Scopus İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395

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Generating Lost Urban Fabric: Exploration of Generative Adversarial Networks as a Design Tool in Post-Disaster Urban Recovery
(Education and Research in Computer Aided Architectural Design in Europe, 2025) Takış, F.N.; Akyüz, S.
This study investigates the use of GANs, particularly the Pix2PixHD, for reconstructing urban fabric and preserving urban memory in post-disaster contexts, focusing on Hatay, Türkiye, after the 2023 earthquakes. Models were trained on pre-disaster urban maps and tested on incomplete post-earthquake data to regenerate damaged urban areas. Evaluation metrics, including FID scores, SSIM values, and visual inspections, demonstrated the model's ability to produce contextually accurate designs. The trained model effectively maintained road networks, building geometries, and spatial coherence. In addition to spatial consistency, the model produced outputs with sharp edges and high visual clarity. These results highlight the significant potential of GANs as generative design tools, offering valuable support to urban planners and architects in balancing urgent reconstruction needs with the long-term preservation of urban identity and memory in disaster-affected areas. © 2025, Education and research in Computer Aided Architectural Design in Europe. All rights reserved.
Supervised Learning-Driven Dead Band Control of Occupant Thermostats for Energy-Efficient Residential HVAC
(Elsevier Ltd, 2026) Savasci, A.; Ceylan, O.; Paudyal, S.
Heating, ventilation, and air conditioning (HVAC) systems play a crucial role in demand-side management (DSM) by shaping residential electricity consumption and enabling flexible, grid-responsive operation. Thermostats in HVAC systems regulate indoor temperature as part of a closed-loop control framework, typically incorporating a fixed temperature dead band–a range around the setpoint where no action is taken–to reduce energy use and prevent frequent cycling of the HVAC system. Although essential for efficiency and equipment longevity, fixed dead bands limit adaptability, as dynamically adjusting them under varying environmental conditions remains challenging for occupants. To address this limitation, we propose a machine learning (ML)-based dead band tuning framework that optimally adjusts thermostat settings in real time. The method integrates conventional optimization with data-driven modeling: a mixed-integer linear programming (MILP) model is first used to generate optimal dead band values under measured outdoor temperature records (diverse seasonal weather scenarios) which are then employed to train the ML-based predictor to learn a real-time discrete dead band decision policy that approximates the MILP-optimal hysteresis-aware decisions. Among the evaluated models, Random Forest demonstrates superior predictive performance, achieving a mean squared error (MSE) of 0.0399 and a coefficient of determination (R2) of 95.75 %. © 2025 Elsevier Ltd.
Development of Resistant Starch Type-5 and Its Utilization in Cookie-Preparation
(North University of Baia Mare, 2025) Oskaybaş-Emlek, B.; Özbey, A.; Kahraman, K.
The objective of this study was the production of resistant starch type-5 (RS-5), its characterization, and utilization in cookie making. In first part of the study, the effects of starch-fatty acid complex formation (RS-5) between tapioca starch and lauric acid on the structure, digestibility, thermal and morphological properties of tapioca starch were investigated. X-ray diffraction revealed that the RS-5 had a V-type crystalline pattern. FT-IR analysis showed that a distinctive peak at 2846 cm-1 was only observed in RS-5. The resistant starch (RS) content of native starch increased from 22.76% to 28.02% with RS-5 formation. In the second part of the study, the RS-5 was added as a replacement for wheat flour with 10%, 20%, and 30% compared to control sample made with 100% wheat flour in cookie-making. The effects of RS-5 replacement of cookie samples on some physicochemical, estimated glycemic index (eGI) value, physical, and hardness properties were determined. Compared to control cookie, the cookie samples included RS-5 had lower hardness value, higher spread ratio. The eGI value of cookie samples was slightly decreased with the replacement with RS-5. The results demonstrated that the RS-5 has good potential for developing softer cookie with no adverse impact on eGI value. © 2025, North University of Baia Mare. All rights reserved.
Citation - Scopus: 4
University Librarians’ Perceptions Of Artificial Intelligence, Its Application Areas İn Libraries, And The Future
(University and Research Librarians Association (UNAK), 2024) Cuhadar, S.; Mert, S.; Gezer, Ç.; Helvacioğlu, E.; Arus, O.; Aslan, Ö.; Atli, S.
