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 - Scopus: 3Stock Price Forecasting Through Symbolic Dynamics and State Transition Graphs With a Convolutional Recurrent Neural Network Architecture(Springer Science and Business Media Deutschland GmbH, 2025) Mirza, F.K.; Pekcan, Ö.; Hekimoğlu, M.; Baykaş, T.Accurate stock price forecasting remains a critical challenge in financial analytics due to volatile market conditions, non-stationary dynamics, and abrupt regime shifts that often defy traditional modeling techniques. This study proposes a comprehensive framework for stock price forecasting that integrates symbolic dynamics, graph-based state representations, and deep learning. By converting continuous-valued stock prices into discrete symbolic states representing amplitude and trend information, the method constructs transition matrices capturing probabilistic relationships within financial time series. These transition matrices are then processed by a convolutional recurrent neural network (CRNN), in which convolutional layers isolate local spatial dependencies in the symbolic-state domain, while recurrent LSTM layers capture multi-scale temporal dynamics extending across multiple time horizons. Experimental evaluations are conducted over prediction horizons of 1 day, 10 days, and 100 days, spanning pre-COVID, COVID, and post-COVID market regimes. The results indicate that while longer prediction horizons naturally incur greater forecasting uncertainty due to compounding variability, the integration of symbolic-state preprocessing with deep temporal modeling demonstrates significant robustness in handling non-stationary financial environments. During the stable pre-COVID period, the proposed methodology achieves reductions in mean squared error (MSE) of up to 98% relative to the volatile COVID phase, highlighting its capability to effectively leverage well-defined market patterns in stable economic conditions. Furthermore, the model consistently delivers competitive forecasting performance across all prediction horizons and market regimes. Collectively, these findings emphasize the potential of symbolic-state-based deep learning architectures as a viable pathway to address the complexity and volatility characteristic of modern financial markets. © The Author(s) 2025.Article Yedek Parçaların Talebe Yönelik Eklemeli Üretiminde Lazer Cilalamanın Optimum Karar Verme Politikası Üzerinde Etkisi(2020) Hekimoğlu, Mustafa; Ulutan, DurulEklemeli 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.Conference Object Citation - WoS: 2Citation - Scopus: 3Optimum utilization of on-demand manufacturing and laser polishing in existence of supply disruption risk(Elsevier, 2022) Ulutan, Durul; Isler, Zulal; Kaya, Burak Erkan; Hekimoglu, Mustafa3D printing has moved from being a rapid prototyping tool to an additive manufacturing method within the last decade. Additive manufacturing can satisfy the need in dire situations where spare parts distribution is an issue but access to a 3D printer is much more likely and rapid than access to original parts. Managing inventories of spare parts can be tackled with more ease thanks to the reduced part types with additive manufacturing. While quality (in terms of reliability) of additively manufactured spare parts in terms of mechanical properties seem to be lower than original parts (particularly due to the inherent staircase appearance and the corresponding stress concentration zones that can lead to premature fatigue failure), use of post-processing subtractive techniques to correct such surface irregularities are found to improve reliability. While each process adds another layer of complexity to the cost minimization problem, demand uncertainty and risk of supply disruption represent the modern global problems faced recently. The problem tackled in this study is the joint optimization of the supply reliability considering the effect of laser polishing parameters and the demand uncertainty. In this problem, a condition of random breakdowns of identical products is considered. Also, the original supplier of machine components is subject to exogenous disruptions, such as strikes, raw material scarcity, or the COVID-19 pandemic. As a result, the optimum control policy with the right cost parameters was shown via numerical experiments originated from mathematical analyses. This optimality can be critical in managing the system in the best possible way, particularly during times of unforeseen circumstances such as pandemics. (C) 2022 Society of Manufacturing Engineers (SME). Published by Elsevier Ltd. All rights reserved. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the Scientific Committee of the NAMRI/SME.Article Citation - Scopus: 19Optimization of Wastewater Treatment Systems for Growing Industrial Parks(Elsevier B.V., 2023) Savun-Hekimoğlu, B.; İşler, Z.; Hekimoğlu, M.; Burak, S.; Karlı, D.; Yücekaya, A.; Akpınar, E.Wastewater treatment is one of the crucial functions of industrial parks as wastewater from industrial facilities usually contains toxic compounds that can cause damage to the environment. To control their environmental loads, industrial parks make investment decisions for wastewater treatment plants. For this, they need to consider technical and economic factors as well as future growth projections as substantial construction and operational costs of wastewater treatment plants have to be shared by all companies in an industrial park. In this paper, we consider the long-term capacity planning problem for wastewater treatment facilities of a stochastically growing industrial park. By explicitly modeling randomness in the arrival of new tenants and their random wastewater discharges, our model calculates the future mean and variance of wastewater flow in the industrial park. Mean and variance are used in a Mixed Integer Programming Model to optimize wastewater treatment plant selection over a long planning horizon (30 years). By fitting our first model to empirical data from an industrial park in Turkey, we find that considering the variance of wastewater load is critical for long-term planning. Also, we quantify the economic significance of lowering wastewater discharges which can be achieved by water recycling or interplant water exchange. © 2023 Elsevier B.V.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.Master Thesis Real Time Prediction of Delivery Delay With Machine Learning(Kadir Has Üniversitesi, 2023) Küp, Büşra Ülkü; Hekimoğlu, Mustafaİnternetin 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.Conference Object A Comparative Application of Machine Learning Approaches To Win-Back Lost Customers(Institute of Electrical and Electronics Engineers Inc., 2023) Yildirim, S.; Yucekaya, A.D.; Hekimoglu, M.; Ozcan, B.Today's consumer is more knowledgeable and conscious than in the past. For this reason, it is quite possible for consumers to leave their service/product providers and start receiving service from another service/product provider. Without a recovery strategy, companies often do not target their lost disloyal customer portfolio correctly and encounter the problem of lost customers. Lost customers can cause loss both in economic terms and in terms of business potential. At the same time, lost customers can also be considered as profits given to rival companies. What if the companies could foresee lost customers who would not want to receive service from them again? Could companies win back their customers? At this point, the article proposes using machine learning methods to recover lost customers for service providers. The customers that are likely to be lost in the future are estimated using the article's past stories of an automotive company's lost customers. The data used is completely real. LGBM, XGBoost, and Random Forest methods were used to estimate lost customers. Finally, the authors select the machine learning with the highest predictive success for customer recovery and discuss why this method might have worked well. © 2023 IEEE.Article Citation - WoS: 25Citation - Scopus: 25Maintenance Optimization for a Single Wind Turbine Component Under Time-Varying Costs(Elsevier, 2022) Schouten, Thijs Nicolaas; Dekker, Rommert; Hekimoglu, Mustafa; Eruguz, Ayse SenaIn this paper, we introduce a new, single-component model for maintenance optimization under timevarying costs, specifically oriented at offshore wind turbine maintenance. We extend the standard age replacement policy (ARP), block replacement policy (BRP) and modified block replacement policy (MBRP) to address time-varying costs. We prove that an optimal maintenance policy under time-varying costs is a time-dependent ARP policy. Via a discretization of time, the optimal time-dependent ARP can be found using a linear programming formulation. We also present mixed integer linear programming models for parameter optimization of BRP and MBRP. We present a business case and apply our policies for maintenance planning of a wind turbine gearbox and show that we can achieve savings up-to 23%.(c) 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )
