Hekimoğlu, Mustafa

Hekimoğlu, Mustafa

Name Variants

Hekimoğlu, Mustafa
M.,Hekimoğlu
M. Hekimoğlu
Mustafa, Hekimoğlu
Hekimoglu, Mustafa
M.,Hekimoglu
M. Hekimoglu
Mustafa, Hekimoglu
Hekimoglu,M.
Hekimoglu, M.
Hekimoğlu, M.

Job Title

Doç. Dr.

Email Address

Mustafa.hekımoglu@khas.edu.tr

Full item page

Scholarly Output

30

Articles

21

Citation Count

0

Supervised Theses

3 Scholarly Output Search Results

Now showing 1 - 10 of 29

Yedek Parçaların Talebe Yönelik Eklemeli Üretiminde Lazer Cilalamanın Optimum Karar Verme Politikası Üzerinde Etkisi
(2020) Hekimoğlu, Mustafa; Hekimoğlu, Mustafa; Ulutan, Durul; Ulutan, Durul
Eklemeli imalatın yakınlarda bulunan bir 3D yazıcı kullanılarak sermaye ürünlerinin yedek parça ihtiyaçlarını karşılamak için kullanılması giderek yaygınlaşmaktadır. Böyle bir teknoloji, talebe-binaen parça üretimini mümkün kılarak arızaların rassallığı nedeniyle tutulan yedek parça envanterinin önemli bir kısmını ortadan kaldırma imkânı sunmaktadır. 3D yazıcı kullanımının en büyük sorunlarından biri olan basılı ve orijinal parçalar arasındaki kalite farkı, yüzey pürüzlülüğünü hafifleten ve ek maliyet terimi karşılığında parçaların güvenilirliğini artıran lazer parlatma kullanılarak azaltılabilir. Farklı parametreler kullanılarak, parçaların güvenilirliği, sermaye ürünlerinin ihtiyaçlarına ve sistemlerin durumuna göre değiştirilebilir. Bu çalışmada, basılı parçaların yüzey pürüzlülüğü ve güvenilirliğinin orijinal yedek parçaların envanter seviyeleri ile birlikte optimize edilmesi sorunu ele alınmıştır. Çalışmada, sınırlı bir planlama ufku üzerinde rastgele arızalara maruz kalan sabit sayıda özdeş makinadan oluşan bir üretim tesisi dikkate alınmıştır. Matematiksel analiz ve ayrıntılı sayısal deneyler kullanılarak, sistemin uygun maliyetli yönetimi için kritik olabilecek optimum kontrol politikası ve maliyet parametreleri arasındaki ilişki gösterilmiştir.
Modeling Repair Demand in Existence of a Nonstationary Installed Base
(Elsevier, 2023) Hekimoglu, Mustafa; Hekimoğlu, Mustafa; Karli, Deniz
Life cycles of products consist of 3 phases, namely growth, maturity, and decline phases. Modeling repair demand is particularly difficult in the growth and decline stages due to nonstationarity. In this study, we suggest respective stochastic models that capture the dynamics of repair demand in these two phases. We apply our theory to two different operations management problems. First, using the moments of spare parts demand, we suggest an algorithm that selects a parametric distribution from the hypergeometric family (Ord, 1967) for each period in time. We utilize the algorithm in a single echelon inventory control problem. Second, we focus on investment decisions of Original Equipment Manufacturers (OEMs) to extend economic lifetimes of products with technology upgrades. Our results indicate that the second moment is sufficient for growing customer bases, whereas using the third moment doubles the approximation quality of theoretical distributions for a declining customer base. From a cost minimization perspective, using higher moments of demand leads to savings up to 13.6% compared to the single-moment approach. Also, we characterize the optimal investment policy for lifetime extension decisions from risk-neutral and risk-averse perspectives. We find that there exists a critical level of investment cost and installed base size for profitability of lifetime extension for OEMs. From a managerial point of view, we find that a risk-neutral decision maker finds the lifetime extension problem profitable. In contrast, even a slight risk aversion can make the lifetime extension decision economically undesirable.
Demand Classification for Spare Parts Supply Chains in the Presence of Three Dimensional Printers
(Kadir Has Üniversitesi, 2022) İşler, Zülal; Hekimoğlu, Mustafa; Hekimoğlu, Mustafa
Three-dimensional printers (3DPs) are currently the source of the supply chain and are used to ensure spare parts supply in case of shortages. However, the reliability of the part produced in 3DP is lower than the original part supplied by the original equipment manufacturer (OEM). Failure of parts creates demand and the failure probability of original and printed part is different than each other. Thus, knowing the total demand distribution have great importance in optimizing the order quantity given to the OEM in the presence of 3DPs. In this study, the demand distribution of system failures has been determined by using the distribution classification methods put forward by Ord (1967) and Adan et al. (1995). In line with the results, according to study of Ord(1967), demand distribution is found as Hypergeometric and Binomial distribution. Discrete distribution family of Adan et al. (1995) gives Binomial distribution for the system demand. All results are tested with chi-square test and likelihood ratio test.
