Lisansüstü Eğitim Enstitüsü

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Market Price Simulations for Turkish Electricity Market Using Equilibrium Models
(Kadir Has Üniversitesi, 2014) Çakır, Hande; Çelebi, Emre
Electrical energy which is known as secondary energy sources is generated by the conversion of renewable energy sources to potential and chemical energy or is generated by variety processes of fossil fuels. Electricity is an important product in an economy and an important input for production of most of the goods and services. Electricity has unique properties such as non-storability and it has no full substitute and therefore the electricity industry is different from classical competitive industries. During the last decade new regulations and developments in the world have initiated various reform movements and a new action plan in Turkey to create a competitive market. The relevant legislation and procedures is created and specific markets are designed within this plan and vertically integrated structure of generation transmission and distribution activities are separated in this restructuring process. in this study we focus on many examples and applications in the world about electricity market equilibrium models. Since there is no market model applied in this way in Turkey it is created to simulate market prices by using GAMS software and adoption of market price simulations to Turkey's electricity markets are examined. Finally we have performed price-cost analyses and observe the welfare effects of different market structures. -- Abstract'tan.
Six Sigma Project Evaluation Under Fuzziness in Food Industry
(Kadir Has Üniversitesi, 2013) Şentürk, Özlenen; Ayağ, Zeki; Kahraman, Cengiz
Modern-day business world is under constant development at production and service market. Common purpose is to return profit by optimizing costs and raise customer satisfaction to maintain acquired success. It is observed at processes that more service and production are provided by less work force. In food sector which is one of the best known service and production areas, if there will be a new production system "knowledge" should be used as base. On the other hand it is necessary to focus on food safety and customer satisfaction. It is obvious that Six Sigma approach is helpful to achieve this tradition. Statistically objective is to enhance performance at processes by reaching defect margin of 3,4 units at 1 million product or service. Six Sigma methodology provides cultural exchange on the way to improvement. Companies can do measurements by using quality control tools for topics like determining cost expenses. However these companies will have difficulties measuring customer satisfaction. Especially at food sector, performing measurements mostly brings along difficulties due to variable customer needs. In this connection it will be an important attempt to use "Fuzzy Logic". Fuzzy Logic is an artificial intelligence principle with variable outcomes which does not gives certain results like classic logic and datas are estimated. In this research, most suitable alternative and method is determined by Six Sigma project evaluation approach under Fuzzy Logic for 5 different selected food facilities in Turkey.
The Social Return of Investment Metrics: Successful Social Media Measurement
(Kadir Has Üniversitesi, 2017) Cengiz, Yasemin; Da?, Hasan
The world economy has been formed based on needs, innovations, wars, and trends until today. Primary sector became stepping stone, industrial development forwarded economy to 2000's and now, today, internet is shaping it with accordance to trends. If someone is aiming for an investment, clicking for stock market or F/X rate is too simple, if someone is looking for renting a house, it takes a few minutes to conclude, if someone is surfing for online shopping, there are many options to the point there it became too easy to buy. Therefore, companies and firms have started to put an emphasis to e-commerce to edge ahead from the competitors. Nowadays, almost all brands/companies are presenting online sales services to consumers, and one of the keys for surviving in this huge market is managing social media networks and guiding consumers with these tools; Facebook, Twitter, Instagram, YouTube etc. Today, social media is becoming more than a place of entertainment and communication. Popular social media sites such as Facebook, Twitter and Instagram, which house millions of people from all corners of the world, have also become the focus of advertisers. Social media is now an essential part of our lives. Even when an employer recruits someone, he can first review the person's social media account and make various assessments. The employer can use the social media as a tool to earn money as the person chooses according to the social media account. Monetary counterparts have begun advertising through social circles, and the return of these ads to investors has been constantly researched and examined. Although advertisers and investors are removed from manipulated data from time to time, the results of the advertising campaign in social media can now be analyzed more