Hekimoğlu, Mustafa
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Hekimoğlu, Mustafa
M.,Hekimoğlu
M. Hekimoğlu
Mustafa, Hekimoğlu
Hekimoglu, Mustafa
M.,Hekimoglu
M. Hekimoglu
Mustafa, Hekimoglu
Hekimoglu,M.
Hekimoglu, M.
Hekimoğlu, M.
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.hekı[email protected]
Main Affiliation
Industrial Engineering
Status
Former Staff
Website
ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
Sustainable Development Goals
11
SUSTAINABLE CITIES AND COMMUNITIES
4
Research Products
17
PARTNERSHIPS FOR THE GOALS
0
Research Products
14
LIFE BELOW WATER
1
Research Products
8
DECENT WORK AND ECONOMIC GROWTH
0
Research Products
15
LIFE ON LAND
1
Research Products
1
NO POVERTY
0
Research Products
7
AFFORDABLE AND CLEAN ENERGY
2
Research Products
6
CLEAN WATER AND SANITATION
4
Research Products
12
RESPONSIBLE CONSUMPTION AND PRODUCTION
2
Research Products
16
PEACE, JUSTICE AND STRONG INSTITUTIONS
0
Research Products
9
INDUSTRY, INNOVATION AND INFRASTRUCTURE
8
Research Products
3
GOOD HEALTH AND WELL-BEING
1
Research Products
2
ZERO HUNGER
0
Research Products
4
QUALITY EDUCATION
0
Research Products
10
REDUCED INEQUALITIES
0
Research Products
13
CLIMATE ACTION
2
Research Products
5
GENDER EQUALITY
0
Research Products
This researcher does not have a Scopus ID.
This researcher does not have a WoS ID.
Scholarly Output
38
Articles
28
Views / Downloads
281/2272
Supervised MSc Theses
4
Supervised PhD Theses
1
WoS Citation Count
159
Scopus Citation Count
223
WoS h-index
7
Scopus h-index
8
Patents
0
Projects
0
WoS Citations per Publication
4.18
Scopus Citations per Publication
5.87
Open Access Source
16
Supervised Theses
5
| Journal | Count |
|---|---|
| International Journal of Production Economics | 3 |
| European Journal of Operational Research | 3 |
| Manufacturing Letters | 2 |
| International Journal of Environment and Geoinformatics | 2 |
| International Journal of Energy Economics and Policy | 2 |
Current Page: 1 / 5
Scopus Quartile Distribution
Competency Cloud
38 results
Scholarly Output Search Results
Now showing 1 - 10 of 38
Article Citation - WoS: 2Citation - Scopus: 2Decoding Rhythmic Complexity: a Nonlinear Dynamics Approach via Visibility Graphs for Classifying Asymmetrical Rhythmic Frameworks of Turkish Classical Music(Elsevier Science inc, 2025) Mirza, Fuat Kaan; Baykas, Tuncer; Hekimoglu, Mustafa; Pekcan, Onder; Tuncay, Gonul PacaciThe non-isochronous, hierarchical rhythmic cycles (usuls) of Turkish Classical Music (TCM) exhibit emergent temporal structures that challenge conventional rhythm analysis based on metrical regularity. To address this challenge, this study presents a complexity-oriented framework for usul classification, grounded in nonlinear time series analysis and network-based representations. Rhythmic signals are processed through energy envelope extraction, diffusion entropy analysis, and spectral transformations to capture multiscale temporal dynamics. Visibility graphs (VGs) are constructed from these representations to encode underlying structural complexity and temporal dependencies. Features derived from VG adjacency matrices serve as complexity-sensitive descriptors and enable high-accuracy classification (0.99) across 40 usul classes and 628 compositions. Energy envelope-derived graphs provide the most discriminative information, highlighting the importance of amplitude modulation in encoding rhythmic structure. Beyond classification, the analysis reveals self-organizing patterns and signatures of complexity, such as quasi-periodicity, scale-dependent variability, and entropy saturation, suggesting that usuls function as adaptive, nonlinear systems rather than metrically constrained patterns. The topological features extracted from the resulting graphs align with theoretical constructs from complexity science, such as modularity and long-range temporal correlations. This positions usul as an exemplary case for studying structured temporal complexity in cultural artifacts through the lens of dynamical systems. These findings contribute to computational rhythm analysis by demonstrating the efficacy of complexity measures in characterizing culturally specific rhythmic systems.Article Citation - Scopus: 1A Novel Multiscale Graph Signal Processing and Network Dynamics Approach to Vibration Analysis for Stone Size Discrimination via Nonlinear Manifold Embeddings and a Convolutional Self-Attention Model(Springer Wien, 2025) Mirza, Fuat Kaan; Oz, Usame; Hekimoglu, Mustafa; Aydemir, Mehmet Timur; Pural, Yusuf Enes; Baykas, Tuncer; Pekcan, OnderUnderstanding nonlinear dynamics is critical for analyzing the hidden complexities of vibrational behavior in real-world systems. This study introduces a graph-theoretic approach to analyze the complex nonlinear temporal patterns in vibrational signals, utilizing the Tri-Axial Vibro-Dynamic Stone Classification dataset. This dataset captures high-resolution acceleration signals from controlled stone-crushing experiments, providing a unique opportunity to investigate temporal dynamics associated with distinct stone sizes. A 12-level Maximal Overlap Discrete Wavelet Transform is employed to perform multiscale signal decomposition, enabling the construction of transition graphs that encode transient and stable structural characteristics. Conceptually, transition graphs are analyzed as dynamic networks to uncover the interactions and temporal patterns embedded within vibrational signals. These networks are studied using a comprehensive suite of complexity metrics derived from information theory, graph theory, network science, and dynamical systems analysis. Metrics such as Shannon and