Qadri, Shah Sultan Mohiuddin
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Qadri, Syed Shah Sultan Mohiuddin
Sultan Mohiuddin Qadri, S.S.
Qadri, S.S.S.M.
Sultan Mohiuddin Qadri, S.S.
Qadri, S.S.S.M.
Job Title
Dr. Öğr. Üyesi
Email Address
syedshahsultan@cankaya.edu.tr
Main Affiliation
Endüstri Mühendisliği
Status
Current Staff
Website
ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
Sustainable Development Goals
13
CLIMATE ACTION
0
Research Products
8
DECENT WORK AND ECONOMIC GROWTH
0
Research Products
3
GOOD HEALTH AND WELL-BEING
0
Research Products
15
LIFE ON LAND
0
Research Products
17
PARTNERSHIPS FOR THE GOALS
1
Research Products
14
LIFE BELOW WATER
0
Research Products
4
QUALITY EDUCATION
1
Research Products
11
SUSTAINABLE CITIES AND COMMUNITIES
8
Research Products
6
CLEAN WATER AND SANITATION
0
Research Products
10
REDUCED INEQUALITIES
0
Research Products
9
INDUSTRY, INNOVATION AND INFRASTRUCTURE
2
Research Products
12
RESPONSIBLE CONSUMPTION AND PRODUCTION
0
Research Products
2
ZERO HUNGER
0
Research Products
1
NO POVERTY
0
Research Products
7
AFFORDABLE AND CLEAN ENERGY
1
Research Products
5
GENDER EQUALITY
0
Research Products
16
PEACE, JUSTICE AND STRONG INSTITUTIONS
0
Research Products
This researcher does not have a Scopus ID.
This researcher does not have a WoS ID.
Scholarly Output
13
Articles
4
Views / Downloads
41/0
Supervised MSc Theses
1
Supervised PhD Theses
0
WoS Citation Count
7
Scopus Citation Count
24
WoS h-index
1
Scopus h-index
3
Patents
0
Projects
0
WoS Citations per Publication
0.54
Scopus Citations per Publication
1.85
Open Access Source
2
Supervised Theses
1
Google Analytics Visitor Traffic
| Journal | Count |
|---|---|
| Communications in Computer and Information Science | 3 |
| 2024 Innovations in Intelligent Systems and Applications Conference, ASYU 2024 -- 2024 Innovations in Intelligent Systems and Applications Conference, ASYU 2024 -- 16 October 2024 through 18 October 2024 -- Ankara -- 204562 | 2 |
| CIEES 2024 - IEEE International Conference on Communications, Information, Electronic and Energy Systems -- 5th IEEE International Conference on Communications, Information, Electronic and Energy Systems, CIEES 2024 -- 20 November 2024 through 22 November 2024 -- Hybrid, Veliko Tarnovo -- 205627 | 1 |
| IET Conference Proceedings -- 4th International Conference on Distributed Sensing and Intelligent Systems, ICDSIS 2023 -- 21 December 2023 through 23 December 2023 -- Dubai -- 202184 | 1 |
| International Journal of Industrial Engineering Computations | 1 |
Current Page: 1 / 2
Scopus Quartile Distribution
Competency Cloud
13 results
Scholarly Output Search Results
Now showing 1 - 10 of 13
Conference Object 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.Conference Object Citation - Scopus: 1Enhanced Task Scheduling in Iaas Cloud Environments Using Elitism-Based Genetic Algorithms(Institute of Electrical and Electronics Engineers Inc., 2024) Osama, M.; Sultan Mohiuddin Qadri, S.S.; Shams Malick, R.A.; Shahid, M.F.; Dawood, K.Cloud computing (CC) is a modern commercial model that enables customers to acquire large amounts of virtual resources on demand. Among the various service models in CC, Infrastructure as a Service (IaaS) provides Virtual Machines (VMs) and data centers. Efficient task scheduling, which maps cloud tasks to VMs, is key to optimizing data center performance and reducing energy consumption. Given the heterogeneous nature and computational intensity of these tasks, meta-heuristic methods are often employed for scheduling. This research proposes an enhanced Genetic Algorithm (GA) that integrates an Elitism-Based strategy with Conditional Parameter Tuning to improve convergence speed and solution quality. The elitism approach preserves top-performing solutions across generations, while conditional parameter tuning dynamically adjusts algorithm parameters based on population diversity and fitness levels. Experimental evaluations on Amazon EC2 show that the proposed method significantly outperforms traditional approaches in task completion time, resource utilization, and convergence efficiency. The results demonstrate the effectiveness of combining elitism with adaptive strategies to create a scalable, robust