delivery time must be met at a certain service level. The firm has the option to expand its production capacity at a cost in order to meet demand and satisfy predefined service level by delivering the product within a guaranteed delivery time. A mathematical programming model for profit maximization is constructed to observe the relation between these three decision variables; price, guaranteed delivery time, and capacity expansion level, and find the optimal decision variables set that maximize the profit. The characteristics of decision variables are investigated for different frameworks and parameters. Our model complements the literature as we introduce convex exponential capacity expansion function alongside the linear capacity expansion cost function. Additionally, we consider production cost as a decreasing function of guaranteed delivery time. We perform a computational study to find the optimal solution set and make observations regarding these results. We further provide general managerial insights about which strategies should be adapted according to company structure.Item Improving wind power forecasts : integrating adaptive histogram of oriented gradients and multiple numerical weather predictions(Thesis (M.S.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2023., 2023) Çelenk, İlayda.; Aras, Necati.; Baydoğan, Mustafa Gökçe.In this research, alternative representations of the grid based Numerical Weather Prediction (NWP) models are proposed for wind power forecasting purposes. Wind speed is the major indicator for the power generation. However, wind direction has nonlinear effects coming from the wind’s dynamic nature. With the traditional representations, the continuous and cyclic behavior of the wind direction is not perceived by the learners. To address these problems, Standard and Supervised transformation methods based on the Histogram of Oriented Gradients (HOG) of the NWP models are proposed. Our experiments on forty-seven wind farms show that the Standard HOG transformation on the naive features of multiple NWP models from distinct locations provides superior performance in linear learners. Additionally, Supervised HOG transformation is proposed with the utilization of tree based clustering methods. According to the results of the experiments with combined representations, linear learning methods outperform the tree based learners in wind power forecasting. This result indicates that the nonlinearity deriving from the wind direction’s circular behavior is represented in as suitable form for the linear learners.Item Impact of return flow estimation with orbıt information on the bullwhip effect in a multi-echelon closed-loop supply chain with production capacity(Thesis (M.S.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2023., 2023) Gündoğdu, Nur.; Korugan, Aybek.Closed-loop supply chains recover used products and reduce material use and waste through remanufacturing. However, uncertainties in the return flow propagate through the supply chain, especially in multi-echelon systems. Return variability amplifies the order and inventory fluctuations upstream, also known as the bullwhip effect phenomenon. In this study, we analyze a multi-echelon closed-loop supply chain with production capacity, where the customer demand for a single type of product is met by manufacturing new products and remanufacturing returned cores. A single retailer, wholesaler, distributor, and OEM replenish their inventory with (r, S) periodic review. Effective return estimation is necessary to mitigate the bullwhip effect. Traditional forecasting methods such as the moving average and exponential smoothing disregard the correlation between past sales and future returns. To this end, we utilize information on the orbit size, expected product lifetime, and return probability to estimate returns. Then, we analyze the impact of return estimation with orbit information on the bullwhip behaviour under various factors such as return probability, reorder period, and average product lifetime. We compare the bullwhip behaviour in the forward supply chain with the closed-loop supply chain under different return estimation methods. We also introduce alternative shipment policies to reduce the delivery lead time, and investigate the impact of impulse demand. We observe that closed-loop supply chains with production capacity constraint exhibit different bullwhip behavior than the uncapacitated systems.Item Modeling long-term dynamic effects of brain injury on biological mechanisms of potential Parkinson's disease progression(Thesis (M.S.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2023., 2023) Gül, Nezihe Nazlı.; Yücel, Gönenç.; Barlas, Yaman.Parkinson’s disease (PD), the second most common neurodegenerative disorder affecting over ten million people worldwide, is a multifactorial disease influenced by several biological and environmental factors. Complex interactions controlled by feedback relationships between neuroinflammation, oxidative damage, mitochondria, protein accumulation, and neuron sub-systems are at the center of the brain. While some lifestyle elements reduce vulnerability, head trauma raises the risk for PD. Traumainduced neuroinflammation is the most prominent short-term consequence, and due to the intricate structure of the brain, multiple variables are affected in the long-term. To study those impacts on potential PD progression, we constructed an individual-level system dynamics model of a specific brain region where dopamine-producing neurons reside. After obtaining the normal aging dynamics, various brain injury scenarios are investigated to see whether healthy individuals would exhibit PD-like behaviors. Then, possible genetic variation and/or lifestyle factors such as healthy diet and exercise are tested on both healthy and PD-prone people. The difficulties in monitoring and quantifying brain-related variables are the primary challenges for this research because only post-mortem analysis allows for neuropathological diagnosis. The model is structurally and behaviorally validated using the qualitative and quantitative knowledge of autopsy reports and animal experiments. In scenario runs, we observed the impact of several external and internal factors. The research aims to provide a comprehensive understanding of relevant brain dynamics in interaction with external factors and identify effective mechanisms for treatment and prevention strategies for PD. This work is open to development by including discoveries from field data and empirical studies.Item Dynamics of long-term essential hypertension and pharmacotherapy options(Thesis (M.S.