Browsing by Author "Qadri, S.S.S.M."

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Design and Implementation of a Custom ERP Framework for a Drilling Equipment Manufacturer
(Springer Science and Business Media Deutschland GmbH, 2025) Torunoğlu, D.; Erkoç, E.C.; Abay, Z.E.; Qadri, S.S.S.M.; Gök, E.C.; Karataş, D.; Güçlüer, G.
This study presents the design and implementation of a web-based Enterprise Resource Planning (ERP) system tailored for a small-to-medium-sized enterprise (SME) operating in the manufacturing sector. With a focus on GEO Sondaj Makine İmalat LTD. ŞTİ, the system was developed to digitize and streamline core operational workflows, including sales order processing, production scheduling, inventory management, procurement, and coordination between customers and suppliers. Built using the Django web framework, the ERP platform provides modular functionality with real-time data integration across departments. Unlike generic ERP packages, this custom-built solution mirrors the company’s actual business processes and addresses typical challenges faced by SMEs, such as limited IT infrastructure, absence of digital records, and resistance to organizational change. The internally developed modules led to enhanced traceability, operational efficiency, and data-driven decision-making. The system also includes a simulation module to support production visualization and planning, although advanced features like bottleneck identification and dynamic queue tracking remain under development. The findings demonstrate that a cost-effective, scalable ERP system can be successfully deployed in resource-constrained environments when grounded in business-specific needs. The system was evaluated based on internal testing, interdepartmental workflow validation, and observed improvements in operational efficiency and traceability. This project offers a practical reference for other SMEs seeking to modernize their operations through digital integration. © 2025 Elsevier B.V., All rights reserved.
From Sawdust To Shipment: Leveraging Flexsim Simulation Models To Optimize Pallet Production Operations
(Institute of Electrical and Electronics Engineers Inc., 2024) Yeter, Ö.; Güngör, B.; Qadri, S.S.S.M.
This study is focused on optimizing the production facility of Burta Enerji Yatırım A.Ş. (BE), a growing company in the renewable energy sector that is known for its pellet production. The project used advanced simulation software, FlexSim, to improve the efficiency of pellet production and the effectiveness of its logistics capacity to customers. Through detailed simulations, the research replicated complex real-life processes in a controlled and cost-effective simulated environment, allowing for a thorough examination of the facility's operational dynamics. The analysis identified significant bottlenecks, interruptions in workflow, and issues in capacity utilization, pinpointing the critical factors that influence overall system performance. By evaluating a range of production configurations, the research established optimal workflow management strategies aimed at maximizing production throughput. Furthermore, this study explored how strategic adjustments in the production line setup and logistics could significantly reduce operational costs and improve customer satisfaction, ultimately contributing to a more sustainable business model. This paper presents a detailed narrative on the use of simulation techniques to refine production and logistics operations, enhancing operational efficiency and service quality in the renewable energy industry. © 2024 IEEE.
Intelligent and Energy-Aware Task Scheduling in Cloud Systems
(Springer Science and Business Media Deutschland GmbH, 2025) Böke, K.N.; Qadri, S.S.S.M.; Kabarcik, A.
The rapid advancement of information technologies has significantly reshaped industrial operations and daily life, leading to a growing demand for responsive and scalable digital services. Among the technologies addressing this growing need, cloud computing has emerged as a foundational infrastructure for delivering on-demand computing resources over the internet. However, its increasing adoption presents complex challenges such as managing dynamic workloads and minimizing virtual machine (VM) usage costs. Therefore, cloud service providers aim to optimize performance and reduce the operational costs of VMs by integrating intelligent scheduling algorithms. In response to this need, the present study explores the use of algorithms, particularly focusing on machine learning driven approaches, to enhance the sustainability and efficiency of cloud systems. Specifically, the study investigates the effectiveness of reinforcement learning through Q-learning for optimizing task scheduling against the traditional Round Robin (RR) scheduling algorithm. The primary objective is to evaluate their performance in minimizing VM usage costs within dynamic and continuously evolving cloud environments. Experimental results indicate that in reducing costs, Q-learning outperforms RR with a 33.14% improvement, demonstrating its superior adaptability and cost efficiency under varying conditions. These insights highlight the potential of reinforcement learning to enable intelligent and cost-aware scheduling strategies in modern cloud computing systems. © 2025 Elsevier B.V., All rights reserved.