Evaluation of Water Supply Alternatives for Istanbul Using Forecasting and Multi-Criteria Decision Making Methods
(Elsevier Ltd, 2020) Savun Hekimoğlu, Başak; Hekimoğlu, Mustafa; Erbay, Barbaros; Hekimoğlu, Mustafa; Burak, Selmin
Water scarcity is one of the most serious problems of the future due to increasing urbanization and water demand. Urban water planners need to balance increasing water demand with water resources that are under increasing pressure due to climate change and water pollution. Decision makers are forced to select the most appropriate water management alternative with respect to multiple, conflicting criteria based on short and long term projections of water demand in the future. In this paper, we consider water management in Istanbul, a megacity with a population of 15 million. Purpose: The purpose of this paper is to develop a method combining demand forecasting with multi-criteria decision making (MCDM) methods to evaluate five different water supply alternatives with respect to seven criteria using opinions of experts and stakeholders from different sectors. Methodology: To combine forecasting with MCDM, we design a data collection method in which we share our demand forecasts with our experts. For demand forecasting, we compare Holt-Winters, Seasonal Autoregressive Integrated Moving Average (S-ARIMA), and feedforward Artificial Neural Network (ANN) models and select S-ARIMA as the best forecasting model for monthly water consumption data. Generated demand projections are shared with experts from different sectors and collected data is evaluated with Fuzzy Theory using two distinct MCDM models: Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE). Also our analyses are complemented with two sensitivity analyses. Findings: Our results indicate that greywater reuse is the best alternative to satisfy the growing water demand of the city whereas all experts find desalination and inter-basin water transfer as the least attractive solutions. In addition, we adopt the PROMETHEE GDSS procedure to obtain a GAIA plane indicating consensus among experts. Furthermore, we find that our results are moderately sensitive to the number of experts and they are insensitive to changes in experts’ evaluations. Novelty: To the best of our knowledge, our study is the first one incorporating water demand and supply management concepts into the evaluation of alternatives. From a methodological perspective, water demand projections have never been used in an MCDM study in the literature. Also, this paper contributes to the literature with a mathematical construction of consensus and Monte Carlo simulations for the sufficiency of experts consulted in a study.
Multi-Criteria Decision-Making Analysis for the Selection of Desalination Technologies
(2022) Hekimoğlu, Mustafa; Hekimoğlu, Mustafa; Savun-hekimoğlu, Başak; Erbay, Barbaros; Gazioğlu, Cem
Accessible fresh water resources for drinking and usage are very limited in our world. Furthermore, these limited fresh water resources are gradually decreasing due to climate change, industrialization, and population growth. Despite the ever-increasing need for water, the inadequacies in our resources have made it critical to develop alternative drinking and utility water production methods. Desalination, one of the most important alternatives for fresh water supply, is on the rise on a global scale. Desalination facilities use various thermal and membrane techniques to separate water and salt. Concentrated brine, which contains desalination chemicals and significant amounts of salt, and is formed in high volumes from desalination processes, is also a concern. This article compares various desalination techniques using a multi-criteria decision-making method. The findings show that the Reverse Osmosis & Membrane Crystallization process is the most preferred technology due to its cost advantages as well as operational efficiency. Similarly, Multistage flash &Electrodialysis, the least preferred alternative, has been criticized for its low cost-effectiveness. These results suggest that cost and operational efficiency will continue to be the main drivers in the evaluation of desalination technologies in the near future.
