Karabağ, Oktay
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Karabag, O.
Karabağ, O.
Karabag, Oktay
Karabağ, O.
Karabag, Oktay
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oktay.karabag@ieu.edu.tr
oktaykarabag@gmail.com
oktaykarabag@gmail.com
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05.09. Industrial Engineering
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| Journal | Count |
|---|---|
| International Journal of Production Research | 3 |
| Lecture Notes in Mechanical Engineering | 2 |
| International Journal of Production Economics | 1 |
| IISE Transactions | 1 |
| Omega (United Kingdom) | 1 |
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12 results
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Now showing 1 - 10 of 12
Article Citation - Scopus: 8Energy-Efficient Production Control of a Make-To System With Buffer- and Time-Based Policies(Taylor and Francis Ltd., 2023) Tan, B.; Karabağ, Oktay; Khayyati, S.Increasing energy efficiency in manufacturing has significant environmental and cost benefits. Turning on or off a machine dynamically while considering the production rate requirements can offer substantial energy savings. In this work, we examine the optimal policies to control production and turn on and off a machine that operates in working, idle, off, and warmup modes for the case where demand inter-arrival, production, and warmup times have phase-type distributions. The optimal control problem that minimises the expected costs associated with the energy usage in different energy modes and the inventory and backlog costs is solved using a linear program associated with the underlying Markov Decision Process. We also present a matrix-geometric method to evaluate the steady-state performance of the system under a given threshold control policy. We show that when the inter-arrival time distribution is not exponential, the optimal control policy depends on both the current phase of the inter-arrival time and inventory position. The phase-dependent policy implemented by estimating the current phase based on the time elapsed since the last arrival yields a buffer- and time-based policy to control the energy mode and production. We show that policies that only use the inventory position information can be effective if the control parameters are chosen appropriately. However, the control policies that use both the inventory and time information further improve the performance. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.Conference Object Maintenance Decision and Spare Part Selection for Multi-Component System(Springer Science and Business Media Deutschland GmbH, 2024) Kaya, B.; Karabağ, O.; Fadıloğlu, M.M.Due to the advancement of technology over time, higher technology machines are being used in the production and service sectors. Companies suffer great financial losses if these machines stop working due to a breakdown. To avoid these losses, maintenance has become increasingly important for companies over time. Condition based maintenance aims to intervene in a system as close to the point of failure as possible using information received from the system. Sensors are used to obtain information about the wear and tear of the machine. However, since sensors are costly, they are not installed on every machine component but rather on the system. While this reduces costs, it also means that we now obtain partial information from the system rather than from each component. In these systems, we need to make two types of decisions. The first decision is when to intervene in the system. The second decision is how many spare parts to carry with us once we decide to intervene. We simulated several different experiments for a periodic system composed of identical components and found optimal policies based on the two decisions we made. Our managerial insights indicate that as the number of components in the machine increases, the importance of selecting spare parts for the system also increases, leading to a tendency to maintain the system as late as possible before the system fails. Moreover, in situations where the penalty for maintenance is lower after a failure occurs, in optimal policy, we maintain later and carry more spare parts during our interventions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.Article A Deterministic Fluid Model for Production and Energy Mode Control of a Single Machine(Elsevier B.V., 2024) Tan, B.; Karabağ, O.Improving machines’ energy efficiency through dynamic