Dr. Mahmoud Masoud’s research encompasses a broad range of areas within Operations Research, Mathematical Sciences, artificial intelligence, and optimization. His work focuses on developing innovative solutions that address complex problems across various industrial sectors.

One of Dr. Masoud’s primary research interests lies in optimization techniques, particularly as they apply to real-world challenges in transportation, logistics and health systems optimization. He has led significant projects related to automated driving and intelligent transport systems, exploring how advanced algorithms can improve safety and efficiency on the roads. For instance, his involvement in the Cooperative and Highly Automated Driving (CHAD) initiative aims to integrate cutting-edge technologies into vehicular systems, fostering safer and more efficient transportation networks.

Furthermore,  his work focuses on developing innovative methodologies to enhance the efficiency and effectiveness of healthcare delivery systems. Through his extensive research, Dr. Masoud aims to improve patient pathways, resource management, and overall performance in emergency care settings. His contributions have significantly advanced the understanding of optimization techniques in healthcare, making a positive impact on patient outcomes and operational efficiency.

In the realm of artificial intelligence, Dr. Masoud has explored machine learning applications for real-time data analysis and decision-making. His research includes developing models for crash and near-crash detection based on naturalistic driving data, which can significantly enhance road safety. Additionally, his work on detecting vulnerable road users using smartphone sensors demonstrates the potential of AI in addressing critical safety issues in urban environments.

Dr. Masoud is also deeply engaged in the field of supply chain management, where he applies optimization and decision support systems to enhance operational efficiency. His projects have included optimizing harvest and transport systems in agricultural contexts, as well as developing methodologies for minimizing environmental impact during transportation—a crucial consideration in today’s sustainability-focused landscape.

His research has not only resulted in academic publications but has also led to practical applications through partnerships with industry and governmental organizations. For example, collaborations with universities in Saudi Arabia have focused on knowledge-sharing workshops about connected and automated vehicles, fostering a deeper understanding of these technologies within the region.

Moreover, Dr. Masoud emphasizes the importance of education and mentorship in his career. He has taught a variety of courses for undergraduate and postgraduate students, integrating both traditional and online learning methodologies to cater to diverse student needs. His role in supervising graduate research students at  KFUPM and QUT reflects his commitment to nurturing the next generation of researchers and practitioners in the field.

Through his extensive research and teaching efforts, Dr. Masoud aims to contribute to the advancement of knowledge in operations research and its applications. He actively participates in professional organizations such as the Australian Society of Operations Research (ASOR) and collaborates with global entities, including Amazon, to further enhance the impact of his work.

PhD – Operations Research and Mathematical Sciences- Queensland University of Technology, Australia, 2012.

M.SC.  in Operations Research and Decision Support Systems, Cairo University, 2004

B.SC. in Mathematics and Computers, Cairo Univerty, 1998.