solution for task scheduling in high-demand cloud environments. © 2024 IEEE.Article Citation - WoS: 1Citation - Scopus: 4Assessing Traffic Performance: Comparative Study of Human and Automated Hgvs in Urban Intersections and Highway Segments(Univ Tun Hussein onn Malaysia, 2024) Almusawi, Ali; Albdairi, Mustafa; Qadri, Syed Shah Sultan MohiuddinThis study conducts a comparative analysis of traffic dynamics at urban signalized intersections and on highways, incorporating both human-operated and automated heavy goods vehicles (HGVs) using the PTV VISSIM simulation model. It examines the impacts of automated driving technologies on critical traffic performance metrics such as queue length, travel time, vehicle delay, emissions, and fuel consumption. Initial findings indicate that automation in HGVs enhances traffic flow, particularly by reducing queue lengths and vehicle delays. However, varying levels of automation from cautious to aggressive reveal complex trade-offs between operational efficiency and environmental impacts. On highways, automated HGVs demonstrate superior performance by reducing travel times and delays while increasing throughput compared to human-driven HGVs. These results underscore the operational benefits of automated HGVs under diverse traffic conditions and highlight their significant implications for transportation planning and policy-making. This research contributes valuable insights into the integration of automated technologies in transportation systems, facilitating informed decision-making for stakeholders considering the adoption of these advancements in the current infrastructure.Article Citation - WoS: 6Citation - Scopus: 14Integrating Autonomous Vehicles (Avs) Into Urban Traffic: Simulating Driving and Signal Control(Mdpi, 2024) Almusawi, Ali; Albdairi, Mustafa; Qadri, Syed Shah Sultan MohiuddinThe integration of autonomous vehicles into urban traffic systems offers a significant opportunity to improve traffic efficiency and safety at signalized intersections. This study provides a comprehensive evaluation of how different autonomous vehicle driving behaviors-cautious, normal, aggressive, and platooning-affect key traffic metrics, including queue lengths, travel times, vehicle delays, emissions, and fuel consumption. A four-leg signalized intersection in Balgat, Ankara, was modeled and validated using field data, with twenty-one scenarios simulated to assess the effects of various autonomous vehicle behaviors at penetration rates from 25% to 100%, alongside human-driven vehicles. The results show that while cautious autonomous vehicles promote smoother traffic flow, they also result in longer delays and higher emissions due to conservative driving patterns, especially at higher penetration levels. In contrast, aggressive and platooning autonomous vehicles significantly improve traffic flow and reduce delays and emissions. Mixed-behavior scenarios reveal that different driving styles can coexist effectively, balancing safety and efficiency. These findings emphasize the need for optimized autonomous vehicle algorithms and signal control strategies to harness the potential benefits of autonomous vehicle integration in urban traffic systems fully, particularly in terms of improving traffic performance and sustainability.Master Thesis Sinyal Kontrollü Kavşaklarda Otonom Araçların Trafik Verimliliği Üzerindeki Etkisinin Değerlendirilmesi(2024) Albdairi, Mustafa Azhar Hussein; Al-Musawi, Ali Abdulhussein Abdulridha; Qadri, Syed Shah Sultan MohiuddinKentsel hareketliliğin gelişen bağlamında, otonom araçların (AV'ler) entegrasyonu, özellikle kentsel trafik yönetiminin hayati bir bileşeni olan sinyalize kavşaklarda büyük vaatler ve zorluklar sunmaktadır. Bu tez, Ankara Balgat'taki sinyalize bir kavşakta otonom araçların karmaşık dinamiklerini, PTV VISSIM kullanarak mikroskobik trafik simülasyonu ile incelemektedir. Çalışma, farklı AV sürüş davranışları (tedbirli, normal, agresif, platoon ve karışık) ve %25'ten %100'e kadar değişen penetrasyon oranları ile insan sürüş senaryolarını içeren 21 senaryoyu kapsamaktadır. Bulgular, sinyal kontrol optimizasyonu öncesi ve sonrası belirgin etkiler ortaya koymaktadır. Optimizasyon öncesinde, güvenliği ön