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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
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ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
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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
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Scholarly Output
36
Articles
26
Views / Downloads
224/1970
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 |
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Scholarly Output Search Results
Now showing 1 - 10 of 36
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: 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/ )Master Thesis Demand Classification for Spare Parts Supply Chains in the Presence of Three Dimensional Printers(Kadir Has Üniversitesi, 2022) İşler, Zülal; Hekimoğlu, MustafaThree-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.Article Citation - WoS: 12Citation - Scopus: 16Markov-Modulated Analysis of a Spare Parts System With Random Lead Times and Disruption Risks(Elsevier Science Bv, 2018) Hekimoğlu, Mustafa; van der Laan, Ervin; Dekker, RommertSpare 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.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: 2Stock 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.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.