healthily in various ways and the social media campaign ROI calculation process gives more accurate results. The results from the calculations to give you the return that you spend on advertising are called "ROI". The ROI, which is the abbreviation of Return of Investment, is a concept that tells what an invested investment is as a return to investor. With an ROI, you can see how much profit you make or how much you suffer from an investment you make. While the posters you put on a street can only be seen by people passing through that street, ads given to social media sites with a preference for successful targeting can be seen by all active users interested in that space. Social media ads offer more advantages in both measurement and return than classic ads. Social media ads offer more advantages in both measurement and return than classic ads. According to classical advertising concept; suppose you advertise on a billboard on the street, in a newspaper or on a television channel. It will take quite a long time to measure the return of this ad and you will have to spend as much money as you learn how to recycle these ads, and you will continue to invest in the same ad until you know you have failed because at first glance it is difficult to see that an advertising project failed. On the other hand, you can measure what an ad you give on social media brings back to you from the moment you advertise it. The return on each digital advertising campaign or ad investment you've made is crucial to your campaign metrics and future brand goals. Being able to measure your return on investment will help you in the same way that you will be new at the same time whether or not your campaign is efficient and where you should optimize your advertising campaigns. It is easy to measure in a certain way the budget of your budgets, such as mobile video ads, natural advertising, programmatic advertising, etc., as you try to make a new marketing move for your brand and go through digital channels. It may also be true on paper, but this excess base will be far less reflective of the rate of return on investment. You'll also need more granular metrics to capture the big picture, such as viewership, engagement, brand awareness, brand image, website visits, fill in lead forms, and downloads. Return on investment (ROI) is one of the most important factors for digital marketing as well as for traditional marketing because it tells us that our campaigns are not making money for us. If you do not earn it, it is a very critical operation to identify the causes and revise it to make it better. Of course, in order to be able to do all of these things, we need to efficiently measure the return on investment in our digital marketing campaigns. My research is, "The social return on investment metrics: successful social media measurement", and the time interval that I am observing resources and references is between 2010 to 2017.
Market-Clearing Simulations and Analyses for Turkish Electicity Market Market
(Kadir Has Üniversitesi, 2017) Şentürk Eker, Cansu; Çelebi, Emre
in this thesis current market structure of the Turkish electricity market which uses a uniform pricing system is analyzed and new market-clearing mechanisms (e.g. single or nodal pricing) are investigated for the market requirements. This has led to the development of different market-clearing models and market-price simulations that can be encountered in transition to a regional pricing model which provides market participants with greater transparency and simplicity in forecasting market outcomes. in the proposed models Turkish electricity market has been analyzed by using nine regional control areas (zones) pre-specified by Turkish Electricity Transmission Company (TETC). Based on Energy Exchange Ýstanbul transparency platform and TETC reports installed generation capacities are calculated for each region according to thirteen fuel types and seven different types of ownership. Different scenarios (e.g. seasonal peak mid-peak and off-peak) and data sets (e.g. capacity and load factors for weekdays and weekends as well as price-elastic linear demand function parameters for each region) are formed and different pricing models are formulated using a mixed complementarity problem (MCP) framework. Operation maintenance and fuel costs for each generation facility are obtained from international cost survey studies. The effects on social welfare and electricity price levels for the pricing models are examined in details using different market structures (e.g. perfectly competitive and Nash-Cournot). in MS EXCEL regional maps containing nine control areas of the transmission network are created and the results obtained from GAMS software are summarized using macros (e.g. visual basic for applications –VBA codes). in the literature such models appear for different regions and countries however it is a major shortcoming for Turkish electricity market. Hence the proposed models of this thesis will enable the analyses of decision-making process of market participants and their short/medium/long-term decisions as well as future investment plans and their impact on the market.
