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学术报告:Agent-based modelling, machine learning, and optimisation: Industry and service-related applications
            2019-11-06

时间20191115日(星期五)下午15:30-17:00

地点:信息馆401

(Dr Raymond Chiong,School of Computing and Electrical Engineering at the University of Newcastle, Australia)

Title:Agent-based modelling, machine learning, and optimisation: Industry and service-related applications

Abstract:

In this talk, I will discuss about my main research areas on the use of agent-based modelling, machine learning, and optimisation methods for industry and service-related applications. Specifically, I will first show how we employ agent-based modelling to study interactions in product sharing, using evolutionary game theory as the theoretical framework and sharing economy activities as an example. Next, I will discuss how machine learning and clustering models can be used for fault detection in the mining industry, based on an industry project of mine. Finally, I will describe how meta-heuristic optimisation algorithms can be used in the service industry, showing that our proposed algorithms can outperform other algorithms being compared by a large margin on dial-a ride problem instances. In addition, I will also talk about research activities carried out by my PhD students and ongoing projects I have with my international collaborators.

Bio:

Dr Raymond Chiongis a tenured academic staff member with the School of Computing and Electrical Engineering at the University of Newcastle, Australia. He is also a guest research professor with the Centre for Modern Information Management at Huazhong University of Science and Technology, Wuhan, China, and a visiting scholar with the Department of Automation, Tsinghua University, Beijing, China. His research focuses on the use of agent-based modelling, machine learning, and optimisation methods to understand and/or solve problems that cannot be easily tackled by the more traditional computational approaches. Specifically, he uses agent-based models to study the evolution of cooperation and trust; he uses machine learning methods for prediction and big data analytics; and he uses optimisation algorithms to solve large-scale production scheduling and transportation problems. He has published over 160 papers in these research areas, and is currently supervising a team of 14 PhD students. He is the Editor-in-Chief of the Journal of Systems and Information Technology (Emerald), an Editor of Engineering Applications of Artificial Intelligence (Elsevier), and an Associate Editor of the IEEE Computational Intelligence Magazine.