Su Nguyen is a Senior Lecturer (AI and Analytics) at RMIT University, Australia. He received his Ph.D. degree in Artificial Intelligence and Operations Research from Victoria University of Wellington (VUW), Wellington, New Zealand, in 2013. His expertise includes simulation-optimization, evolutionary computation, automated algorithm design, interfaces of artificial intelligence and operations research, and their applications in logistics, energy, and transportation. Nguyen has a strong track record in developing simulation models, simulation-based decision support tools, and simulation-optimisation algorithms for industry applications. He has 70+ publications in top peer-reviewed journals and conferences in computational intelligence and operations research. His current research focuses on hybrid intelligence systems that combine the power of modern artificial intelligence technologies and operations research methodologies. He was the chair (2014-2018) of IEEE task force on Evolutionary Scheduling and Combinatorial Optimisation and is a member of IEEE CIS Data Mining and Big Data technical committee. He delivered tutorials about evolutionary simulation-optimisation and AI-based visualisation at Parallel Problem Solving from Nature Conference (2018), IEEE World Congress on Computational Intelligence (2020), and Genetic and Evolutionary Computation Conference (2022).
Selected publications
- Zhang, F.,Mei, Y.,Nguyen, S.,Zhang, M. (2024). Survey on Genetic Programming and Machine Learning Techniques for Heuristic Design in Job Shop Scheduling In: IEEE Transactions on Evolutionary Computation, 28, 147 – 167
- Thiruvady, D.,Nguyen, P.,Sun, Y.,Shiri, F.,Zaidi, N.,Li, X. (2024). Adaptive population-based simulated annealing for resource constrained job scheduling with uncertainty In: International Journal of Production Research, , 1 – 24
- Saeed, N.,Nguyen, S.,Cullinane, K.,Gekara, V.,Chhetri, P. (2023). Forecasting container freight rates using the Prophet forecasting method In: Transport Policy, 133, 86 – 107
- Wu, J.,Nguyen, S.,Alahakoon, D. (2023). Explainable Network Pruning for Model Acceleration Based on Filter Similarity and Importance In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Auckland, New Zealand, 24/11/2022-25/11/2022
- Tran, B.,Sudusinghe, C.,Nguyen, S.,Alahakoon, D. (2023). Building interpretable predictive models with context-aware evolutionary learning In: Applied Soft Computing, 132, 1 – 13
- Zhang, F.,Mei, Y.,Nguyen, S.,Zhang, M. (2023). Multitask Multiobjective Genetic Programming for Automated Scheduling Heuristic Learning in Dynamic Flexible Job-Shop Scheduling In: IEEE Transactions on Cybernetics, 53, 4473 – 4486
- Zhang, F.,Mei, Y.,Nguyen, S.,Tan, K.,Zhang, M. (2023). Task Relatedness Based Multitask Genetic Programming for Dynamic Flexible Job Shop Scheduling In: IEEE Transactions on Evolutionary Computation, 27, 1705 – 1719
- Zhang, F.,Mei, Y.,Nguyen, S.,Tan, K.,Zhang, M. (2023). Instance Rotation Based Surrogate in Genetic Programming with Brood Recombination for Dynamic Job Shop Scheduling In: IEEE Transactions on Evolutionary Computation, 27, 1192 – 1206
- Nguyen, S.,O’Keefe, G.,Arisian, S.,Trentelman, K.,Alahakoon, D. (2023). Leveraging explainable AI for enhanced decision making in humanitarian logistics: An Adversarial CoevoluTION (ACTION) framework In: International Journal of Disaster Risk Reduction, 97, 1 – 19
- Zhang, F.,Mei, Y.,Nguyen, S.,Zhang, M. (2022). Importance-Aware Genetic Programming for Automated Scheduling Heuristics Learning in Dynamic Flexible Job Shop Scheduling In: Proceedings of the 17th International Conference, PPSN 2022, Dortmund, Germany, 10/9/2022–14/9/2022
Grants (last 5 years)
- Logistics strategies to enhance landforce mobilisation and littoral operations to protect northern Australia from the security threats in the Indo-Pacific [Army Research Scheme 2023]. Funded by: Department of Defence Contract from (2024 to 2025)
Supervision
- 1 PhD Current Supervisions