Real Time Prediction of Delivery Delay With Machine Learning
(Kadir Has Üniversitesi, 2023) Küp, Büşra Ülkü; Hekimoğlu, Mustafa
İnternetin yaygınlaşması, e-ticaret ve lojistik endüstrilerinde önemli bir dönüşüme yol açmıştır. Bu dönüşüm, çevrimiçi alışverişte önemli bir artışa öncülük etmiş ve rekabetçi ortamda kargo şirketlerinin operasyonel verimliliğini arttırma ihtiyacını ortaya çıkarmıştır. Teslimat süreçlerini optimize etmek ve müşteri memnuniyetini artırmak amacıyla, makine öğrenimi kullanılarak teslimat gecikmelerinin tahmin edilmesi, lojistik şirketlerine önemli katkılar sağlayacaktır. Ayrıca, gerçek dünya verilerinin bu çalışmada kullanılması, elde edilen sonuçların güvenilirliğini artırmakta ve makine öğreniminin lojistik endüstrisi odaklı akademik araştırmalarda kullanılmasının avantajlarını vurgulamaktadır. Bu çalışmada, Logistic Regression, XGBoost, CatBoost ve Random Forest gibi en yaygın kullanılan dört denetimli sınıflandırma algoritması, bir e-ticaret lojistik şirketinde gerçek zamanlı veriler kullanılarak teslimat gecikmelerinin tahmin edilmesi amacıyla uygulanmıştır. Tüm süreç boyunca sürekli gecikme tahmini yapabilmek için, tüm teslimat süreci farklı gönderi türleri için sırasıyla 11 ve 15 adım şeklinde ayrıştırılmış ve her adım için ayrı tahmin modelleri oluşturulmuştur. Bu modellerin performansını artırmak için optimal parametre ve öznitelik seçimi yöntemleri kullanılmıştır. Kullanılan bu optimizasyon teknikleri, modellerin performansları üzerinde önemli bir olumlu etki sağlamıştır. Elde edilen sonuçlara göre, dört farklı sınıflandırıcı kullanılarak oluşturulan modellerin nihai ROC-AUC skoru ile değerlendirildi. XGBoost için ROC-AUC puanları \%71,5 ile \%99,9 arasında değişmekteyken, CatBoost için ROC-AUC puanları \%72,4 ile \%99,9 arasında değişim gösterdi. Bu iki sınıflandırıcı farklı adımlarda çok yakın performans göstermiş olsalar da, CatBoost genel olarak XGBoost'a kıyasla biraz daha iyi bir sonuç ortaya koymuştur. Gelecekteki çalışmalarda, daha doğru sonuçlar elde edebilmek için derin öğrenme bazlı sınıflandırma methodlarının denenmesi ve ek özniteliklerin entegre edilmesi üzerine çalışmalar yapılacaktır. Daha büyük veri kümeleri kullanılması önerilen gecikme tahmini yaklaşımının, daha etkin çıktılar ve performans iyileştirmeleri sağlayacaktır. Ancak, daha büyük veri kümeleri elde edilmesi, işlenmesi ve derin öğrenme modellerinin denenmesi için daha yüksek performanslı donanımsal, işlemci ve hafıza, kaynaklara ihtiyaç duyulacaktır. Bu zorlukların üstesinden gelmek ve daha yüksek performanslı çözümler sunmak için çeşitli stratejiler ve teknikler geliştirilmeye devam edilecektir.
Location and Distribution Decisions in an Earthquake Relief Network
(Kadir Has Üniversitesi, 2015) Yenice, Zeren D.; Samanlıoğlu, Funda
In this thesis, a multi-objective mathematical model was developed in order to configure part of the earthquake relief network in Istanbul, Turkey. The aim of the mathematical model was to help decision makers decide on the locations of storage areas for relief aids as well as distribution of relief aids from these areas to temporary shelter areas while minimizing expected total distribution distance, expected total earthquake damage risk factor of storage areas and expected total unsatisfied demand penalty cost. In the model, demands of the population, coverage restrictions, and storage area capacity restrictions were taken into consideration. The data related to the potential storage areas and shelter locations were obtained from Kadıköy municipality and İstanbul metropolitan municipality (IMM). The earthquake damage risk was determined based on possible earthquake scenarios given in Japan International Cooperation Agency's (JICA) report. The mathematical model was implemented in a pilot area, Kadiköy, and sample efficient solutions were obtained in order to prepare inventory and distribution plan.