Evaluation of Various Machine Learning Methods To Predict Istanbul’s Freshwater Consumption
(2023) Hekimoğlu, Mustafa; Hekimoğlu, Mustafa; Çetin, Ayse Irem; Kaya, Burak Erkan
Planning, organizing, and managing water resources is crucial for urban areas and metropolitans. Istanbul is one of the largest megacities, with a population of over 15 million. The large volume of water demand and increasing scarcity of clean water resources make long-term planning necessary for this city, as sustained water supply requires large-scale investment projects. Successful investment plans require accurate projections and forecasting for freshwater demand. This study considers different machine learning methods for freshwater demand forecasting for Istanbul. Using monthly consumption data provided by the municipality since 2009, we compare forecasting accuracies of ARIMA, Holt-Winters, Artificial Neural Networks, Recursive Neural Networks, Long-Short Term Memory, and Simple Recurrent Neural Network models. We find that the monthly freshwater demand of Istanbul is best predicted by Multi-Layer Perceptron and Seasonal ARIMA. From the predictive modeling perspective, this result is another indication of the combined usage of conventional forecasting models and novel machine learning techniques to achieve the highest forecasting accuracy.
Decoding Compositional Complexity: Identifying Composers Using a Model Fusion-Based Approach With Nonlinear Signal Processing and Chaotic Dynamics
(Pergamon-elsevier Science Ltd, 2024) Mirza, Fuat Kaan; Baykaş, Tunçer; Baykas, Tuncer; Hekimoğlu, Mustafa; Hekimoglu, Mustafa; Pekcan, Mehmet Önder; Pekcan, Onder; Tuncay, Gonul Pacaci
Music, a universal medium that effortlessly transcends the confines of language and culture, serves as a vessel for the distinctive expression of a composer's ingenuity, particularly palpable through the elaborate symphony of melodies, harmonies, and rhythms. This phenomenon is acutely observable in the realm of Turkish Classical Music, where the identification of individual composers poses a formidable challenge due to a confluence of diverse stylistic expressions and sophisticated techniques. Shaped by centuries of cultural interchanges, this genre is celebrated for its convoluted rhythmic frameworks and deep melodic modes, often exhibiting fractal characteristics that compound the complexity of composer classification based on mere audio signals. In response to these complexities, this study introduces an advanced analytical paradigm that amalgamates Multi-resolution analysis, spectral entropy assessments, and a spectrum of multidimensional chaotic and statistical descriptors. By invoking chaos theory, the research delineates distinct patterns and features inherent to musical compositions, subsequently deploying these discoveries for composer categorization. Employing a model fusion-based strategy, the approach utilizes esteemed base estimators for section-level probabilistic determinations, subsequently amalgamated at the song level through a Long Short-Term Memory (LSTM) neural network model to classify a corpus of 380 compositions from 15 distinct composers. The results of this study not only highlight the efficacy of chaos-based approaches in Musical Information Retrieval but also provide a nuanced understanding of the unique characteristics of Turkish Classical Music, thus advancing the boundaries of how musicological data is scrutinized and conceptualized within scholarly discourse.
The impact of the COVID-19 pandemic and behavioral restrictions on electricity consumption and the daily demand curve in Turkey
(Elsevier Sci Ltd, 2022) Bilge, Ayşe Hümeyra; Hekimoğlu, Mustafa; Yucekaya, A.; Bilge, A.; Aktunc, E. Agca; Hekimoglu, M.
The rapid spread of COVID-19 has severely impacted many sectors, including the electricity sector. The reliability of the electricity sector is critical to the economy, health, and welfare of society; therefore, supply and demand need to be balanced in real-time, and the impact of unexpected factors should be analyzed. During the pandemic, behavioral restrictions such as lockdowns, closure of factories, schools, and shopping malls, and changing habits, such as shifted work and leisure hours at home, significantly affected the demand structure. In this research, the restrictions and their corresponding timing are classified and mapped with the Turkish electricity demand data to analyze the estimated impact of the restrictions on total demand and daily demand profile. A