Today, libraries are among the institutions affected by changing technology and innovations. The popularization of artificial intelligence (AI) technologies has also begun to transform library services. In this research, a survey was conducted to determine the adjustments that university libraries in Turkey have made and plan to make during the development process of AI technologies and applications, and to identify the services they have developed specific to the relevant period. The survey was carried out with the participation of 111 university library managers from 208 university libraries in Turkey. Through the analysis of the data, the status, knowledge, and awareness levels of university libraries regarding AI technologies and applications were determined, and measures and recommendations were presented to improve deficiencies and weaknesses. This research is the first and most comprehensive study conducted in Turkey by obtaining opinions and suggestions from university library managers on artificial intelligence. The research findings revealed that university libraries use AI applications such as ChatGPT, Gemini, and Grammarly to a certain extent; however, they have needs in developing institutional policies, enhancing personnel competencies, and planning related to AI. © 2024 University and Research Librarians Association (UNAK). All rights reserved.
Spatial Dimension of The ‘Local’ Phenomenon in Kayseri
(Gazi Üniversitesi, 2025) Özmen, N.M.; Asiliskender, B.
Kayseri is in the centre of Anatolia, at the intersection of trade and military routes, and possesses a rich cultural heritage. Throughout its history, the city has hosted various civilizations, developing around a central castle and continuing to expand, particularly after the 19th century. Kayseri has long served as a meeting point for diverse cultures. Within this diversity, families known as locals, whose origins date back to the oldest neighbourhoods within the city walls, have held significant mercantile power. These local families regard themselves as the actual owners of Kayseri and have influenced the city’s developmental trajectory. Over time, they have moved outward from the centre to newly developed neighbourhoods, first to the north and then to the east. This study examines the urban development of Kayseri in the 20th century and the spatial mobility of these local families. It employs qualitative methods such as ethnographic observation, oral history interviews, and GIS-based thematic mapping to analyse these movements in a multi-layered way. The study also aims to understand Kayseri’s socio-cultural dynamics and historical texture by investigating the role of local families in the city’s physical and functional transformations. In this context, it addresses the physical and functional changes in neighbourhoods vacated by these relocations. © 2025, Gazi Universitesi. All rights reserved.
Performance Boost in QLEDs Using Octanethiol-Capped Core/Shell Quantum Dots
(IOP Publishing Ltd, 2026) Yazici, Ahmet F.; Yuruc, Adnan M.; Kelestemur, Yusuf; Serin, Ramis Berkay; Kacar, Rifat; Ulku, Alper; Mutlugun, Evren
Quantum dots attract significant attention as one of the most promising colloidal nanocrystals with unique optical properties and potential applications for the next generation of display technology. In this paper, we evaluate the performance of CdZnSeS-based alloyed-shell quantum dots (QDs) for electroluminescence devices upon additional shell growth and ligand exchange. This includes core/shell (C/S) and core/shell/shell (C/S/S) QDs, whose latter includes an additional ZnS shell and octanethiol (OT) ligands. We present detailed characterizations of QDs using transmission electron microscopy, XRD, and various spectroscopic techniques and demonstrate their QD light emitting (QLEDs). We find the photoluminescence quantum yield of C/S/S QDs increased from 68.8% to 88.7% compared to C/S QDs whereas the emission linewidth narrows from 22.2 nm to 20.8 nm. QLEDs fabricated with C/S/S QDs exhibit a higher peak external quantum efficiency (EQE) of 4.1% and maximum luminance of 85 000 cd m-2, compared to 2.3% EQE and 67 000 cd m-2 for C/S QLEDs. In this respect, the OT-assisted shell growth significantly improves the optical property of QDs and performance of QLEDs, likely attributed to the enhanced charge balance and increased radiative recombination rate.