Designing of an Enterprise Product Innovation System for Companies
(Kadir Has Üniversitesi, 2018) Demirhan, Mehmet Özhan; Ayağ, Zeki
Today, innovation is the most decisive factor for companies to grow and gain competitive edge in the market. In particular, firms support a product innovation policy in order to be permanent in the market. Product innovation refers to the emergence of new products and the development of existing products. By means of companies' product innovation reduces expenditure; new customers win and make high profits. AHP Method with a numerical example to evaluate the projects in this system. Organizational, technical, strategic and financial criteria have been examined. In the selection of the projects, companies have given much importance to the financial and organizational criteria. This research is expected to be very useful for evaluating and selecting projects for companies.
Co-Optimization Models of Generation and Transmission Investments With Market-Clearing Equilibrium
(Kadir Has Üniversitesi, 2018) Uyan, Zeki; Çelebi, Emre
Methods for co-optimizing transmission and generation investments including bi-level or multi-level problems consider trade-offs with market operations and interactions in electric power supply and demand. Under fairly general conditions it is known that simultaneous solution of these multi-level models using complementarity problems can give more useful results than iterative optimization methods or single-level optimization of generation or transmission expansion alone. Hence in this thesis we provide mixed complementarity problem formulations for transmission and generation expansion models with electricity market-clearing models. in this study we have considered co-optimization models formulated as bi-level programming problems as well as single-level mixed complementarity problems. in the upper level of the bi-level problem the system operator decides on the transmission expansion plans while anticipating the decisions in the lower level of the problem. The lower level problems present models of generation expansion and oligopolistic competition among power generators in the market where we examine perfect competition models to Cournot game among generators. This model is essentially an economic equilibrium problem for electricity markets that is defined by the optimality conditions that examine system operator’s and generators’ expansion behavior along with supply-demand balance in the market. These models will be handful for planning generation/transmission expansions and analyzing the relations between these expansions and the market outcomes. We have simulated market outcomes and expansion decisions in a 6-bus test system and a realistic Turkish electricity market under two different market structures (perfect competition and Nash-Cournot). Furthermore four different scenarios considering carbon costs and feed-in-tariffs (FiT) for Turkish electricity market for December 2020 are simulated and results are examined. Scenario considering both carbon costs and FiT have provided relatively better results in terms of social welfare.
Decarbonization Pathways For Turkish Power System Using The Leap Model Leap Modeli Kullanılarak Türkiye Elektrik Sistemi için Dekarbonizasyon Yolları
(Kadir Has Üniversitesi, 2021) Özer, Fatma Ece; Kirkil, Gökhan; Yücekaya, Ahmet Deniz
The negative impact of GHG released into the atmosphere on global warming cannot be ignored. Fossil-fueled power plants constitute a large part of Turkey's electricity production, as every country has a growing economy. Therefore, the electricity generation sector accounts for a significant portion of GHG emissions in Turkey. In addition to national bindings such as the Paris Agreement and the Kyoto Protocol, it is known that the Republic of Turkey aims to make not only electricity but also energy production greener in the coming years, in line with its own efforts. For this purpose, there are different modeling studies in the literature. This thesis aims to model Turkey's electricity generation sector in 2017, reveal the current situation, and then analyze how a greener and sustainable energy transformation will be possible with different scenarios and different main factors. In this direction, Turkey's electricity generation sector was modeled using the LEAP tool, then the decarbonization scenarios created within the openENTRANCE project were adapted to Turkey's data, and the numerical results of the scenarios were compared. As a result, it has been revealed that social awareness, adaptation to new technologies, and incentives of decision-makers are all critical factors in this regard.