energy mode control to meet demand requirements with minimal energy consumption is a promising approach. This study considers a machine operating in working, idle, off, and warmup energy modes with different energy consumption in each mode. A deterministic fluid model is developed to analyze an energy mode control policy that determines when to keep the machine working, off or idle, and switch to other modes based on the inventory/backlog level to minimize the total energy, inventory, and backlog costs. This approach facilitates the derivation of closed-form expressions for the optimal thresholds and the associated costs. This modeling approach allows us to prove that a policy that operates the machine between the working and off modes or the working and idle modes is always better than a hybrid policy that operates the machine in working, off, and idle modes simultaneously. We use the solution of the deterministic fluid model to propose an approximate policy for machines with stochastic production, warmup, and demand processes. We compare the results of the proposed approximation method with the optimal solution of a stochastic system where the production and warmup times are exponential and the demand inter-arrival times have Erlang distribution. The optimal solution for the stochastic system is determined by solving a Markovian Decision Process (MDP). Our numerical experiments show that the proposed approximation method predicts the optimal policy type for the stochastic case with a 89.3% accuracy, and the average error between the optimal cost and the cost obtained with the approximation method is 1.37% for 729 different cases tested. Furthermore, the computational efficiency of the proposed approximation is around 250 times better than the effort to determine the optimal policy using an MDP approach. We propose this approximation method where the control parameters are given in closed form as an easy-to-implement and effective policy to control energy modes to minimize the total energy, inventory, and backlog costs. Furthermore, we present the deterministic fluid modeling approach as a versatile approach to analyze energy mode control problems. © 2024 The AuthorsMaster Thesis Ölü Kilometre Minimizasyonuna Dayalı Otobüs Garajlarındaki İş Yükünün Dengelenmesi(2025) Türker, Yaprak; Karabağ, OktayToplu taşıma sistemlerinde bakım, personel ve yakıt giderleri temel maliyet bileşenlerini oluşturmaktadır. Yolcu taşımacılığından değil, operasyonel hareketlerden kaynaklanan ölü kilometrelerin azaltılması; yakıt harcamalarının düşürülmesi ve çevresel etkilerin azaltılmasında kritik bir rol oynamaktadır. Bu çalışma, yalnızca ölü kilometre maliyetlerini en aza indirmeyi değil, aynı zamanda otobüs garajlarında görev yapan bakım personeline düşen iş yükünü dengelemeyi amaçlamaktadır. Çalışmanın önemi, mevcut literatürde hem ölü kilometreleri hem de bakım personelinin iş yükü dağılımını eşzamanlı olarak ele alan herhangi bir çalışmanın bulunmamasından kaynaklanmaktadır. Çalışma iki ana aşamadan oluşmaktadır. İlk aşamada, her hat için planlanan otobüs sayısı, garaj giriş-çıkış frekansları ve otobüs tiplerine göre yakıt tüketim oranları dikkate alınarak ölü kilometre maliyetlerini minimize eden bir matematiksel model geliştirilmiştir. Bu model, her garaj için gerekli otobüs tipi ve sayısını belirleyerek ölü kilometre maliyetlerini azaltmayı hedeflemektedir. İkinci aşamada ise, farklı otobüs markalarına ait arıza ağırlıkları analiz edilmiş ve bakım personeline düşen iş yükünü dengelemek amacıyla, marka bazlı arıza özellikleri ile personel kapasitesi dikkate alınarak bir optimizasyon modeli oluşturulmuştur. Her iki modelden elde edilen sonuçlar, mevcut atama ve iş yükü dağılımıyla karşılaştırılmış; buna yönelik istatistiksel analizler gerçekleştirilmiştir. Bulgular, ölü kilometreye ilişkin maliyetlerde %19 azalma ve iş yükü dengesinde %53 iyileşme olduğunu ortaya koymaktadır. Bu sonuçlar, her iki amacın birlikte optimize edilmesinin operasyonel verimlilik ve insan kaynakları yönetimi açısından önemini açıkça vurgulamaktadır.Master Thesis Sales and Returns Forecasting for Inventory Control(İzmir Ekonomi Üniversitesi, 2013) Karabağ, Oktay; Eliiyi, Deniz Türsel; Fadıloğlu, Mehmet MuratGelişen çevre duyarlılığı, geleneksel üretim sistemlerini kullanan üreticileri yeni stratejiler benimsemeye zorlamaktadır. Piyasaya verilen ürünlerin kullanımlarından sonra toplanıp, işlenerek tekrar tüketiciye ulaştırılmasına, yeniden üretim (remanufacturing) ismi