A Linear Programming Approach To Carpooling: Enhancing Commute Efficiency at Federal University of Technology Minna
(Institute of Electrical and Electronics Engineers Inc., 2024) Abdulrahman, H.S.; Almusawi, A.; Bamisaye, R.T.; Qadri, S.S.S.M.; Dawood, K.
This study explores the development of a carpooling system specifically designed for the Federal University of Technology Minna staff, utilizing the Civil Engineering Department as a case study. Amidst the escalating concerns of environmental sustainability, traffic congestion, and the economic burdens of individual commuting, carpooling presents itself as a sustainable alternative. Employing a mixed-methods approach, this research integrates a comprehensive survey to assess staff attitudes towards carpooling with the development of a linear programming model aimed at optimizing vehicle routes and allocations. The findings from the survey indicate a significant willingness among the staff to engage in carpooling, motivated by the anticipated benefits such as cost savings and reduced commuting times. The linear programming model further validates the practicality of substantially lowering total travel distances and emissions when compared to solo commuting practices. This targeted investigation showcases the carpooling system's capability not only to enhance commute efficiency among university staff but also positions it as a replicable and sustainable model for other academic institutions. The study contributes valuable insights into the design and operationalization of effective carpooling strategies within the university landscape, proposing a scalable framework applicable to similar urban contexts. © 2024 IEEE.
Citation - Scopus: 3
Microscopic Insights Into Autonomous Vehicles' Impact on Travel Time and Vehicle Delay
(Institution of Engineering and Technology, 2023) Almusawi, A.; Albdairi, M.; Qadri, S.S.S.M.
The future of highway travel is being reshaped by autonomous vehicles (AVs). This microscopic study, conducted along a 9-kilometer highway in Ankara, Turkey, explores the dynamic relationship between AVs and travel time, as well as vehicle delay. Analyzing 17 scenarios with varying AV penetration rates (ranging from 25% to 100%) and diverse AV behaviors (cautious, normal, aggressive, and mixed) uncovered intriguing patterns. Cautious AVs, while promoting safety, introduced slightly slower travel times. In contrast, aggressive AVs prioritized efficiency and reduced travel times, striking a delicate balance between speed and safety. The introduction of mixed AV fleets demonstrated an exciting equilibrium, delivering competitive travel times and mitigating delays. Most notably, the presence of AVs in all configurations exhibited the potential to relieve congestion and enhance overall traffic flow. The findings offer a compelling microscopic perspective on the transformative potential of AVs in shaping the future of highway transportation. Understanding the complex dynamics of travel time and delay is critical for informed policy decisions and the evolution of urban mobility as autonomous vehicles (AVs) continue to improve. © The Institution of Engineering & Technology 2023.
Citation - Scopus: 3
Optimizing Traffic Signal Timing at Urban Intersections: a Simheuristic Approach Using Ga and Sumo
(Institute of Electrical and Electronics Engineers Inc., 2024) Qadri, S.S.S.M.; Almusawi, A.; Albdairi, M.; Esirgün, E.
This study introduces an innovative simheuristic framework that integrates the Simulation of Urban MObility (SUMO), a detailed microsimulation tool, with the Genetic Algorithm (GA), a robust optimization method, for optimizing traffic signal timing (TST) at signalized intersections. Specifically designed to be applied to typical four-leg intersection phase plans, this framework systematically determines the most effective green signal timings to enhance traffic flow efficiency and reduce environmental impact. By meticulously testing each potential TST solution generated by the GA, using SUMO to simulate its real-world impacts, the framework provides a thorough assessment of various signal timing strategies. Comparative analyses against established methodologies, such as the Particle Swarm Optimization (PSO) algorithm and Webster's traditional method, are conducted during peak traffic demand periods to evaluate the framework's effectiveness in managing congestion and emissions. Our results demonstrate that the proposed simheuristic approach significantly outperforms the benchmarks: it achieves a reduction in CO levels by 4.97% compared to PSO and 11.76% compared to Webster; NOx emissions are reduced by 2.5% and 3.94%, respectively; and PMx levels see a decrease of 3.83% and 6.58%. These improvements underscore the substantial benefits of the framework in both traffic flow efficiency and environmental sustainability, providing critical insights for traffic engineers and urban planners aiming to implement advanced TST strategies in complex urban settings. This study not only enhances understanding of dynamic traffic management but also supports sustainable urban development goals. © 2024 IEEE.