A Decision Support System for Assembly Line Balancing Problem
(Kadir Has Üniversitesi, 2015) Ringim, İbrahim Uba; Yücekaya, Ahmet Deniz
Assembly line balancing problems are generally considered to be complicated in real life. Like most complicated real life assembly line balancing problems obtaining a good solution is much easier than finding an optimal solution especially with big size problems. As a result, many heuristic approaches have been developed to find good optimal solutions to those problems. In this study, we develop a decision support system that solves a deterministic assembly line balancing problem using three heuristic approaches. The objectives considered are: minimizing the number of workstations, maximization of line efficiency and minimization of balance delay. Our aim is using the decision support system created; user can enter any value into the system and obtain 3 different results. The results obtained are feasible enough which shows that the decision support system works and can be able to solve even larger problems if the correct formula is applied
A Fuzzy Ahp Approach for Financial Performance Evaluation Airline Companies
(Kadir Has Üniversitesi, 2012) Gürel, Sinem; Ayağ, Zeki
Previous researches focused on operation performance. This thesis purposes for evaluating the financial performance of the airlines. In order to achieve financial objectives to be incorporated into the financial performance of their degree. It is a method to measure the results of a company?s operations in a monetary term. The problem is modeled by multi-criteria decision making (MCDM) one. Multi criteria decision making (MCDM) is a method of the most important fields of operations research and deals with the problems that include multiple and conflicting objectives. It is obvious that when more than objective exists in the problem, making a decision becomes more complex. To solve the problem, I used the fuzzy numbers to explain their values. After that, I used a method of fuzzy multi criteria group decision making (FMCGDM) as Fuzzy Analytic Hierarchy Process (FAHP) to solve the problem of the evaluation of airlines? financial performances. At first time the decision making process of the financial performance is investigated, when financial ratios are given by a fuzzy function, they are obtained through classical methods. After that, we will discuss the main advantages of the new approach. Finally, we illustrate an experimantal model of evaluation of the three domestic airlines? financial performance in Turkey.
Analysis of the Stochastic Skiving Stock Problem
(Kadir Has Üniversitesi, 2022) KARACA, TOLGA KUDRET; Samanlıoğlu, Funda
This study addresses the stochastic version of the one-dimensional skiving stock problem (SSP), a rather recent combinatorial optimization challenge. The tradi tional SSP aims to determine the optimal structure that skives (combines) small items of various sizes side-by-side to form as many large items (products) as possible that satisfy a target width. This study considers a single-product and multi-product cases for the stochastic SSP. First, two-stage stochastic programming model is pre sented to minimize the total cost for the single product stochastic SSP which is under random demand. Integration of the Column Generation, Progressive Hedging Al gorithm, and Branch and Bound is proposed where Progressive Hedging Algorithm is embedded in each node of the search tree to obtain the optimal integer solution. Next, the single product stochastic model is extended to the multi-product, multi random variable model with the additional costs as a large size complex model. To examine this large-sized stochastic N P-hard problem, a two-stage stochastic programming approach is implemented. Moreover, as a solution methodology, this problem is handled in two phases. In the first phase, the Dragonfly Algorithm constructs minimal patterns as an input for the next phase. The second phase executes a Sample Average Approximation method that provides solutions for the stochastic production problem with large size scenarios. Results indicate that the two-phase heuristic approach provides good feasible solutions under numerous sce narios without requiring excessive execution time. Finally, a multi-objective case for the deterministic SSP is analyzed where the objectives are minimization of the trim loss (waste), number of items in each product by considering the quality aspect, and number of pattern changes as the set-up. Lexicographic method is preferred for the multi-objective approach where preferences are ranked according to their importance. Column generation and Integer programming are further used to solve the multi-objective problem. In addition, a heuristic is proposed for the same multi objective problem.