verilmektedir. Yeniden üretim sistemlerinde talep ve geri dönüş tahmini, satın alma kararları, üretim planlama ve envanter yönetimi vb. gibi yönetim konuları için vazgeçilemez öğelerdir. Bu çalışma, talep ve geri dönüş tahmini için yeni metotlar geliştirmeyi ve mevcut literatürü incelemeyi amaçlamaktadır. İlk olarak, talep tahmini için en bilindik ve uygulaması kolay olan HoltWinters metodu incelenmiştir. Ancak, Gregoryen ve Hicri takvim gibi iki asenkron takvimin ortak etkisi belirli bir pazarda kendini gösterdiğinde, bu metodun talep eğilimini yakalamakta yeterli olmadığı gerçeği fark edilmiştir. Bu nedenle, HoltWinters metodu iki asenkron takvim nedeniyle oluşan sezonluk etkiler dikkate alınarak geliştirilmiştir ve oluşturulan bu yeni yöntem Augmented HoltWinters metodu olarak adlandırılmıştır. İkinci olarak, geri dönüş tahmini için Kelle ve Silver tarafından geliştirilen metotlar incelenmiş ve farklı bir bakış açısıyla yeniden sunulmuşlardır. Kelle ve Silvera ait bu yöntemler durağan talep şartıyla sınırlandırıldıklarından, talebin durağan olmadığı durumlarda etkili olmayacaktır. Bu sorunu gidermek için, ilgili yöntemler durağan olmayan talebe izin veren daha gerçekçi bir bakış açısı ile revize edilmiştir. Son olarak, talep ve geri dönüşler için geliştirilen yeni tahmin yöntemleri önceki halleri ile gerçek zaman serisi kullanılarak karşılaştırılmıştır. Elde edilen sonuçlar, talep ve geri dönüş tahmini için önerilen yeni metotlar kullanıldığında önemli iyileştirmeler sağlanabileceğini göstermiştir.Article Citation - WoS: 4Citation - Scopus: 4Purchasing and Remanufacturing Decisions With Different Quality Returned Material and Finished Goods(Taylor & Francis Ltd, 2025) Karabag, Oktay; Karaesmen, Fikri; Tan, BarisTo transition from a linear to a circular value chain, an effective refurbishment policy is crucial to preserve material value and functionality at the end of a product's life-cycle. This study examines a refurbisher that processes first- and second-quality returned materials to produce first- and second-quality products in a make-to-stock system. The refurbisher makes purchasing decisions for the returned materials and determines whether to refurbish or remain idle, and if refurbishing, how to convert them into finished goods of varying quality. There are five refurbishment decisions (converting first-quality to first- or second-quality, second-quality to first- or second-quality, or no production) and two purchasing decisions for the materials. With production and arrival times modelled as exponential random variables, the optimal control problem is formulated as a Markovian Decision Process, using a long-run average profit criterion to identify optimal decisions. A linear programming approach is employed for numerical optimisation. Results show that the most profitable option based solely on sales prices, purchasing, and conversion costs may not be optimal. Instead, the optimal policy is influenced by per-unit profit differences, returned material availability, demand rates, and production times across various refurbishment scenarios.Article Citation - WoS: 11Citation - Scopus: 11An Efficient Procedure for Optimal Maintenance Intervention in Partially Observable Multi-Component Systems(Elsevier Ltd, 2024) Karabağ, O.; Bulut, Ö.; Toy, A.Ö.; Fadıloğlu, M.M.With rapid advances in technology, many systems are becoming more complex, including ever-increasing numbers of components that are prone to failure. In most cases, it may not be feasible from a technical or economic standpoint to dedicate a sensor for each individual component to gauge its wear and tear. To make sure that these systems that may require large capitals are economically maintained, one should provide maintenance in a way that responds to captured sensor observations. This gives rise to condition-based maintenance in partially observable multi-component systems. In this study, we propose a novel methodology to manage maintenance interventions as well as spare part quantity decisions for such systems. Our methodology is based on reducing the state space of the multi-component system and optimizing the resulting reduced-state Markov decision process via a linear programming approach. This methodology is highly scalable and capable of solving large problems that cannot be approached with the previously existing solution procedures. © 2023 The Author(s)Article Kısmi Gözlemlenebilir Çok Bileşenli Sistemler için Bakım Politikalarının Pekiştirmeli Derin Öğrenme Yöntemleri ile