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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
3
GOOD HEALTH AND WELL-BEING
1
Research Products
6
CLEAN WATER AND SANITATION
4
Research Products
7
AFFORDABLE AND CLEAN ENERGY
2
Research Products
9
INDUSTRY, INNOVATION AND INFRASTRUCTURE
7
Research Products
11
SUSTAINABLE CITIES AND COMMUNITIES
4
Research Products
12
RESPONSIBLE CONSUMPTION AND PRODUCTION
2
Research Products
13
CLIMATE ACTION
2
Research Products
14
LIFE BELOW WATER
1
Research Products
15
LIFE ON LAND
1
Research Products
This researcher does not have a Scopus ID.
This researcher does not have a WoS ID.
Scholarly Output
36
Articles
26
Views / Downloads
259/2006
Supervised MSc Theses
4
Supervised PhD Theses
1
WoS Citation Count
157
Scopus Citation Count
198
WoS h-index
7
Scopus h-index
7
Patents
0
Projects
0
WoS Citations per Publication
4.36
Scopus Citations per Publication
5.50
Open Access Source
14
Supervised Theses
5
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| Journal | Count |
|---|---|
| International Journal of Production Economics | 3 |
| European Journal of Operational Research | 3 |
| International Journal of Energy Economics and Policy | 2 |
| International Journal of Environment and Geoinformatics | 2 |
| Applied Sciences | 1 |
Current Page: 1 / 5
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Scholarly Output Search Results
Now showing 1 - 10 of 36
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 A 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.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 Citation - WoS: 3Citation - Scopus: 3Markdown Optimization in Apparel Retail Sector(Springer international Publishing Ag, 2020) Yildiz, Sevde Ceren; Hekimoglu, MustafaPrice discounts, known as markdowns, are important for fast fashion retailers to utilize inventory in a distribution channel using demand management. Estimating future demand for a given discount level requires the evaluation of historical sales data. In this evaluation recent observations might be more important than the older ones as majority of price discounts take place at the end of a selling season and that time period provides more accurate estimations. In this study, we consider a weighted least squares method for the parameter estimation of an empirical demand model used in a markdown optimization system. We suggest a heuristic procedure for the implementation of weighted least squares in a markdown optimization utilizing a generic weight function from the literature. We tested the suggested system using empirical data from a Turkish apparel retailer. Our results indicate that the weighted least squaresmethod is more proper than the ordinary least squares for the fast fashion sales data as it captures price sensitivity of demand at the end of a selling season more accurately.Master Thesis Madencilik Sektöründe Uçtan Uca Durum İzleme Sistemi(2024) Öz, Usame; Hekimoğlu, MustafaMadencilik sektöründe üretim verimliliğini artırmak ve bakım maliyetlerini azaltmak amacıyla, bu tez, uçtan uca bir durum izleme sisteminin kurulumu ve işletilmesini incelemektedir. Problem olarak, taş kırma makinelerinde meydana gelen beklenmedik arızaların operasyonel kesintilere ve yüksek bakım maliyetlerine yol açması ele alınmıştır. Bu çalışmada, durum izleme sisteminin tasarımı, sensörlerin kalibrasyonu ve veri toplama, işleme ve aktarım süreçleri detaylandırılmıştır. Titreşim, sıcaklık ve akım sensörleri kullanılarak, makinelerin kalan kullanım ömrünü tahmin etmek için optimize edilmiş makine öğrenimi algoritmaları entegre edilmiştir. Laboratuvar ve saha testleri, sistemin doğruluğunu ve güvenilirliğini değerlendirmek amacıyla gerçekleştirilmiştir. Testler sırasında, gerçek zamanlı veri toplama ve analiz yetenekleri gözlemlenmiş ve sistemin performansı ölçülmüştür. Sonuç olarak, önerilen sistem, bakım planlamasında daha doğru ve uygulanabilir öngörüler sunarak operasyonel verimliliği artırmayı başarmıştır. Elde edilen verilerin analizi, enerji tüketiminin optimize edilmesi ve ürün kalitesinin iyileştirilmesi gibi operasyonel iyileştirmelere olanak tanımıştır. Bu çalışma, madencilik sektöründe durum izleme sistemlerinin uygulanabilirliğini ve potansiyel faydalarını göstermekte ve endüstri profesyonelleri için pratik uygulama rehberleri sunmaktadır.Article Citation - WoS: 4Citation - Scopus: 4Modeling Repair Demand in Existence of a Nonstationary Installed Base(Elsevier, 2023) Hekimoglu, Mustafa; Karli, DenizLife 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.