Building a Framework for Adopting Lean Principles To Achieve Sustainability in Solar Energy Firms: Turkiye as a Case Study
(Kadir Has Üniversitesi, 2022) Aldewachi, Bilal; Zeki Ayağ
The two terms lean, and sustainability have become one of the most important terms in the field of business because of their role in developing the work in line with increasing profits on the one hand and taking into account the future on the other. Solar energy firms are witnessing great competition to meet energy requirements and suffering from a huge amount of waste, which negatively affects in achieving sustainability. Hence, this study aims to build a framework for solar energy firms to achieve sustainability dimensions through adopting lean principles. The framework will help to solve problems that the solar energy sector is facing. The method in the study included reviewing the literature to examine the founding of the relation between lean and sustainability and using a questionnaire that was directed to the responsible people in Turkish solar energy companies. The results of the survey were analyzed to (1) Discover what the people in charge in solar energy firms think about lean and sustainability; (2) Measure the possible relationship between lean principles and sustainability dimensions by applying a linear regression and non-linear regression test; (3) Using the results of the second point to build the framework. The final results indicated a high level of the relative importance of the two terms from the perspective of the people in charge in the solar energy firms. Besides, the study found a relationship between adopting the two principles of lean, pull, and flow and achieving economic, social resources, and technology dimensions of sustainability; this finding is represented in a framework.
A Behavioral Study for Examining the Compliance of Pricing Models in Revenue Management Theory With the Decisions of Human Decision Makers
(Kadir Has Üniversitesi, 2020) Erkol, Cüneyt; Çavdaroğlu, Nur; Ağca Aktunç, Esra; Çavdaroğlu, Nur; Aktunç, Esra Ağca
This study involves four computer-based experiments based on different assumptions which are performed in a laboratory-setting. The behavior patterns of the subjects and the degree of deviation of these behaviors from optimal strategies are analyzed by various statistical methods. The common aim of the experiments examined in this thesis is to understand how successful Revenue Management theoretical models are in explaining real human behavior. In various cases, it has been possible to determine in which direction deviations from theoretical models occur and causes can be understood. In static competitor price treatment (in experiments 1 and 2), subjects exhibit a higher tendency to underprice. The "gambler's fallacy" bias is the dominant behavioral pattern observed in dynamic price setting. Humans consistently make pricing decisions below theoretical optimum, and only a small minority of the subjects are able to make optimum pricing decisions, supporting the presence of bounded rationality. Higher cognitive reflection skills help perform decisions closer to optimal, although not significantly better. Maximizing tendency does not show significance in meeting neither the optimum price, nor the potential revenue. Higher risk appetite makes better decisions in a dynamic competitor price setting. Neither the impact of learning effect, nor the demand-chasing bias is prevalent in the findings. Anchoring on the competitor price is observable in dynamic price setting. The study is useful in revealing the human factor issues that companies aiming to increase their profitability should pay attention to. Furthermore, the study can also be helpful in determining information to be provided to decision makers by an effective decision support system, and it proposes recommendations regarding the measures companies can take to improve human decision makers' decisions.
Models for Electricity Demand Forecasting, Classification, and Imbalance Reduction in Competitive Markets
(Kadir Has Üniversitesi, 2023) Yükseltan, Ergün; Yücekaya, Ahmet Deniz; Bilge, Ayşe
In liberalized energy markets, hourly forecasts of consumers and producers are crucial for efficiently using energy resources and reducing environmental impacts. In this study, the countries’ consumption in the ENTSO-E common network between 2006 and 2018 was analyzed using the time series method. With the created model, short, medium, and long-term demand forecasts are made using Fourier Series Expansion. In order to improve the error rate of short-term forecasts, a hybrid model was created with alternatively created feedback and autoregressive methods. While annual forecasts are made with an average error rate of 6%, the error rate in daily forecasts is around 4.5%. With the hybrid models created, hourly estimates can be made with approximately 1.5% and 1% error rates. Accurate estimations are of great importance in terms of the efficiency of energy markets, and the emergence of energy storage opportunities with the developing technology increases this importance. For this reason, the amount of imbalance was estimated by using the forecast result of the hybrid model in the Turkish Energy Market, and a strategy was developed to reduce the imbalance cost accordingly. With this strategy, simulations have been made for situations with and without storage, and the results have been shared.
