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

Main Affiliation

Industrial Engineering

Status

Former Staff

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Sustainable Development Goals Report Points

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Scholarly Output

31

Articles

21

Citation Count

0

Supervised Theses

4 Scholarly Output Search Results

Now showing 1 - 10 of 30

Demand Classification for Spare Parts Supply Chains in the Presence of Three Dimensional Printers
(Kadir Has Üniversitesi, 2022) İşler, Zülal; Hekimoğlu, Mustafa; Hekimoğlu, Mustafa; Industrial Engineering
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.
Citation - WoS: 9
Citation - Scopus: 13
Markov-Modulated Analysis of a Spare Parts System With Random Lead Times and Disruption Risks
(Elsevier Science Bv, 2018) Hekimoğlu, Mustafa; Hekimoğlu, Mustafa; van der Laan, Ervin; Dekker, Rommert; Industrial Engineering
Spare parts supply chains are highly dependent on the dynamics of their installed bases. A decreasing number of capital products in use increases the nonstationary supply-side risk especially towards the end-of-life of capital products. This supply-side risk appears to present itself through varying lead times coupled with supply disruptions. To model the nonstationary supply-side risk we consider an exogenous Markov chain that modulates random lead times and disruption probabilities. Assuming that order crossovers do not occur we prove the optimality of a state-dependent base stock policy. Later we conduct an impact study to understand the value of considering stochastic lead times and supply disruption risk in spare parts inventory control. Our results indicate that the coupled effect of random lead times and disruptions can be larger than the summation of individual effects even for moderate lead time variances. Also the effect of nonstationarity on total cost can be as large as the summation of all risk factors combined. In addition to this managerial insight we present a procedure for supply risk mitigation based on an empirical model and our mathematical model. Experiments on a real business case indicate that the procedure is capable of reducing costs while making the inventory system more prepared for disruptions. (C) 2018 Elsevier B.V. All rights reserved.
Citation - WoS: 31
Citation - Scopus: 38
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; Industrial Engineering
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.
Citation - Scopus: 0
Blockchain Technology in Loyalty Program Applications
(Association for Computing Machinery, 2022) Bozkurt,H.I.; Hekimoğlu, Mustafa; Gemici,S.; Yucekaya,A.; Hekimoglu,M.; Industrial Engineering
Loyalty programs provide income to the user as a result of their expenditures. It helps businesses if the customer ensures continuity. A loyalty program is based on the bilateral relationship between the customer and the business. It is important to be sure of the reliability of this bilateral relationship and to be able to follow it. Today, many companies are trying to incorporate Blockchain technology into their systems because they pay attention to reliability and transparency. In this study, a loyalty ecosystem based on Blockchain technology, which will include member businesses, change the shopping and reward experience, and encourage employees is explained. We propose an application named Decentralized Loyalty Token (DLOT) to be used by entities where the community is together and the spending potential is high such as restaurants, cafes, and e-commerce platforms that want to be included in this structure. Users will earn rewards while spending DLOT Tokens and can store and accumulate the rewards indefinitely or use them for any payment. In addition, users will be able to convert the loyalty Tokens into a digital value in other businesses included in Wallet. The details of the proposed Blockchain-based loyalty system are explained along with benefits to customers and entities. © 2022 ACM.
Citation - Scopus: 6
Forecasting Hourly Electricity Demand Under Covid-19 Restrictions
(Econjournals, 2022) Kök, A.; Yücekaya, Ahmet Deniz; Yükseltan, E.; Bilge, Ayşe Hümeyra; Hekimoğlu, M.; Hekimoğlu, Mustafa; Aktunc, E.A.; Yücekaya, A.; Bilge, A.; Industrial Engineering
The rapid spread of the COVID-19 pandemic has severely impacted many sectors including the electricity sector. The restrictions such as lockdowns, remote-working, and-schooling significantly altered the consumers’ behaviors and demand structure especially due to a large number of people working at home. Accurate demand forecasts and detailed production plans are crucial for cost-efficient generation and transmission of electricity. In this research, the restrictions and their corresponding timing are classified and mapped with the Turkish electricity demand data to analyze the impact of the restrictions on total demand using a multiple linear regression model. In addition, the model is utilized to forecast the electricity demand in pandemic conditions and to analyze how different types of restrictions impact the total electricity demand. It is found that among three levels of COVID-19 restrictions, age-specific restrictions and the complete lockdown have different effects on the electricity demand on weekends and weekdays. In general, new scheduling approaches for daily and weekly loads are required to avoid supply-demand mismatches as COVID-19 significantly changed the consumer behavior, which appears as altered daily and weekly load profiles of the country. Long-term policy implications for the energy transition and lessons learned from the COVID-19 experience are also discussed. © 2022, Econjournals. All rights reserved.
