Kabak, Kamil Erkan
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erkan.kabak@ieu.edu.tr
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05.09. Industrial Engineering
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22
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6
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109
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50
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57
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2.50
Scopus Citations per Publication
2.85
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8
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| Journal | Count |
|---|---|
| Proceedings - Winter Simulation Conference | 6 |
| Computers in Biology and Medicine | 1 |
| Journal of Manufacturıng Systems | 1 |
| 25Th Internatıonal Conference on Productıon Research Manufacturıng Innovatıon: Cyber Physıcal Manufacturıng | 1 |
| Lecture Notes in Mechanical Engineering | 1 |
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Now showing 1 - 10 of 20
Article Citation - WoS: 6Citation - Scopus: 6A Systematic Literature Review Into Simulation for Building Operations Management Theory: Reaching Beyond Positivism?(Taylor & Francıs Ltd, 2024-03) Kabak, Kamil Erkan; Hinckeldeyn, Johannes; Dekkers, RobThis paper examines the extent to which simulation modelling has been employed beyond positivism, particularly under post-positivist and design-science-oriented paradigms, for theory building in operations management. Our study demonstrates that existing literature reviews have neglected the aspect of theory building and the generation of technological rules from a design-science perspective. A systematic literature review appraises 53 studies, published between 1976 and 2019, revealed that classical positivist approaches predominate (40 of 53 studies), followed by directive positivist (6), classical post-positivist (5), and participatory post-positivist (2) paradigms. The review highlights the potential of simulation modelling for theory building in operations management from both positivist and post-positivist perspectives. However, the dominance of positivist approaches suggests a lack of engagement with practical implementation and comparative studies. The findings advocate for greater external validation and practitioner involvement, and integrated and extended checklists for simulation studies are proposed to guide future research in this area.Editorial Shared Manufacturing and the Sharing Economy Ideal: Strategic Limits in a Fragmenting World(Research and Development Academy, 2025-06-11) Yeralan, S.; Kabak, K.E.This study offers a strategic critique of shared manufacturing (SharedMfg), a concept rooted in the broader sharing economy (SE) and promoted as a mechanism for optimizing industrial capacity through peer-to-peer coordination. While such frameworks emphasize digital platforms, scheduling efficiency, and resource pooling, they frequently neglect the deeper constraints that govern the real-world feasibility of manufacturing. In particular, SharedMfg models are often constructed atop idealized abstractions, treating manufacturing units as modular, cyber-physical assets within an Industry 4.0 ecosystem, while overlooking the material, energetic, and geopolitical foundations on which all manufacturing ultimately depends. Extending beyond critique, we explore the conceptual underpinnings of SharedMfg within its systemic context, a prelude to the layered pyramid model advanced in this study. This paper argues that manufacturing does not evolve autonomously, but rather reflects the socio-political order in which it is embedded. To address this oversight, we propose a layered conceptual framework – a manufacturing transformation pyramid – that begins not with coordination, but with the substrate: matter, energy, and institutional structure. We contend that genuine transformation in manufacturing systems must be grounded in these foundational realities, rather than in digital optimization alone. Absent this grounding, SharedMfg/SE risks becoming a transient theoretical exercise, bounded by the specific conditions of its historical moment and detached from the structural realities that shape industrial capacity. © The Author 2025.Master Thesis Telekomünikasyon Sektörü Çalışanlarının İş Sağlığı ve Güvenliği Açısından Risklerinin Belirlenmesi(İzmir Ekonomi Üniversitesi, 2019) Budak, Devrim; Kabak, Kamil ErkanKüresel Mobil İletişim Sistemi (Global System for Mobile Communication-GSM) teknoloji ve bilgi çağı olarak adlandırılan günümüzde, insan yaşamının vazgeçilmez unsuru olarak ilk kez Finlandiya'da devreye girmiştir. Hızla gelişmekte olan GSM teknolojisi yaşamın her alanında kullanım imkânı bulan etkili yüksek teknolojiye sahip bir sistemdir. Bu sistem yaşanan gelişmelerle birlikte dünyanın en büyük sektörlerinden biri olan telekomünikasyon sektörünü doğurmuştur. Bu teknoloji arka planında büyük bir alt yapı barındırmaktadır. Telekomünikasyon sektörü alt yapı işlerinde çalışanlar; inşaat işleri, yüksekte çalışma, gece çalışma, kazı işleri, elektromanyetik alan maruziyeti ve ağır çalışma koşulları kaynaklı risklere, mobing gibi psikososyal risklere maruz kalmaktadır. Telekomünikasyon sektöründe meydana gelen iş kazalarına yönelik risklerin değerlendirilmesi amacıyla çok çalışma yapılmıştır. Telekomünikasyon sektörü baz istasyonu kurulum ve bakım onarımlarında 6331 sayılı İş Sağlığı ve Güvenliği Kanununun gereklilikleri, alınması gereken tedbirler, çevresel faktörler, çalışan