Minimizing the Defect Rate Using Six Sigma Dmaic Method; a Case Study in Supsan A.ş.
(Kadir Has Üniversitesi, 2016) Aslan, Nazmiye Yağmur; Ayağ, Zeki
Today manufacturing industries are highly impacted by the fast changing economic conditions. In this scenario, manufacturing industries become face to face with global competition due to globalization. The major problems of these industries are declining profit margin, customer demand for high quality product and product variety. Therefore so many organizations, especially in manufacturing sector, understand the importance of quality. Companies try to implement various strategies and innovations for enhancing their producing process. A very powerful philosophy in this area is Six Sigma. The aim of Six Sigma is to reduce cost, waste and increase productivity to produce high-quality products. So this study is applied to improve the quality of the manufactured valves, reduce the manufacturing waste and increase the yield of the manufacturing process by applying the Six Sigma methodology. Also comparison analysis is made between approaches as DMAIC, Jidoka, Value Stream Tranformation, Kaizen. In result of the analysis DMAIC is chosen for this project. DMAIC approach is a business strategy help to improve business profitability and efficiency of operations to meet customer needs and expectations. In this search discusses the quality and productivity improvement in an manufacturing enterprise which is called SUPSAN. And this search deals with an application of Six Sigma DMAIC methodology in SUPSAN to improve quality performance, to identify root causes of failure in Stellite Coating Process which is found with help of DMAIC tools. Several tools are used to identify root causes and situation analysed with help of Pareto Charts, FMEA, SIPOC, C&E Diagram. In improve phase Process Capability Analysis and Implementation Plan are made. Also improvement solutions are applied. Then with the help of tools as Control Charts, SPC Analysis results tracked, documented and controlled.In SUPSAN, the appliation of Six Sigma DMAIC methodology resulted in a reduction in the defect rate from 4.34% to 2%.
Multi-objective disaster relief logistics
(Kadir Has Üniversitesi, 2018) Samarah, Mahdi M; Agca Aktunc, Esra
Disaster relief logistics is one of the major fields of operations research. Deciding the locations of depots before the disaster by minimizing total costs and total distances between nodes of demands and these depots is the main purpose of this study. The efficiency of disaster relief logistics is expressed in terms of the total transportation cost. The other objective function is considering minimizing total accumulated time to represent efficacy to supply different number of pallets which include basic materials and necessary types of foods. Equity is represented by minimizing the percentage of unsatisfied demand achieved by balancing the capability to serve demand nodes and the ability to diminish number of pallets that would not reach the nodes. Dealing with uncertainty in both demands and distances create different scenarios for our study and the results explain how each objective function affects the logistics decisions for each scenario.
Determination of Time-Of Prices in Electricity Markets Using Clustering Analyses
(Kadir Has Üniversitesi, 2016) Hussain, Mohsan; Çelebi, Emre
in this thesis a clustering analysis to determine the blocks (clusters) of hours for time-of-use (TOU) pricing scheme is proposed and different clustering algorithms are compared using different measures i.e. change in overall revenue mean absolute percent error and adjusted coefficient of determination (R2) from multiple linear regression analyses. Hourly electricity price and demand (load) data for two seasons (winter and summer) from Pennsylvania-New Jersey-Maryland (PJM) wholesale electricity market for 2014-2015 is used and based on detailed descriptive analyses and observations three blocks of hours (off-peak mid-peak and on-peak) are presented. in R software two clustering algorithms (agglomerative hierarchical and k-means) are employed and several clusters for summer and winter weekday hours are formed. The average of the hourly electricity prices in the same cluster for off-peak mid-peak and on-peak hours determines the TOU pricing scheme (hours in each cluster and prices for each clusters). These prices are compared to real-time pricing (RTP) rates in terms of change in overall revenue collected (price*load) and mean absolute percent error with respect to RTP rates. Finally in order to measure the significance of the TOU price and the demand relationship multiple linear regression analyses are performed. in the regression models dependent variable is the TOU price (or logarithm of it) and independent variables are the average load (or logarithm of it) of the TOU block of hours lagged TOU price and lagged TOU average load as well as categorical variables for off-peak mid-peak and on-peak hours for each TOU pricing scheme. Using Minitab software different regression models are built and adjusted R2 significance of regression coefficients and the significance of the overall model are computed. The significant models (with 95% confidence) are reported and the TOU clusters with higher adjusted R2 values are determined. Moreover in order to measure the autocorrelation effect Durbin-Watson statistics for each significant regression model are calculated and positive correlation among dependent and independent variables are reported. These analyses can be used by electricity market retailers distribution companies as well as regulatory bodies in determining TOU time blocks (clusters) and prices.