A Small Indole Derivative Isolated From Caper (Capparis Ovata) as an Inducer of P53-Mediated Apoptosis in Prostate Cancer: Comprehensive In Vitro and In Silico Studies
(Wiley, 2025) Acar, Ozden Ozgun; Gazioglu, Isil; Oruc, Hatice; Kale, Elif; Senol, Halil; Topcu, Gulacti; Sen, Alaattin
Natural products with stunning chemical diversity have been extensively researched for their anticancer potential for more than fifty years. This study aimed to determine the effect of indole derivative 1H-indole-2-hydroxy-3-carboxylic acid (IHCA), isolated as a novel alkaloid from Capparis ovata, on selected tumor suppressor, apoptotic, and cell cycle regulatory genes, which are known to be important in cancer pathophysiology, on Caco-2 and LNCaP cells in comparison with Taxol. The molecular mechanism of IHCA's anticancer activity is essentially undefined. Different concentrations of IHCA increased the expression levels of apoptosis-related genes, including BCL-2 and TNF-alpha. In addition, the tumor suppressor genes PTEN, P53, and RB were increased in LNCaP and Caco-2 cells. KRAS, an oncogenic gene, was significantly downregulated by IHCA in LNCaP cells. Western blot results showed that the protein expression levels of P53 and PTEN in LNCaP cells were increased when treated with IHCA, whereas CDK4 and TNF-alpha were decreased. Finally, IHCA and doxorubicin significantly increased P53-driven luciferase activity compared to the control. The results strongly suggest that the novel natural compound IHCA has an anticancer effect involving the regulation of the P53 gene and its networks in vitro. The molecular docking and MD simulation analyses reveal that IHCA exhibits superior binding potential to the MDM2 protein compared to Nutlin-3a. MD simulations further confirm that IHCA maintains a more stable and consistent interaction with MDM2, as indicated by lower RMSD values and reduced ligand fluctuation. These results highlight IHCA's potential as a more effective MDM2 inhibitor, suggesting its promise as a lead compound for anticancer drug development.Clinical Trial Registration: Not applicable.
Deep-Learning Detection of Open-Apex Teeth on Panoramic Radiographs Using YOLO Models
(Springer, 2025) Edik, Merve; Celebi, Fatma; Cukurluoglu, Aykagan
ObjectivesThe use of deep learning in detecting teeth with open apices can prevent the need for additional radiographs for patients. The presented study aims to detect open-apex teeth using You Only Look Once (YOLO)-based deep learning models and compare these models.MethodsA total of 966 panoramic radiographs were included in the study. Open-apex teeth in panoramic radiographs were labeled. During the labeling process, they were divided into 6 classes in the maxilla and mandible, namely incisors, premolars, and molars. AI models YOLOv3, YOLOv4, and YOLOv5 were used. To evaluate the performance of the three detection models, both overall and separately for each class in the test dataset, precision, recall, average precision (mAP), and F1 score were calculated.ResultsYOLOv4 achieved the highest overall performance with a mean average precision (mAP) of 87.84% at IoU (Intersection over Union) 0.5 (mAP@0.5), followed by YOLOv5 with 85.6%, and YOLOv3 with 84.46%. Regarding recall, YOLOv4 also led with 90%, while both YOLOv3 and YOLOv5 reached 89%. Moreover, the F1 score was the highest for YOLOv4 (0.87), followed by YOLOv3 (0.86) and YOLOv5 (0.85).ConclusionsIn this study, YOLOv3, YOLOv4, and YOLOv5 were evaluated for the detection of open-apex teeth, and their mAP, recall, and F1 scores exceeded 84%. Deep learning-based systems can provide faster and more accurate results in the detection of open-apex teeth. This may help reduce the need for additional radiographs from patients and aid dentists by saving time.
Advances in Natural Building and Construction Materials
(MDPI, 2025) Strzalkowski, Pawel; Sousa, Luis; Koken, Ekin
G-C3N4@Fe3O4 Nanomaterial Synthesis for Magnetic Solid-Phase Extraction and Photocatalytic Removal of Basic Blue 3
(Springer Heidelberg, 2025) Kizil, Nebiye; Kayaci, Nilgun; Erbilgin, Duygu Erkmen; Yola, Mehmet Lutfi; Yilmaz, Erkan; Soylak, Mustafa
The present research synthesized a g-C3N4@Fe3O4 hybrid material for efficient magnetic solid-phase extraction (MSPE) and photocatalytic degradation of Basic Blue 3 (BB3) dye from wastewater. Characterization of the synthesized g-C3N4@Fe3O4 was conducted through Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), scanning electron microscopy (SEM), and energy-dispersive X-ray spectroscopy (EDX). The optimization of the method was carried out by examining parameters such as pH, g-C3N4@Fe3O4 amount, sample volume, and adsorption/desorption duration. In addition, analytical performance criteria such as limit of detection (LOD), limit of quantification (LOQ), and relative standard deviation (RSD) of the MSPE method were calculated as 1.29 mu g L-1, 4.28 mu g L-1, and 1.9%, respectively. The method was applied to real samples, including wastewater and textiles, and validated through addition/recovery studies for the magnetic solid-phase extraction procedure. The recoveries were gained between 91 and 100%. The reusability synthesized g-C3N4@Fe3O4 was also evaluated. The recoveries for Basic Blue 3 dye decreased to 81% after the fourth experiment. Furthermore, the photocatalytic performance of the g-C3N4@Fe3O4 hybrid material was evaluated due to its good surface area and strong interaction with Basic Blue 3 dye. The photocatalytic activity of g-C3N4@Fe3O4 hybrid material was calculated as 96.8% for 100 mg in 300 min.