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2022., 2022) Hasgül, Zeynep.; Yücel, Gönenç.Hypertension is a preventable and treatable disease however; blood pressure control remains insufficient in many countries. An incomplete understanding of the highly complex mechanism of blood pressure regulation is one of the reasons for poor hypertension management. Physiological differentiation in individuals causes optimal antihypertensive treatment strategies to vary for patients. Consequently, instead of one fits all treatments, personalized treatments can offer improved results on therapeutic goals. Due to the sheer number of feedback loops and interconnected mechanisms, we developed and validated a system dynamics model for understanding and interpreting relationships within hypertension treatment. We aim to demonstrate the use of simulation modeling to study inter-patient physiological variations and their effects on hypertension treatment outcomes. The model includes the central nervous system and renal control mechanisms, vascular elasticity, and kidney function, capturing long-term dynamics of blood pressure. Three pathogenesis mechanisms of primary hypertension (the over-active nervous system, the over-active renin-angiotensin-aldosterone system, and low sodium excretion) are examined to represent different patient profiles. Later, treatment outcomes are analyzed on the identified patient profiles by comparing the decline in kidney and vascular functions under various treatment options. This study presented the potential benefit of using dynamic simulation models to compare treatment options for patients with physiological differences. Even though obtaining granular empirical data is challenging, they can be used for calibrating simulation models such as these to get closer to individualized treatments in clinical settings. As a future study, cost-effectiveness and value of information aspects can be incorporated to assess the benefit of personalized treatments.Item The maximum acyclic matching problem(Thesis (M.S.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2022., 2022) Çelebi, Cemre.; Aşıcı, Tınaz Ekim.The aim of this thesis is to develop exact and heuristic methods to solve the Maximum Acyclic Matching problem, which deals with obtaining maximum matching such that the subgraph induced by saturated vertices is acyclic. The maximum matching problem tries to find the most extensive possible matching set. it is a well-studied problem that can be solved with combinatorial algorithms. However, for the maximum acyclic matching problem, we are searching for not only a maximum size matching but also we require that the subgraph induced by saturated vertices does not contain any cycles. For this purpose, an additional acyclicity constraint is needed. Even though some exact and approximate algorithms are established for particular graph classes, there is no such algorithm applicable for general graphs to find a maximum acyclic matching. In this study, four algorithms are suggested. Randomly generated graphs with different density levels and sizes are used to analyze their performance. Two of the algorithms are exact algorithms, which are extensive and cutting plane formulations. Based on experimental results, the cutting plane approach performs better since it works also with larger graphs. The other two algorithms are heuristics, which are modification and construction approaches. It is observed that the construction approach performs better than the modification approach in terms of both time efficiency and quality. The construction approach yields feasible and close-to-optimal results in a shorter period. When the results of the best exact and heuristics are compared, the cutting plane algorithm performs better based on optimality. However, it is not applicable for large graphs compared to the construction algorithm. Additionally, it is seen that the effect of acyclicity constraint is increasing while graph size and density are getting larger.Item Money laundering detection in cryptocurrency networks(Thesis (M.S.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2022., 2022) Erdem, Elif Emine.; Ekim, Tınaz.This study aims to develop scalable methods to detect suspicious wallets using historical transaction data in cryptocurrency networks such as Ethereum and Bitcoin. Different transaction networks are generated for each wallet data set using the illicit wallets dispersed around the internet. Egonet-dependent and independent features are used with a range of machine learning techniques, including logistic regression (LR), random forest (RF), and XGBoost (XGB), to predict illicit wallets. Firstly, we analyze performance of models to detect suspicious wallets in the two datasets that include suspicious bitcoin mixer services wallets such as Bitcoinfog and Helix. The area under the ROC curve value (AUC) is over 99% for XGB models. We observe that models perform better on Helix wallets than BitcoinFog wallets in terms of precision, recall, f1 score, and AUC. Secondly, we notice that egonet dependent features do not significantly improve the models’ performances. Hence, best- performing models have only egonet independent features. Thirdly, on Bitcoin datasets that do not use any mixer services, we obtain over 99% AUC. Although the performance of the models is similar in these three datasets, dominant features in terms of feature importance measure are different between the datasets including wallets using mixer services (Helix, Bitcoinfog) and the other (Bitcoin). Lastly, utilizing the same feature set as we do on Bitcoin, Bitcoinfog and Helix datasets, we train the same machine learning models on the Ethereum dataset and obtain 96% AUC. We repeated the tests with varying degrees of class imbalance to simulate real-life situations. We observe a decline in AUC up to 0.10 together with the increasing severity of the class imbalance.Item Air cargo revenue management spot allocation problem(Thesis (M.S.