Belirlenmesi(Pamukkale Univ, 2025) Karabağ, OktayBu çalışmada, kısmi gözlemlenebilir çok bileşenli sistemler için bakım/onarım kararları incelenmiştir. Bu tip sistemler genellikle servis sağlayıcının uzakta olduğu koşullarda işletilmekte ve bileşenlerin aşınma seviyeleri genellikle sensörler yardımı ile tam olarak izlenememektedir. Rüzgâr türbinleri, bu tarz sistemlere birebir uyan bir örnek oluşturmaktadır. İlgili sistemlerde, servis sağlayıcı ne zaman bakım/onarım yapacağına, bakım kararı ile birlikte hangi parçaları bakım noktasına sevk edeceğine ve bakım noktasındaki incelemesinin ardından hangi sistem bileşenlerinin değiştirilmesi gerektiğine karar vermektedir. Çalışmamızda, bahsi geçen bu komplike karar problemi kısmi gözlemlenebilir Markov karar süreci olarak modellenmiş ve ilgili nümerik çözümler aktör kritik pekiştirmeli öğrenme yöntemi kullanılarak elde edilmiştir. Yaptığımız nümerik çalışmalar, pekiştirmeli öğrenme algoritması ile elde edilen çözümlerin pratikte ve literatürde yaygın olarak kullanılan sezgisel bakım/onarım politikalarına kıyasla daha iyi sonuçlar verdiğini göstermiştir. Bazı durumlarda, bu çözümlerin ortalamada %10-%15 düzeyinde bir iyileştirme sağladığı gözlemlenmiştir. Ayrıca, düzeltici bakım maliyeti, acil sipariş maliyeti ve fazla yedek parçayı geri döndürme maliyeti arttıkça, pekiştirmeli öğrenme algoritması ile elde edilen çözümlerin diğer sezgisel politikalara kıyasla daha fazla avantaj sağladığı da belirlenmiştir.Article Optimal Purchasing and Production Control for a Circular Production System(Taylor & Francis Inc, 2026) Song, Yun-Lei; Frigerio, Nicla; Karabag, Oktay; Wang, Jun-Qiang; Tan, BarisThe paper investigates the optimal purchasing and production control policy within a circular production system that processes two returned material types through a shared manufacturing center. Materials are purchased or rejected upon arrival, with differences in procurement cost. At each decision epoch, the system must decide whether to purchase incoming materials and which type to prioritize for production. The problem is formulated as a Markov decision process, and a linear programming approach is developed to compute the optimal purchasing and production policy for the system under both non-preemption and preemption rules. Theoretically, the structure of the optimal policy is analytically characterized under the preemption rule in the case of equal production rates. Numerical experiments reveal that this policy remains optimal or near-optimal for the system under non-preemption and preemption rules with varying production rates. Additionally, comparisons of the optimal policies between non-preemption and preemption rules demonstrate that the system under the preemption rule yields higher average reward than that under the non-preemption rule, to varying degrees depending on system parameters, such as production rate, holding cost, and arrival rate.Conference Object Citation - Scopus: 1Inventory Management Optimization for Intermittent Demand(Springer Science and Business Media Deutschland GmbH, 2024) Kaya, B.; Karabağ, O.; Çekiç, F.R.; Torun, B.C.; Başay, A.Ö.; Işıklı, Z.E.; Çakır, Ç.This report discusses inventory management and demand forecasting issues faced by a well-known electrical equipment company. The company requires a precise inventory management system with a wide range of products to handle its high production volume. The company has trouble forecasting intermittent demand patterns due to a lack of appropriate analytical methodologies. To overcome these challenges, this study developed an inventory management system that integrates Newsvendor and Order Up Policy, whose analytical methods are optimized with the inventory management policy. A comprehensive review of the existing literature on inventory management is undertaken to gather valuable information and best practices. This study has been developed based on the research conducted by Syntetos (2009). A mathematical model has been included to maximize order levels, considering lead time and costs. In the model, SBA and Croston methods are used for intermittent demand forecasting. This model includes various parameters and assumptions that allow calculating expected total costs and determining the optimum order level that efficiently meets customer demand while minimizing expenses. The methods employed optimize inventory management, minimize inventory cost, and enhance customer satisfaction. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