The Effect of Human-Computer Interaction (hci) Factors on Students' E-Learning Acceptance and Success During Covid-19 Pandemic
(Kadir Has Üniversitesi, 2022) Al-Sayid, Fareed; KIRKIL, GOKHAN
The purpose of this study is to investigate the effect of human-computer interaction (HCI) factors on the ease of use and usefulness of e-learning and their success (SS) at the time of the COVID-19 pandemic, to investigate if students' activities on systems moderate the relationship between the main constructs in the proposed model called "e-LASS," which goes beyond technology adoption, and to explore non-linear relationships between these constructs. Moreover, this study proposes a comprehensive model called "e-LASS2," integrating the main (Technology Acceptance Model- TAM) factors included in e-LASS and a unified theory of acceptance and use of technology (UTAUT) factors. To answer the questions that addressed these relations in the first and second parts, the researcher surveyed 103 students from Kadir Has University whose grade and activity logs were accessible, while the data related to the third part were collected via an online survey conducted on 232 students utilizing the Khas Learn system of Kadir Has University in Turkey. The results of the first and second parts show that most of the hypotheses have been proven, three comprehensive conceptual models were developed, the grades in the online courses improved students’ GPA, and the logs moderate the effects of HCI on TAM which together explained 54.9% of the variance in SS (student success), usefulness is the strongest determinant of SS, and non-linear models (cubic, quadratic, logarithmic, and s-curve) performed better in the description for the correlations when compared to linear models. The findings of the integrative approach in the third part reveal that the main predictors of students' success are behavior intention, ease of use, usefulness, visual design, and learner interface interactivity which explained 53.6% of perceived success in using the system.
Detection of Fraudulent Activities in Mobile Display Advertising
(Kadir Has Üniversitesi, 2017) Kaya, Safiye Şeyma; Çavdaroğlu, Burak
Most of the marketing expenditures in mobile advertising are conducted through realtime bidding (RTB) marketplaces in which ad spaces of the sellers (publishers) are auctioned for the impression of the buyers’ (advertisers) mobile apps. One of the most popular cost models in RTB marketplaces is cost per install (CPi). in a CPi campaign publishers place mobile ads of the highest bidders in their mobile apps and are paid by advertisers only if the advertised app is installed by a user. CPi cost model causes some publishers to conduct some infamous fraudulent activities known as click spamming and click injection. A click spamming publisher executes clicks for lots of users who haven’t made them. if one of these users hears about the advertised app organically (say via TV commercial) and installs it this installation will be attributed to the click spamming publisher. in click injection the fraudulent publisher’s spy app monitors the user’s activities in the app market to detect when a mobile app is downloaded on her device and triggers a click attributed to the fraudster right before the installation completes. in this study we propose a novel multiple testing procedure which can identify click spamming and click injection activities using the data of click-to-install time (CTiT) the time difference between the click of a mobile app’s ad and the first launch of the app after the installation. in a sample set of publishers we show that our procedure has a falsepositive error rate of at most 5%. Finally we run an experiment with 15263 publishers. According to the results of the experiment a total of 1474 fraudulent publishers are successfully detected.

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