planda tutan tedbirli AV davranışları, özellikle yüksek penetrasyon oranlarında, kuyruk uzunlukları, seyahat süreleri, gecikmeler, emisyonlar, yakıt tüketimi ve çatışma noktalarında artışa neden olmuştur. Normal AV davranışları, %25 penetrasyon oranında başlangıçta insan sürüşüne benzerlik göstermiş, ancak penetrasyon oranları arttıkça kuyruk uzunlukları, seyahat süreleri, gecikmeler ve çatışmalarda iyileşmeler göstermiştir, ancak emisyonlar ve yakıt tüketimi biraz daha yüksek kalmıştır. Agresif AV davranışları, tüm penetrasyon oranlarında kuyruk uzunlukları, seyahat süreleri, gecikmeler, emisyonlar, yakıt tüketimi ve çatışma noktalarını önemli ölçüde azaltmış ve %100 penetrasyonda en olumlu sonuçlara ulaşmıştır. Platoon davranışları da kuyruk uzunluklarını, seyahat sürelerini, gecikmeleri ve çatışmaları azaltmış, ancak %100 penetrasyonda platoon liderliğinin dinamikleri nedeniyle emisyonlarda ve yakıt tüketiminde artış göstermiştir. Optimizasyon sonrası, tüm AV davranışlarında önemli iyileşmeler gözlemlenmiştir. %0 AV penetrasyonunda insan sürüş senaryolarında kuyruk uzunlukları, seyahat süreleri, gecikmeler ve çatışma noktalarında önemli azalmalar yaşanmıştır. Tedbirli AV'ler, diğer davranışlara göre hala daha yüksek değerlere sahip olmasına rağmen, tüm penetrasyon oranlarında iyileşmeler göstermiştir. Normal AV'ler, optimizasyon sonrası daha yüksek penetrasyon oranlarında tüm metriklerde önemli düşüşler göstermiştir. Agresif AV'ler, özellikle %100 penetrasyonda, tüm metriklerde en iyi sonuçlara ulaşmıştır. Platoon AV'ler de önemli iyileşmeler göstermiştir, ancak %100 penetrasyonda liderin etkisi nedeniyle emisyonlar ve yakıt tüketiminde artışlar görülmüştür. Özellikle, daha kısa sinyal sürelerinin kuyruk uzunlukları, seyahat süreleri, gecikmeler, emisyonlar ve yakıt tüketimini azaltmada daha uzun sürelerden daha iyi performans gösterdiği gözlemlenmiştir. Karışık davranış senaryosu, tüm penetrasyon oranlarında sürekli olarak kuyruk uzunluklarını, seyahat sürelerini, gecikmeleri ve çatışmaları azaltmış ve emisyonlar ve yakıt tüketiminde insan sürüş senaryolarına benzer eğilimler sergilemiştir. Alan verileri kullanılarak yapılan doğrulama, modelin doğruluğunu sağlamış ve AV'lerin mevcut trafik yapısına entegrasyonu için pratik içgörüler sunmuştur. Bu çalışma, AV'lerin kentsel trafik yönetimini iyileştirmedeki dönüştürücü potansiyelini vurgulamakta ve AV entegrasyonu ve optimizasyonu alanında gelecekteki araştırmalar için bir temel oluşturmaktadır.Conference Object Optimization of Signalized Intersections: Analyzing Autonomous Vehicle Behaviors Through Data-Driven Simulations(Springer Science and Business Media Deutschland GmbH, 2026) Qadri, Syed Shah Sultan Mohiuddin; Albdairi, Mustafa; Almusawi, Ali; Kabarcik, Ahmet; Abdulrahman, H. S.Autonomous vehicles (AVs) present a transformative opportunity to enhance traffic flow, particularly at urban intersections where delays are most frequent. This study investigates how different AV driving behaviors and penetration rates affect traffic efficiency at signalized intersections. Using a microscopic simulation model in PTV VISSIM, the research centers on a four-way intersection in Balgat, Ankara. Five AV driving behaviors—cautious, normal, aggressive, platooning, and mixed—are modeled under various signal cycle lengths. The simulation’s accuracy was ensured through calibration and validation with real-world traffic data. The findings reveal that the integration of AVs can significantly improve traffic flow, with aggressive and platooning driving behaviors achieving the most notable reduction in vehicle delays, particularly at shorter cycle lengths (60–70 s). Increased AV penetration rates amplify these positive effects, reducing delays and queue lengths in all tested scenarios. In contrast, cautious AV behaviors led to more significant delays, highlighting the importance of intelligent AV driving strategies for optimizing traffic management. The results underscore that optimizing signal cycle lengths with AV integration can reduce congestion and improve urban traffic flow. While the study demonstrates the potential of AVs to enhance urban traffic management, it also stresses the need for real-world validation and the development