Von Neumann's entropy evaluate signal dynamics' stochasticity and information retention. At the same time, the spectral radius measures the network's stability and structural robustness. Lyapunov exponents and fractal dimensions, informed by chaos theory and fractal geometry, further capture the degree of nonlinearity and temporal complexity. Complementing these dynamic measures, static network metrics-including the clustering coefficient, modularity, and the static Kuramoto index-offer critical discernment into the network's community structures, synchronization phenomena, and connectivity efficiency. Manifold learning techniques address the high-dimensional feature space derived from complexity metrics, with UMAP outperforming ISOMAP, Spectral Embedding, and PCA in preserving critical data structures. The reduced features are input into a convolutional self-attention model, combining localized feature extraction with long-term sequence modeling, achieving 100% classification accuracy across stone-size categories. This study presents a comprehensive framework for vibrational signal analysis, integrating multiscale graph-based representations, nonlinear dynamics quantification, and UMAP-based dimensionality reduction with a convolutional self-attention classifier. The proposed approach supports accurate classification and contributes to the development of data-driven tools for automated diagnostics and predictive maintenance in industrial and engineering contexts.Article Citation - WoS: 36Citation - Scopus: 46Evaluation of Water Supply Alternatives for Istanbul Using Forecasting and Multi-Criteria Decision Making Methods(Elsevier Ltd, 2020) Savun Hekimoğlu, Başak; Erbay, Barbaros; Hekimoğlu, Mustafa; Burak, Selmin; Savun-Hekimoğlu, BasakWater 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.Article Citation - WoS: 1Citation - Scopus: 1Markdown Optimization with Generalized Weighted Least Squares Estimation(Springernature, 2022) Hekimoglu, MustafaRetailers increasingly apply price markdowns for their seasonal products. Efficiency of these markdown applications is driven by the accuracy of empirical models, especially toward the end of a selling season. In the literature, recent sales are recognized to be more important than older sales data for estimating the current period's demand for a given markdown level. The importance difference between the weeks of a selling season is addressed by weighted least squares (WLS) method with continuous weight functions of time. This study suggests a generalization of the weight functions and a method for optimizing their shape and discretization parameters to stimulate the predictive accuracy of models. We find that addressing the importance difference of recent sales observations using our generalized weight functions improves the forecast accuracy by up to 20%, and most of the improvement stems from our weight discretization method.Article Citation - Scopus: 3Optimum Utilization of On-Demand Manufacturing and Laser Polishing in Existence of Supply Disruption Risk(Elsevier Ltd, 2022) Erkan Kaya, Burak; Hekimoğlu, Mustafa; Ulutan, Durul; İşler, ZülalArticle An Integrated Framework for Internal Replenishment Processes of Warehouses Using Approximate Dynamic Programming(MDPI, 2025) Kalafat, Irem; Hekimoglu, Mustafa; Yucekaya, Ahmet Deniz; Kirkil, Gokhan; Ediger, Volkan S.; Yildirim, SendaWarehouses are vital in linking production to consumption, often using a forward-reserve layout to balance picking efficiency and bulk storage. However, replenishing the forward area from reserve storage is prone to delays and congestion, especially during high-demand periods. This study investigates the strategic use of buffer areas-intermediate zones between forward and reserve locations-to enhance flexibility and reduce bottlenecks. Although buffer zones are common in practice, they often lack a structured decision-making framework. We address this gap by developing an optimization model that integrates demand forecasts to guide daily replenishment decisions. To handle the computational complexity arising from large state and action spaces, we implement an approximate dynamic programming (ADP) approach using certainty-equivalent control within a rolling-horizon framework. A real-world case study from an automotive spare parts warehouse demonstrates the model's effectiveness. Results show that strategically integrating buffer zones with an ADP model significantly improves replenishment timing, reduces direct picking by up to 90%, minimizes congestion, and enhances overall flow of intra-warehouse inventory management.Conference Object Citation - Scopus: 1Blockchain Technology in Loyalty Program Applications(Association for Computing Machinery, 2022) Bozkurt,H.I.; Gemici,S.; Yucekaya,A.; Hekimoglu,M.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.Article Citation - Scopus: 6Forecasting Hourly Electricity Demand Under Covid-19 Restrictions(Econjournals, 2022) Kök, A.; Yükseltan, E.; Hekimoğlu, M.; Aktunc, E.A.; Yücekaya, A.; Bilge, A.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.Article Citation - WoS: 12Citation - Scopus: 15Residual Lstm Neural Network for Time Dependent Consecutive Pitch String Recognition From Spectrograms: a Study on Turkish Classical Music Makams(Springer, 2023) Mirza, Fuat Kaan; Gursoy, Ahmet Fazil; Baykas, Tuncer; Hekimoglu, Mustafa; Pekcan, OnderTurkish 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.Master Thesis Optimum Spare Parts Inventory Control in Existence of a Non-Stationary Installed Base(Kadir Has Üniversitesi, 2021) Kök, Ali; Hekimoğlu, MustafaIn 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.