Operations Research

Mathematical Sciences

Artificial Intellgence and Machine Learning

Data Science

Optimization

1. Masoud, M. (2024). Machine learning-based modeling of celeration for predicting red-light violations. IEEE Open Journal of Intelligent Transportation Systems.
2. Zhang, Q., Liu, S. Q., D’Ariano, A., Chung, S. H., Masoud, M., & Li, X. (2024). A bi-level programming methodology for decentralized mining supply chain network design. Expert Systems with Applications, 250, 123904.
3. AlKhars, M., Masoud, M., AlNasser, A., & Alsubaie, M. (2024). Sustainable practices and firm competitiveness: An empirical analysis of the Saudi Arabian energy sector. Discover Sustainability, 5(1), 146.
4. Hussain, M., Glaser, S., Larue, G. S., Dehkordi, S. G., & Masoud, M. (2024). A cooperative lane-change behaviour evaluation for connected and autonomous vehicles in road work zones environments. IEEE Transactions on Intelligent Vehicles.
5. Pan, W., Liu, S. Q., Kumral, M., D’Ariano, A., Masoud, M., Khan, W. A., & Bakather, A. (2024). Iron ore price forecast based on a multi-echelon tandem learning model. Natural Resources Research, 1-24.
6. Liu, S. Q., Liu, L., Kozan, E., Corry, P., Masoud, M., Chung, S. H., & Li, X. (2024). Machine learning for open-pit mining: A systematic review. International Journal of Mining, Reclamation and Environment, 1-39.
7. Masoud, M., Abdelhay, A., & Elhenawy, M. (2024). Exploring combinatorial problem solving with large language models: A case study on the Travelling Salesman Problem using GPT-3.5 Turbo. arXiv preprint arXiv:2405.01997.
8. Luan, F., Tang, B., Li, Y., Liu, S. Q., Yang, X., Masoud, M., & Feng, B. (2024). Solving multi-objective green flexible job shop scheduling problem by an improved chimp optimization algorithm. Journal of Intelligent & Fuzzy Systems, 1-14.
9. Khan, W. A., Masoud, M., Eltoukhy, A. E. E., & Ullah, M. (2024). Stacked encoded cascade error feedback deep extreme learning machine network for manufacturing order completion time. Journal of Intelligent Manufacturing, 1-27.
10. Masoud, M. (2023). A hybrid K-Means and particle swarm optimization technique for solving the rechargeable e-scooters problem. IEEE Access, 11, 132472-132482.
11. Zeng, L., Liu, S. Q., Kozan, E., Burdett, R., Masoud, M., & Chung, S. H. (2023). Designing a resilient and green coal supply chain network under facility disruption and demand volatility. Computers & Industrial Engineering, 183, 109476.
12. Elhenawy, M., Masoud, M., Haworth, N., Young, K., Rakotonirainy, A., & others. (2023). Detection of driver distraction in the Australian naturalistic driving study videos using pre-trained models and transfer learning. Transportation Research Part F: Traffic Psychology and Behaviour, 97, 31-43.
13. Masoud, M., Hsieh, J., Helmstedt, K., McGree, J., & Corry, P. (2023). An integrated pasture biomass and beef cattle liveweight predictive model under weather forecast uncertainty: An application to Northern Australia. Food and Energy Security, 12(3), e453.
14. Ghanem, A., Abdelhay, A., Salah, N. E., Nour Eldeen, A., Elhenawy, M., & others. (2023). Leveraging cross-view geo-localization with ensemble learning and temporal awareness. PLoS One, 18(3), e0283672.
15. Komol, M. M. R., Elhenawy, M., Masoud, M., Rakotonirainy, A., Glaser, S., & others. (2023). Deep RNN based prediction of driver’s intended movements at intersection using cooperative awareness messages. IEEE Transactions on Intelligent Transportation Systems, 24(7), 6902-6921.
16. Masoud, M., Elhenawy, M., Liu, S. Q., Almannaa, M., Glaser, S., & Alhajyaseen, W. (2023). A simulated annealing for optimizing assignment of e-scooters to freelance chargers. Sustainability, 15(3), 1869.
17. Masoud, M. (2023). An adaptive tabu search optimisation algorithm for solving e-scooters battery swapping problem. Qatar University Press.
18. Liu, S. Q., Kozan, E., Masoud, M., Li, D., & Luo, K. (2022). Multi-stage mine production timetabling with optimising the sizes of mining operations: An application of parallel-machine flow shop scheduling with lot streaming. Annals of Operations Research, 1-27.
19. Liu, S. Q., Lin, Z., Li, D., Li, X., Kozan, E., & Masoud, M. (2022). Recent research agendas in mining equipment management: A review. Mining, 2(4), 769-790.
20. Luan, F., Li, R., Liu, S. Q., Tang, B., Li, S., & Masoud, M. (2022). An improved sparrow search algorithm for solving the energy-saving flexible job shop scheduling problem. Machines, 10(10), 847.
21. Elhenawy, M., Larue, G., Masoud, M., Rakotonirainy, A., & Haworth, N. (2022). Using random forest to test if two-wheeler experience affects driver behaviour when interacting with two-wheelers. Transportation Research Part F: Traffic Psychology and Behaviour.
22. Alharbi, M. G., Stohy, A., Elhenawy, M., Masoud, M., Khalifa, H. A. E. W. (2022). Solving pickup and drop-off problem using hybrid pointer networks with deep reinforcement learning. PLoS One, 17(5), e0267199.
23. Luan, F., Li, R., Liu, S. Q., Tang, B., Li, S., & Masoud, M. (2022). An improved sparrow search algorithm for solving the energy-saving flexible job shop scheduling problem. Machines, 10, 847. https://doi.org/10.3390/machines10090847
24. Liu, S. Q., Kozan, E., Corry, P., Masoud, M., & Luo, K. (2022). A real-world mine excavators timetabling methodology in open-pit mining. Optimization and Engineering. https://doi.org/10.1007/s11081-022-09741-9
25. Rahman Komol, M. M., Elhenawy, M., Yasmin, S., & Masoud, M. (2022). A review on drivers red light running behavior predictions and technology based countermeasures. IEEE Access, 10, 25309-25326. https://doi.org/10.1109/ACCESS.2022.3152674