of adaptive traffic signal systems capable of accommodating diverse driving behaviors. These insights offer urban planners and policymakers valuable guidance on integrating AVs into current infrastructure to create more resilient and efficient transportation networks. © 2025 Elsevier B.V., All rights reserved.Conference Object 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.Article An ALNS-Based Decision Support System for Scheduling and Routing in Home Healthcare With Lunch Break Constraints(Growing Science, 2025) Ozsakalli, Gokberk; Ozturkoglu, Omer; Qadri, Syed Shah Sultan MohiuddinThis study addresses the daily scheduling and routing problem for home healthcare workers while incorporating lunch break requirements. The Home Healthcare Scheduling and Routing Problem is analysed alongside its common constraints, including patient and caregiver time windows, caregiver qualifications, and mandated breaks. To address this, four different variants of an effective Adaptive Large Neighbourhood Search (ALNS) algorithm were developed to provide high-quality solutions. The algorithms demonstrate significant efficiency, solving 30-patient instances optimally within an average of 12 seconds. For scenarios involving 100 patients, they maintained robust performance with a slight increase in computational time of about 54 seconds. Results indicate operational efficiency improvements of up to 36% through optimized travel routes and patient visitation schedules. To translate these findings into practice, a decision support system, the Home Healthcare Decision Support System (HHDSS), was designed to assist administrators by automating the complex task of scheduling and routing of caregivers. Tested using realistic patient data generated from Turkey, the system effectively allocates healthcare resources and improves responsiveness. Overall, the proposed framework shows strong potential as a valuable practical tool for improving the responsiveness and efficiency of home healthcare logistics. (c) 2026 by the authors; licensee Growing Science, CanadaConference Object Citation - Scopus: 3Microscopic 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.Article Evaluation of Robust Evacuation Strategies for Resilient Urban Infrastructure Through Microscopic Traffic Simulation(Univ Studi Trieste, Ist Studio Trasporti integrazione Econ Europea-Istiee, 2025) Kabarcık, Ahmet; Qadri, Syed Shah Sultan Mohiuddin; Qadri, Shah Sultan Mohiuddin; Athar, Ambreen Ilyas; Albdairi, Mustafa; Kabarcik, Ahmet; Endüstri MühendisliğiNatural disasters are a global threat, highlighting the urgent need for effective disaster management systems worldwide. Many countries, both developed and developing, are not adequately prepared, emphasizing the importance of governmental action. Key to disaster management is the creation of specialized disaster management units that develop and implement rapid response plans for potential risks. A crucial aspect of disaster management is evacuation-the process of moving vulnerable populations to safer areas. However, evacuations face challenges such as timely alert issuance, traffic congestion, resident reluctance to evacuate, and potential damage to transportation infrastructure. These challenges can be mitigated through comprehensive evacuation plans that ensure smooth relocation to shelters. This paper addresses these issues by developing and evaluating traffic routing conditions in an evacuation study area using the microscopic simulator SUMO. It examines two algorithms, Dijkstra and A-star (A*), which optimize vehicle routes under different network conditions. By focusing on criteria such as Minimum Travel Time and Maximum Number of Evacuations (clearance time), the research aims to improve disaster response and resilience. The objective is to enhance evacuation procedures, thereby strengthening disaster management and ensuring the safety of affected populations. Results show that the A* algorithm outperforms Dijkstra, reducing travel times by up to 18% and network clearance times by up to 6.8% under optimal conditions. The Manhattan-based network design further enhances evacuation efficiency, reducing average waiting time by up to 35% compared to the actual map.