eğitimleri, sağlık kontrolleri, risk değerlendirmeleri, acil eylem planları gibi prosedürler uygulanarak çalışanın ortaya çıkabilecek her türlü riske karşı korunması gerekmektedir. Bu çalışmanın amacı telekomünikasyon sektöründe çalışanlar için iş sağlığı ve güvenliği açısından riskleri araştırmak, literatür incelemesi yapmak, risklerin önlenmesi için hipotezler önermek, tartışılan hipotezlere göre literatür araştırmasına dayanan riskleri önleyici çözüm önerilerinde bulunmak olarak belirlenmiştir. Konu bütünü ile ele alınıp telekomünikasyon sektöründe çalışanların iş sağlığı ve güvenliği açısından taşıdıkları riskler ve bu risklerin ortadan kaldırılmasına yönelik önerilerde bulunarak literatüre katkı sunulmaya çalışılmıştır.Article Citation - WoS: 25Citation - Scopus: 27Optimizing Capacity Allocation in Semiconductor Manufacturing Photolithography Area - Case Study: Robert Bosch(Elsevier Sci Ltd, 2020-01) Ghasemi, Amir; Azzouz, Radhia; Laipple, Georg; Kabak, Kamil Erkan; Heavey, CathalIn this paper, we advance the state of the art for capacity allocation and scheduling models in a semiconductor manufacturing front-end fab (SMFF). In SMFF, a photolithography process is typically considered as a bottleneck resource. Since SMFF operational planning is highly complex (re-entrant flows, high number of jobs, etc.), there is only limited research on assignment and scheduling models and their effectiveness in a photolitography toolset. We address this gap by: (1) proposing a new mixed integer linear programming (MILP) model for capacity allocation problem in a photolithography area (CAPPA) with maximum machine loads minimized, subject to machine process capability, machine dedication and maximum reticles sharing constraints, (2) solving the model using CPLEX and proofing its complexity, and (3) presenting an improved genetic algorithm (GA) named improved reference group GA (IRGGA) biased to solve CAPPA efficiently by improving the generation of the initial population. We further provide different experiments using real data sets extracted from a Bosch fab in Germany to analyze both proposed algorithm efficiency and solution sensitivity against changes in different conditional parameters.Conference Object Citation - WoS: 3Citation - Scopus: 5Demonstration of the Feasibility of Real Time Application of Machine Learning To Production Scheduling(Institute of Electrical and Electronics Engineers Inc., 2022-12-11) Ghasemi A.; Kabak K.E.; Heavey C.; Ghasemi, Amir; Heavey, Cathal; Kabak, Kamil ErkanIndustry 4.0 has placed an emphasis on real-time decision making in the execution of systems, such as semiconductor manufacturing. This article will evaluate a scheduling methodology called Evolutionary Learning Based Simulation Optimization (ELBSO) using data generated by a Manufacturing Execution System (MES) for scheduling a Stochastic Job Shop Scheduling Problem (SJSSP). ELBSO is embedded within Ordinal Optimization (OO), where in the first phase it uses a meta model, which previously was trained by a Discrete Event Simulation model of a SJSSP. The meta model used within ELBSO uses Genetic Programming (GP)-based Machine Learning (ML). Therefore, instead of using the DES model to train and test the meta model, this article uses historical data from a front-end fab to train and test. The results were statistically evaluated for the quality of the fit generated by the meta-model. © 2022 IEEE.Article Analysis of Inoculation Strategies During Covid-19 Pandemic With an Agent-Based Simulation Approach(Elsevier Ltd, 2025-03) Kulaç, O.; Toy, A.Ö.; Kabak, K.E.Background: The severity of recent Coronavirus (COVID-19) pandemics has revealed the importance of development of inoculation strategies in case of limited vaccine availability. Authorities have implemented inoculation strategies based on perceived risk factors such as age and existence of other chronic health conditions for survivability from the disease. However, various other factors can be considered for identifying the preferred inoculation strategies depending on the vaccine availability and disease spread levels. This study explores the effectiveness of inoculating different groups of population in case of various vaccine availabilities and disease spread levels by means of some performance metrics namely: Attack Rate (AR), Death Rate (DR) and Hospitalization Rate (HR). Method: In this study we have implemented a highly detailed Agent-Based Simulation (ABS) model that extends classical SEIR Model by including five more additional states: Asymptomatic (A), Quarantine (Q), Hospitalized (H), Dead (D) and Immune (M) which can be used as a decision support tool to prioritize the groups of the population inoculated. The approach employs the modelling of daily mobility of individuals, their interactions and transmission of virus among individuals. The population is heterogeneously clustered according to age, family size, work status, transportation and leisure preferences with 17 different groups in order to find the most appropriate one to inoculate. Three different Disease Spread Levels (DSL) (low, mid, high) are experimented with four different Vaccine Available Percentages (VAP) (25%, 50%, 75% and 85%) with a total of 84 scenarios. Results: As the benchmark, under the No Vaccine case Attack Rate, Hospitalization Rate, and Death Rate goes as high as 99.53%, 16.96%, and 1.38%, respectively. Corresponding highest performance metrics (rates) are 72.33%, 15.95%, and 1.35% for VAP = 25%; 50.25%, 9.55%, and 0.94% for VAP = 50%; 24.53%, 2.62%, and 