G-S a Prior Biological Knowledge-Based Pattern Detection and Enrichment Framework for Multi-Omics Data Integration
(MDPI, 2025) Unlu Yazici, Miray; Bakir-Gungor, Burcu; Yousef, Malik
The rapid advancements in high-throughput technologies have led to a dramatic increase in diverse -omics data types, enabling comprehensive analyses, especially for complex diseases like cancer. Despite the development of multi-omics approaches, the challenges of scaling integration to massive, heterogeneous -omics datasets suggest that novel computational tools need to be designed. In this study, we propose an approach for integrating microRNA (miRNA) and messenger RNA (mRNA) expression data, incorporating prior biological knowledge (PBK). This approach scores and ranks groups of miRNAs and their associated genes using cross-validation iterations. The proposed method incorporates a Pattern detection (P) component to identify molecular motifs unique to each biological group. The analysis also facilitates the visualization of the groups, facilitating the identification of co-occurring groups and their characteristic features across iterations. Furthermore, the groups are scored using an over-representation analysis through a new Enrichment (E) component in each iteration. The clusters of the groups based on the Enrichment Scores (ESs) are visualized in a heatmap to obtain novel insights into the collective behavior and dependencies of the groups, aiming to understand the molecular mechanisms of complex diseases. The developed G-S-M-E tool not only provides performance metrics and biological scores at the group level but also offers comprehensive insights into intricate multi-omics interactions. In summary, our study emphasizes the importance of mathematical and data science methodologies in elucidating intricate multi-omics integration, yielding a formalized approach that deepens our comprehension of complex diseases.
Assessment of the Quality of Tuffs in Central Anatolia, Turkey: A Quantitative Classification Approach
(Acad Sci Czech Republic Inst Rock Structure & Mechanics, 2025) Koken, Ekin; Ince, Ismail
The growing global demand for dimension stones necessitates efficient and accurate evaluation methods to ensure their optimal use in various industries. To assess their suitability for various dimension stone applications, this study investigates tuffs from Central Anatolia, Turkey. For this purpose, the fundamental physical and mechanical properties of the tuffs were determined in laboratory studies, and a detailed durability assessment was conducted for each rock type. The analysis results indicate that most of the examined rocks are of low quality and more suitable for non-load-bearing applications. Based on the collected data, fuzzy clustering techniques were applied to develop a new classification system, categorising the tuffs into four classes (Class A-D) according to their potential applications. Additionally, a user-friendly MATLAB-based software tool was also developed to facilitate the implementation of the proposed classification system.
Does Your Love Lift Me Higher? A Direct Replication of the Energising Role of Secure Relationships
(John Wiley & Sons Ltd, 2025) Lagap, Adar Cem; Harma, Mehmet
Previous work has revealed that priming people with significant others increases feelings of security and energy, and in turn, boosts exploration motivations. In this preregistered study, we directly replicated Luke et al.'s (2012) Study 2 (N = 281). We found similar results as the replicated study regarding increased security feelings and exploration motivations on the self-report measures after the priming. However, we did not find any support for the increased energy feelings after the attachment security priming. In addition, contrary to Luke et al.'s (2012) results, energy feelings did not mediate the relationship between security priming and exploration motivations. A discussion of null findings, along with the limitations of self-reports and potential misinterpretation of the mediational analyses, follows. We also discuss possible future implications of the current findings.