modulated Fourier Series Expansion evaluates deviations from normal conditions in the aggregate demand and the daily consumption profile. The aggregate demand shows a significant decrease in the early phase of the pandemic, during the period March-June 2020. The shape of the daily demand curve is analyzed to estimate how much demand shifted from daytime to night-time. A population-based restriction index is proposed to analyze the relationship between the strength and coverage of the restrictions and the total demand. The persistency of the changes in the daily demand curve in the post-contingency period is analyzed. These findings imply that new scheduling approaches for daily and weekly loads are required to avoid supply-demand mismatches in the future. The longterm policy implications for the energy transition and lessons learned from the COVID-19 pandemic experience are also presented.
A Framework To Forecast Electricity Consumption of Meters Using Automated Ranking and Data Preprocessing
(Econjournals, 2023) Guzel, T.; Hekimoğlu, Mustafa; Çınar, H.; Çenet, M.N.; Oguz, K.D.; Yucekaya, A.; Hekimoglu, M.
Forecasting electricity consumption is crucial for the operation planning of distribution companies and suppliers and for the success of deregulated electricity markets as a whole. Distribution companies often need consumption forecasting for meters to better plan operations and demand fulfillment. Although it is easier to forecast the aggregated demand for a region, meter based demand forecasting brings challenging issues such as non-uniform usage and uncertain customer consumption patterns. The stochastic nature of the demand for electricity, along with parameters such as temperature, humidity, and work habits, eventually causes deviations from the expected demand. In this paper, real meter data from a regional distribution company is used to cluster the customer using their non-uniform usage and automated ranking mechanism is proposed to select the best method to forecast the consumption. The proposed end-to-end methodology includes data processing, missing value detection and filling, abnormal value detection, and mass reading for meters and is applied to regional data for the period 2017-2018 and provides a powerful tool to forecasts the demand in hourly and daily horizons using only the past demand data. Besides proposing effective methodologies for data preprocessing, 10 different regression methods, 7 regressors, 5 machine learning methods that include LSTM and Ar-net models are used to forecast the meter based consumption. The hourly forecasting errors in the demand, in the Mean Absolute Percentage Error (MAPE) norm, are <4% for most customer groups. The meter based forecast is then aggregated to reach a final demand which is then used for operation and demand planning. The proposed framework can be considered reliable and practical in the circumstances needed to make demand and operation decisions. © 2023, Econjournals. All rights reserved.
Optimum Laser Polishing Decision-Making for On-Demand Additive Manufacturing of Spare Parts: an Exploratory Study
(Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 2020) Hekimoğlu, Mustafa; Hekimoğlu, Mustafa; Ulutan, Durul; Ulutan, Durul
Additive manufacturing is increasingly being used for satisfying spare parts needs of capital products using a nearby 3D printer. Such a technology allows inventory managers to start manufacturing after the demand realization which eliminates significant portion of spare parts inventory being held due to random nature of component breakdowns. Quality difference between printed and original parts, which is one of the biggest problems of using 3D printers, can be decreased by the use of laser polishing which alleviates surface roughness and increases reliability of parts in exchange of an additional cost term. Using different parameters, reliability of parts can be altered depending on needs of capital products and systems’ status. In this study, the problem where surface roughness and reliability of printed parts are jointly optimized with inventory levels of original spare parts is considered. In the problem setting, a machine part consisting of a constant number of identical products which are subject to random breakdowns over a finite planning horizon is considered. Using mathematical analysis and exhaustive numerical experiments, the relationship between optimum control policy and cost parameters was shown, which might be critical for cost-effective management of the system.