0.25% for VAP = 75%; and 11.51%, 0.002%, and 0.08% for VAP = 85%. The results of our study shows that the common practice of inoculation based on the age of individual does not yield the best outcome in terms of performance metrics across all DSL and VAP values. The groups containing workers and students that represent highly interactive individuals, i.e. Group (9, 10), Group (9, 11, 10‾) and Group (9, 10, 11, 12‾) emerge as a commonly recommended choice for inoculation in the majority of cases. As expected, we observe that the higher is the VAP levels the more is the number of alternative inoculation groups. Conclusions: Findings of this study present that: (i) inoculation considerably decreases the number of infected individuals, the number of deaths and the number of hospitalized individuals due to the disease, (ii) the best inoculation group/groups with respect to performance metrics varies depending on the vaccine availability percentages and disease spread levels, (iii) simultaneous implementation of both inoculation and precautions like lock-down, social distances and quarantines, yields a stronger impact on disease spread and its consequences. © 2024 Elsevier LtdConference Object Citation - Scopus: 2Deep Learning Enabling Digital Twin Applications in Production Scheduling: Case of Flexible Job Shop Manufacturing Environment(Institute of Electrical and Electronics Engineers Inc., 2023-12-10) Ghasemi, A.; Yeganeh, Y.T.; Matta, A.; Kabak, Kamil Erkan; Heavey, C.Digital twin-based Production Scheduling (DTPS) is a process in which a digital model replicates a manufacturing system, known as a "Digital Twin (DT)". DT is essentially a virtual representation of physical equipment and processes that are connected to the physical environment using an online data-sharing infrastructure within the Manufacturing Execution System (MES). In the case of reactive scheduling, DT is used to detect fluctuations in the scheduling plan and execute rescheduling plans. In proactive scheduling, it is used to simulate different production scenarios and optimize future states of production operations. Replicating detailed simulation models in most PS cases is highly computationally intensive, which negates against the main goal of DT (online decision making). Thus, this research aims to examine the possibility of using data-driven models within the DT of a Flexible Job Shop (FJS) production environment aiming to provide online estimations of PS metrics enabling DT-based reactive/proactive scheduling. © 2023 IEEE.Conference Object Citation - WoS: 8Citation - Scopus: 7Implementing a New Genetic Algorithm To Solve the Capacity Allocation Problem in the Photolithography Area(Institute of Electrical and Electronics Engineers Inc., 2018-12) Ghasemi A.; Heavey C.; Kabak K.E.Photolithography plays a key role in semiconductor manufacturing systems. In this paper, we address the capacity allocation problem in the photolithography area (CAPPA) subject to machine dedication and tool capability constraints. After proposing the mathematical model of the considered problem, we present a new genetic algorithm named RGA which was derived from a psychological concept called Reference Group in society. Finally, to evaluate the efficiency of the algorithm, we solve a real case study problem from a semiconductor manufacturing company in Ireland and compare the results with one of the genetic algorithms proposed in the literature. Results show the effectiveness and efficiency of RGA to solve CAPPA in a reasonable time. © 2018 IEEEMaster Thesis Unrelated Parallel Machine Scheduling With Sequence Dependent Setup Times by Ant Colony Optimization in Textile Industry(İzmir Ekonomi Üniversitesi, 2018) Önem, Ebru; Kabak, Kamil ErkanBu çalışma bir tekstil firmasının örgü kumaş aşamasındaki toplam ağırlıklandırılmış gecikmeyi en aza indirgeyen gerçek bir üretim problemini içermektedir. Örgü kumaş üretiminde belirli sayıda ilişkisiz paralel makine vardır. Ayrıca, örgü kumaş üretim sisteminde kurulum zamanları sıralamaya bağlıdır. Buna ek olarak, sistemde farklı ve değişen çeşitte müşteri siparişlerinin üretimine başlayabileceği zamanlar da tanımlanmıştır. Problemi çözmek için, bir karışık tamsayılı matematiksel model önerilmiştir ve problemin zor bir problem olduğu deneysel sonuçlarla gösterilmiştir. Sonra, deneysel tasarımla test edilen değişen problem durumlarıyla çözülerek test edilen karınca kolonisi eniyilemesi yaklaşımı tabanlı yeni bir sezgisel algoritma geliştirilmiştir. Sonuçlar, algoritmanın yeterince hızlı çözümler üreten pratik bir uygulama olduğunu göstermektedir.Conference Object Citation - WoS: 3Citation - Scopus: 4Generating Operating Curves in Complex Systems Using Machine Learning(Institute of Electrical and Electronics Engineers Inc., 2014-12) Can B.; Heavey C.; Kabak K.E.This paper proposes using data analytic tools to generate operating curves in complex systems. Operating curves are productivity tools that benchmark factory performance based on key metrics, cycle time and throughput. We apply a machine learning approach on the flow time data gathered from a manufacturing system to derive predictive functions for these metrics. To perform this, we investigate incorporation of detailed shop-floor data typically available from manufacturing execution systems. These functions are in explicit mathematical form and have the ability to predict the operating points and operating curves. Simulation of a real system from semiconductor manufacturing is used to demonstrate the proposed approach. © 2014 IEEE.