Measuring Disaster Resilience in MENA Countries and Its Impact on Disaster Losses
(Nature Portfolio, 2025) Demir, Abdullah; Dincer, Ali Ersin; Dincer, Nazire Nergiz
Disaster resilience is a protective feature aimed at reducing the effects of natural disaster events and losses resulting from these events. This study develops a Disaster Resilience Index (DRI) for MENA countries to assess resilience across ten dimensions, including economic, social, institutional, infrastructural, and environmental factors. Unlike most prior studies, which focus on individual countries or use narrower sets of indicators, this study provides a multi-country, region-specific framework tailored to MENA's socio-economic and environmental heterogeneity. The index integrates geospatial data on disaster risk from geographic information systems (GIS) and a natural hazard risk dimension. Validation using disaster-related fatalities, supported by a dual PCA-based sensitivity analysis, confirms the robustness of the DRI and reveals that countries with stronger governance, higher human capital, and robust infrastructure tend to exhibit greater resilience, while fragile states and resource-dependent economies are more vulnerable. Notably, the DRI calculated using both dimension-specific and all-indicator PCA produces closely aligned values, indicating the choice of conducting PCA at the dimension level does not significantly alter the overall assessment of disaster resilience. These insights provide a foundation for targeted disaster risk reduction strategies and highlight areas where international cooperation and policy interventions can strengthen resilience in the region.
Clean Energy Production and Decarbonization of Energy Sector With Floating Photovoltaic Systems
(Institute of Physics, 2025) Bajc, T.; Ozgun, F.; Koca, K.; Karipoğlu, F.
Floating photovoltaic systems (FPVS) offer several advantages over traditional land-based PV systems, which has contributed to a growing global interest in their deployment. Since the energy yields are strongly dependent on location and tilt angle of FPVS, this research focuses on the clean energy production and decarbonization potential of FPVS in Serbia and Türkiye for different water bodies, such are natural and artificial lakes and dams. The research is performed for the most appropriate lakes and dams, having in mind importance of the location, energy yields potential, distance from the electricity grid and main roads, environmental impact, water depth and land type quality. Tilt angles are analyzed in a range from 5 to 40°, and the optimal angle is depicted for selected locations. The highest energy yields for Türkiye were obtained for 30° tilt angle, while for Serbia it was 36°. The results showed that possible clean energy production in both countries reaches 15345 kWh of energy in total, while the yearly carbon emissions reduction for all selected locations goes up to 10.76 tCO2/year in total. Since the legal framework for the application of FPVS is not established yet in observed countries, these results contribute to the future development of legislation in the field of FPVS and encourage the stakeholders to invest in clean energy production. © Published under licence by IOP Publishing Ltd.
Offshore Floating Modular Energy Islands: Technologies and Challenges
(Institute of Physics, 2025) Gkantou, M.; Amlashi, H.; Snæbjörnsson, J.; Skejic, D.; Ferri, G.; Marino, E.; Baniotopoulos, C.
Offshore floating modular energy islands (FMEIs) represent a promising solution to meet growing global energy demands, while addressing challenges associated with conventional energy infrastructure. This paper explores key technological components underpinning the design, construction, and energy generation of FMEIs, with a focus on three core areas: energy generation systems, floating structures and construction methods. The first technological component focuses on energy generation systems, examining the technologies of offshore wind power, solar and wave energy, to maximise overall energy production, while also highlighting the challenges involved in integrating these technologies. The second section examines the support structure of offshore floating platforms, as well as the key role of mooring systems in ensuring structural integrity under challenging marine conditions. The importance of advanced monitoring and maintenance strategies for long-term viability is also discussed. The third technological component discusses modular construction, highlighting the material choices and associated construction challenges in building FMEIs. This review also includes case studies and ongoing projects that demonstrate the real-world application of these technologies. Through the integration of advanced renewable energy generation technologies, floating and mooring systems and modular construction methods, FMEIs offer a sustainable and innovative approach to offshore energy production. This paper provides an overview of FMEIs and contributes to advancing development in this emerging field. © Published under licence by IOP Publishing Ltd.
Modular Floating Energy Islands With Green Hydrogen Integration: Design of a Small-Scale P2x Scheme
(Institute of Physics, 2025) Akpolat, A.N.; Cundeva, S.; Todorovic, J.; Rexhepi, V.; Okhay, O.; Bakon, T.; Borg, R.P.