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 - WoS: 3Citation - Scopus: 4Admission control for a capacitated supply system with real-time replenishment information(Elsevier, 2023) Ma, Weina; Hekimoglu, Mustafa; Dekker, RommertControl towers can provide real-time information on logistic processes to support decision making. The question however, is how to make use of it and how much it may save. We consider this issue for a company supplying expensive spare parts and which has limited production capacity. Besides deciding on base stock levels, it can accept or reject customers. The real-time status information is captured by a k-Erlang distributed replenishment lead time. First we model the problem with patient customers as an infinite-horizon Markov decision process and minimize the total expected discounted cost. We prove that the optimal policy can be characterized using two thresholds: a base work storage level that determines when ordering takes place and an acceptance work storage level that determines when demand of customers should be accepted. In a numerical study, we show that using real-time status information on the replenishment item and adopting admission control can lead to significant cost savings. The cost savings are highest when the optimal admission threshold is a work storage level with a replenishment item halfway in process. This finding is different from the literature, where it is stated that the cost increase of ignoring real-time information is negligible under either the lost sales or the backordering case. Next we study the problem where customers are of limited patience. We find that the optimal admission policy is not always of threshold type. This is different from the literature which assumes an exponential production lead time.Conference Object Citation - WoS: 2Citation - Scopus: 3The Implementation of Smart Contract via Blockchain Technology in Supply Chain Management: A Case Study from The Automotive Industry in Turkey(IEEE, 2021) Yuksel, Hasan Basri; Bolat, Serdar; Bozkurt, Hayreddin; Yucekaya, Ahmet; Hekimoglu, MustafaBlockchain Technology, underlined as the most revolutionizing innovation after the internet, is still in the growth phase and waits for the practitioners to enlighten its productivity promises. In the current environment, volatile profits require a more digitalized work experience and competitive advantages to get ahead in such a highly competitive automotive industry and innovative applications that lead to more simplified operation management. Accordingly, this paper aims to present a case study via use cases in which Blockchain has been used and smart contract as the sought-out innovation and its application for the digitized spare parts disposal legal process. Blockchain Technology in the automotive sector is discussed by focusing on the supply management process of an automotive company's processes in Turkey. Blockchain technology is expected to develop and simplify spare parts-related transactions in the automotive industry, which deals with more than 500K stock keeping units per company. Paper presents the current, future, and ideal states of spare parts transactions with Blockchain adoption. The implemented application enables the development of an enterprise-level blockchain platform with hyper-ledger fabric as an open-source. The distributed ledger technology provides a smart contract system between actors of the existed supply chain process. The study aims to show the potential of Blockchain Technology in delivering a high degree of competitive advantage especially for automotive service providers with regards to its features related to providing security, transparency, traceability, cost reduction, more efficient data storage in dense supply based industries.Article Citation - WoS: 4Citation - Scopus: 5Dual Sourcing Models With Stock-Out Dependent Substitution(Elsevier, 2023) Hekimoglu, Mustafa; Scheller-Wolf, AlanCompanies use different criteria such as lead time, cost and quality to evaluate suppliers; often using multiple suppliers with the aim of reducing stockout risk. But in many industries there may be significant differences between the quality levels of different suppliers. Thus quality-sensitive companies may prefer an item from a primary supplier, but be forced to accept substitute products of lesser quality in case of a stock-out. Motivated by an example in the aviation industry, we introduce a Dual Sourcing problem With Stock-out dependent substitution (DSWS) which includes quality differences. Due to nonconvexity of the multi-period model, analytical characterization of the optimal policy appears intractable. To overcome this problem, we prove a relation between the optimal cost of DSWS and costs of three other problems -dual sourcing without substitution and single sourcing problems with and without backlogging. This leads us to propose the use of the dual index policy (and a variant) as heuristics for DSWS, and to develop an algorithm for parameter optimization of our heuristics. Extensive numerical experiments show that the dual index policy outperforms all other candidate solutions from the literature by at least 8%. Our experiments show that the utilization of the back-up supplier leads to substantial cost savings and service rate increase, especially in case of high differences between holding cost rates of different quality items.& COPY; 2023 Elsevier B.V. All rights reserved.