Multi-Criteria Decision-Making Analysis for the Selection of Desalination Technologies
(2022) Hekimoğlu, Mustafa; Hekimoğlu, Mustafa; Savun-hekimoğlu, Başak; Erbay, Barbaros; Gazioğlu, Cem; Industrial Engineering
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.
Citation - WoS: 12
Citation - Scopus: 18
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.; Industrial Engineering
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.
Citation - WoS: 0
Citation - Scopus: 0
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; Industrial Engineering; Electrical-Electronics Engineering; Molecular Biology and Genetics
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.
Citation - WoS: 5
Citation - Scopus: 5
Residual Lstm Neural Network for Time Dependent Consecutive Pitch String Recognition From Spectrograms: a Study on Turkish Classical Music Makams
(Springer, 2023) Mirza, Fuat Kaan; Baykaş, Tunçer; Gursoy, Ahmet Fazil; Hekimoğlu, Mustafa; Baykas, Tuncer; Pekcan, Mehmet Önder; Hekimoglu, Mustafa; Pekcan, Onder; Industrial Engineering; Electrical-Electronics Engineering; Molecular Biology and Genetics
Turkish classical music, characterized by 'makam', specific melodic configurations delineated by sequential pitches and intervals, is rich in cultural significance and poses a considerable challenge in identifying a musical piece's particular makam. This identification complexity remains an issue even for experienced musical experts, emphasizing the need for automated and accurate classification techniques. In response, we introduce a residual LSTM neural network model that classifies makams by leveraging the distinct sequential pitch patterns discerned within various audio segments over spectrogram-based inputs. This model's design uniquely merges the spatial capabilities of two-dimensional convolutional layers with the temporal understanding of one-dimensional convolutional and LSTM mechanisms embedded within a residual framework. Such an integrated approach allows for detailed temporal analysis of shifting frequencies, as revealed in logarithmically scaled spectrograms, and is adept at recognizing consecutive pitch patterns within segments. Employing stratified cross-validation on a comprehensive dataset encompassing 1154 pieces spanning 15 unique makams, we found that our model demonstrated an accuracy of 95.60% for a subset of 9 makams and 89.09% for all 15 makams. Our approach demonstrated consistent precision even when distinguishing makam pairs known for their closely related pitch sequences. To further validate our model's prowess, we conducted benchmark tests against established methodologies found in current literature, providing a comparative assessment of our proposed workflow's abilities.
Optimum Spare Parts Inventory Control in Existence of a Non-Stationary Installed Base
(Kadir Has Üniversitesi, 2021) Kök, Ali; Hekimoğlu, Mustafa; Hekimoğlu, Mustafa; Industrial Engineering
In spare parts supply chains, demand is profoundly dependent on the life cycle of the product. Thus, MROs should incorporate installed base information in demand forecasting to prevent production/service interruptions and high holding costs. MROs also try to exploit secondary markets as a cheap and expedited source of spare parts apart from the OEM. However, the secondary markets are not reliable since they have a limited and stochastic spare parts capacity. Therefore, MROs need to determine when and how much to order from two supply sources. Under the assumption of stationary demand, a mathematical model is developed for an inventory control model in a dual sourcing setup. Then, this model is extended by assuming a non-stationary demand by employing Hekimoğlu and Karlı (2021)'s demand model. Optimal ordering policies are derived when the lead time difference of suppliers is one period, under both stationarity assumptions. Heuristics policies are utilized when the lead time difference is more than one period. It is found that the Dual Index policy outperforms other considered heuristics, resulting in a satisfactory cost deviation from the optimum cost. The value of higher moment information in demand forecasting is measured by simulation studies. Information of the first two and three moments are found to be superior over the other for declining and growing installed bases, respectively. The same simulation study is conducted by presenting an estimation error to the first moment. Results showed that the information of higher moments could save costs up to 14.2% and 9.26% for growth and decline phases, respectively. Finally, empirical analyses are conducted on a company from the Turkish automotive sector by performing statistical tests. It is concluded that Hekimoğlu and Karlı (2021)'s demand model could be practical to model spare parts demand of automobiles in the growth phase.