Dr. Mahmoud Masoud received his Ph.D. in Operations Research and decsion Science from Queensland University of Technology, Australia, in 2012, with a thesis titled “Scheduling Techniques to Optimize Rail Operations”, which was nominated for QUT’s Outstanding Doctoral Thesis Award.

Masoud is Currently V. Associate Professor in the Department of Information Systems & Operations Management at King Fahd University of Petroleum and Minerals (KFUPM). He has led research and development initiatives in operations research, supply chain systems , optimization, artificial intelligence, large language model, mathematical modeling, and data science, to address challenges in Intellgent transport systms, logistics, mobility/micromobility Connected/Automated, coal mining, road safety, supply chain management, healthcare systems, and smart manufacturing.

Masoud has led more than 20 large-scale industrial and academic projects across Australia, Saudi Arabia, China, and abroad. Notable examples include the Cooperative and Highly Automated Driving (CHAD) Safety project in Australia, which was twice a finalist for the ITS Australia National Award in the Automated Vehicle Category (2019 and 2020); projects in Saudi Arabia focused on hybrid truck–drone delivery optimization and desert road safety through AI-powered camel detection systems; and research in China on mining scheduling methodologies. His recent initiatives also include developing smart manufacturing algorithms using deep learning and designing decentralized supply chain networks for mining operations.

Masoud has published more than 86 peer-reviewed papers, where the majority have been published in Q1-ranked journals.

He has over 15 years of teaching experience in undergraduate and postgraduate programs and is a Fellow of the Higher Education Academy (FHEA), UK.

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

Decsion Science

Supply Chain Management

AI and Data Science, LLMs. MLLMs

Khan, W. A., Chung, S. H., Liu, S. Q., Masoud, M., & Wen, X. (2025). Smoothing and Matrix Decomposition-Based Stacked Bidirectional GRU Model for Machine Downtime Forecasting. IEEE Transactions on Systems, Man, and Cybernetics: Systems.

Masoud, M., Omar Ashraf, and Mohammed Elhenawy. “Employing Hybrid Pointer Networks with Deep Reinforcement Learning for Drone Routing in Delivery Using Public Transportation as Carriers.” IEEE Access(2025).

Khan, W.A., Masoud, M., Eltoukhy, A.E.E. et al. Stacked encoded cascade error feedback deep extreme learning machine network for manufacturing order completion time. Journal of Intelligent Manufacturing (2025). https://doi.org/10.1007/s10845-023-02303-0.

Liu, S. Q., Liu, L., Kozan, E., Corry, P., Masoud, M., Chung, S. H., & Li, X. (2025). Machine learning for open-pit mining: a systematic review. International Journal of Mining, Reclamation and Environment, 39(1), 1-39.

Zhang, Q., Liu, S. Q., D’Ariano, A., Chung, S. H., Masoud, , & Li, X. (2024). A bi-level programming methodology for decentralized mining supply chain network design. Expert Systems with Applications, 250, 123904.

AlKhars, , 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.

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.

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.

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.

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. IF: 7.9 .

Elhenawy, M., Masoud, M., Haworth, N., Young, K., Rakotonirainy, A., Grzebieta, R., & Williamson, A. (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. IF: 4.1Masoud, “A Hybrid K-Means and Particle Swarm Optimization Technique for Solving the Rechargeable E-Scooters Problem,” in IEEE Access, vol. 11, pp. 132472-132482, 2023, doi: 10.1109/ACCESS.2023.3336810. IF: 3.87 .

Komol, M. M. R., Elhenawy, M., Masoud, M., Rakotonirainy, A., Glaser, S., Wood, M., & Alderson, D. (2023). Deep RNN Based Prediction of Driver’s Intended Movements at Intersection Using Cooperative Awareness Messages. IEEE Transactions on Intelligent Transportation Systems. IF: 9.55