The climate crisis and rising carbon emissions make the integration of renewable energy systems into electricity grids worldwide inevitable. In this context, modular floating energy islands (MFEI) provide innovative solutions for hybrid systems with high renewable energy penetration. This study explores the simultaneous use of various renewable resources, such as solar, wind, tidal, and wave energy, through small-scale MFEI structures that can be situated in seas and lakes. Thanks to their modular design, these systems offer benefits like scalability, portability, and ease of maintenance, allowing for flexible and adaptive developments in the energy infrastructure. As highlighted in recent literature (e.g., the North Sea Wind Power Hub and EU H2Ocean projects), offshore structures for green hydrogen production support energy storage and carbon-free fuel conversion within the Power-to-X (P2X) framework. This study evaluates the potential of photovoltaic (PV)-supported hydrogen production in MFEI structures through numerical analyses. The results emphasize the strategic role of these structures in enhancing energy security, coastal protection, and reducing carbon emissions by producing significant amounts of hydrogen. This hydrogen can be used for various purposes, including re-electrification, industrial applications, heating, and agriculture. Future research should focus on real-time data optimization, AI-supported system management, and integrated hydrogen consumption scenarios. © Published under licence by IOP Publishing Ltd.
Design and Analysis of a Solar-Assisted Combined Cooling, Heating, and Power System for Smart Cities: Case Study From Doha
(IGI Global, 2025) Akroot, A.; Almohammedi, A.A.; Talal, W.
The rising demand for sustainable and energy-efficient solutions in urban areas has driven interest in renewable systems for smart cities. This chapter presents a solar-assisted combined cooling, heating, and power (SA-CCHP) system designed for Doha, Qatar, where high solar radiation and cooling needs prevail. Powered solely by a parabolic trough collector (PTC) field, the system delivers net power from 1200 kW in winter to 195 kW in summer, with cooling loads of ~2100-3400 kW and heating loads of ~90)00-14500 kW. Increasing the superheating degree at the ORC turbine inlet enhances power and heating but reduces cooling, while raising the pressure ratio (A) from 0.5 to 0.8 boosts net output and efficiency, cutting CO2 emissions from 0.22 to 0.13 kg/kWh. Overall energy efficiency rises from 85% to 90% and exergy efficiency from 76% to 78.5%, while costs decline from $40/hr to $36/hr, confirming both environmental and economic viability. The study demonstrates the feasibility of solar-powered CCHP systems as scalable models for achieving clean energy goals in smart cities. © 2026, IGI Global Scientific Publishing. All rights reserved.
Fully Flexible, Low-Cost, Environmentally Friendly Yarn-Based Inp/Ag Nw Photodetectors for UV-Visible Light Detection
(SPIE, 2025) Savaş, M.; Akrema, A.; Ocal, S.K.; Erdem, T.
We report the fabrication and investigate of a novel photodetector using a heterostructure of InP quantum dots (QDs) and silver nanowires (Ag NWs) incorporated into yarn. This device is simple, scalable, low-cost, flexible, and functions under ambient conditions. Ag NWs and red-emitting InP QDs were separately synthesized via chemical methods and mixed in a specific ratio to coat functional yarns, which were then knitted into fabrics. The photodetector benefits from the excellent electrical conductivity of Ag NWs and the strong optical absorption of InP QDs. It shows enhanced photoelectric response in both UV and visible regions. At 405 nm illumination, the device achieves a photoresponsivity of 5.8 mA W-1 and a detectivity of 2 × 1010 Jones-values comparable to or exceeding those of similar devices. The enhanced performance is attributed to efficient charge transfer enabled by favorable band alignment between Ag NWs and InP QDs, along with synergistic effects from nanostructure dimensionality and quantum confinement. The device's combination of flexibility, sensitivity, and cost-efficiency makes it a strong candidate for wearable UV-visible photodetectors. © 2025 SPIE. All rights reserved.
Minimising the Cycle Time with Assembly Line Balancing and Worker Assignment: A Case Study in a Medical Device Manufacturer Company
(ISRES Publishing, 2025) Kayser, A.; Sipahi, G.; Sevimli, O.; Toplu, N.; Turan, S.; Satic, U.
In this research, we considered the mixed-model assembly line balancing and worker assignment problems of a medical device manufacturer in Türkiye. We combined these problems into a single integer programming model where multiple types of products can be assembled simultaneously on a single assembly line, and workers are assigned to workstations based on their abilities while ensuring the balance and efficiency of the assembly line. Our proposed approach seeks to minimise the cycle time and ability-based assignment costs on the assembly line. We used the Gurobi solver to find the optimal solution for the proposed problem. Our approach provides higher efficiency and results in a 76% increase in productivity without requiring additional work hours or workers. © 2025 Published by ISRES Publishing.

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