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2022., 2022) Bütün, Yahya Umut.; Taşkın, Zeki Caner.The focus of Air Cargo Revenue Management (ACRM) is to best estimate cargo capacity, forecast future demand, and take accept or reject decisions on the bookings accordingly. ACRM is a different problem than passenger revenue management due to uncertainty of cargo capacity, business, operations, and cargo booking behavior. These factors add additional complexity to a problem and make traditional revenue management approaches inadequate. Certain additional models need to be developed to solve the ACRM problem. The purposes of this thesis are to discuss the processes of air cargo revenue management and develop a spot allocation model. In the thesis, we develop a spot allocation optimization model. In necessary booking control conditions, this model is solved repetitively to decide on allocating expected demand to cargo capacity. A simulation study is performed after the optimization model to compare the results of our optimization model with a commonly used heuristic, First-Come First-Served, under defined scenarios and other test problem settings. Finally, we conclude that our model performed better than 26 out of 27 scenarios according to t-test statistics with a 95% confidence level.Item A comprehensive dynamic model of cyclic neuropenia(Thesis (M.S.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2022., 2022) Işık, Yusuf.; Yaşarcan, Hakan.Neutropenia is a hematological disorder that is defined as having a low level of neutrophils in the bloodstream. Low levels of absolute neutrophil counts leave the body defenseless against and vulnerable to infections. Cyclic neutropenia is a type of neutropenia that is described as the oscillations observed in the level of blood neutrophils. The disorder is mostly treated with a cytokine named recombinant granulocyte colony– stimulating factor, rG–CSF, which is administered via injection. A delicate injection schedule is called for because the treatment procedure is costly. However, treatment experiments on an actual patient require frequent sampling from bone marrow and blood, which simply cannot be allowed as it can be detrimental to the health of the patient. Therefore, modeling is a must to carry out treatment experiments. Accordingly, the main motivation in this thesis is to construct a comprehensive dynamic model of cyclic neutropenia. As human physiology is rich in dynamic complexities, system dynamics is selected as the primary methodology. We first construct a model that represents the regulatory structures of neutrophil production for a healthy person. After validating the model, the neutrophil dynamics of a cyclic neutropenia patient is obtained by simply changing the parameter values, but without changing the model structure. Neutrophil production deficiency is the most mentioned cause of cyclic neutropenia in the literature, which is also confirmed in our study. According to our simulation results, the clearance of the apoptotic neutrophils of CN patients takes longer than normal and apoptotic neutrophils can suppress both the production and effects of G– CSF. As a result of experiments with pathogens, we claim that the oscillatory behavior is a characteristic of the neutrophil–GCSF–pathogen system even for a healthy person. This may shed some light on the periodic symptoms observed in patients with diseases caused by an overactive immune system. We experiment with rG–CSF injections too.Item Analysis of inventory policies under estimation of demand parameters(Thesis (M.S.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2022., 2022) Bozkaya, İrem Cansu.; Güllü, Refik.The aim of this thesis is to analyze the estimation performance for different characteristics of data in different supply chain systems. In this study, we analyze two different systems. The first section of this thesis covers the parameter estimation problem of a single retailer who observes price-dependent uncertain demand in additive form. To estimate the parameters of the demand, we use linear regression. Additionally, we jointly optimize the price and order quantity. We present a numerical analysis where parameter estimations are calculated for different experiment sets. We analyze the estimations for the number of observations, coefficient of variation, price, and profit margin. We show that as the number of observations increases and the coefficient of variation decreases, the accuracy of our estimations increases. The real optimal price of the data sets does not indicate a pattern of improvement and the profit margin of the product is observed to have no significant effect on the estimation performance. In the second section, we introduce a two-echelon system observing identical and non-identical exponentially distributed demand rates. The warehouse makes the allocation decision of its limited inventory based on the past demand data of retailers. The capacity constraint of the warehouse creates a conditional expected profit function. To find the expected profit, we propose a simulation method where the inventory allocation is repeated multiple times. To evaluate the performance of our method, we introduce experiment sets. Results show that for the systems with identical and non-identical demand rates, the percent loss in expected profit is smaller when the system utilization and the profit margin are larger. For the latter system, we analyze the effect of the ratio between the demand rates of two retailers. The results show no significant effect of this ratio on the percent loss in expected profit.Item Multi-level production planning for multi-location chemical company(Thesis (M.S.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2022., 2022) Gökçe, Emre.; Ünal, Ali Tamer.We investigate the decision-making process at a chemical company which is a major soda manufacturer having three production facilities in different countries. The company is one of the top ten largest soda producers in the world and serves as a major supplier to the global market with sales to over 50 countries. Complex sales and operation structures of company require synchronization of optimized decisions. Our study focuses on optimizing decisions from various levels which are strategic, tactical, operational and synchronizing them within and among organizations. In order to reach this purpose, we designed an integrated solution platform based on various optimization models. We fully implemented and deployed the solution platform, which has been used since 2020.