| Prof. Wenyin GongDeputy Dean, School of Computer Science, China University of Geosciences (Wuhan), ChinaGong Wenyin, PhD, professor, and doctoral supervisor, is a recipient of the Hubei Province Outstanding Youth Fund, a senior member of IEEE, and a senior member of CCF. He earned his bachelor's, master's, and doctoral degrees from the School of Computer Science at China University of Geosciences (Wuhan) in 2004, 2007, and 2010, respectively. His main research areas are intelligent computing and its applications. He currently serves on the board of the China Society for Simulation, is a member of the ECOLE executive committee, and is on the editorial boards of international journals such as Swarm and Evolutionary Computation, Expert Systems with Applications, and Memetic Computing. He has led one National Key R&D Program project, four National Natural Science Foundation projects, and one Ministry of Education Doctoral Program Fund project. He has published over 160 papers in SCI journals, including 13 ESI highly cited papers, with more than 12,000 Google citations and an H-index of 63. He has authored two monographs and translated one book. He has received awards including the second prize of Hubei Provincial Natural Science Award twice, the second prize of Hubei Provincial Teaching Achievement Award, Hubei Outstanding Doctoral Dissertation Award, and Hubei Outstanding Master's Thesis Award. Speech Title: Evolutionary Multi-Task Optimization Abstract: By leveraging the similarities and connections between different tasks, we extract and transfer problem-solving knowledge from various optimization tasks to solve multiple optimization problems faster and better. Based on intelligent optimization algorithms in computational intelligence, we design suitable knowledge transfer techniques between tasks to speed up the solving of complex, NP-hard optimization problems. This report introduces the research team's recent work on evolutionary multitask optimization and looks ahead to future work. |
| Prof. Debiao LiFuzhou University, ChinaLi Debiao is a Professor in the Department of Management Science and Engineering at Fuzhou University and a PhD supervisor. He received his PhD from the Department of Systems Science and Industrial Engineering at Binghamton University, State University of New York (SUNY), USA, and completed postdoctoral research in Management Science and Engineering at Shanghai Jiao Tong University. His research focuses on "Operations Research and Management," specifically addressing operational challenges in the electronics manufacturing and healthcare industries. He has led over ten research projects, including General Program and Young Scientists Fund projects from the National Natural Science Foundation of China (NSFC), and the Distinguished Young Scholars Program of Fujian Provincial Natural Science Foundation. He has published more than 40 papers in prestigious journals and conferences such as POM, EJOR, IEEE TASE, Omega, IJPR. He has been invited to serve as Associate Editor for Computers in Industry (ABS 3-star) and Complex System Modeling and Simulation. His research achievements have been applied by enterprises including Samsung (Korea), The Home Depot (USA), Innovation Associates, TPV Technology, and Guangdong Pangu Technology, generating significant economic benefits. He has received honors including the Distinguished Young Scholars Program of Fujian Provincial Natural Science Foundation and the Young Talent Program of Fujian Province's "Hundred Talents Plan." Speech Title: Feature-aware Robust Scheduling for High-mix PCBA Manufacturing Abstract: Motivated by an industrial production project, this talk investigates scheduling challenges in printed circuit board assembly (PCBA), a critical process in high-mix electronic manufacturing. In practical production environments, processing and setup times are subject to uncertainties caused by machine variability, product diversity, and human intervention. We study a robust scheduling problem under such uncertainties and formulate it as an identical parallel machine scheduling problem.A feature-aware robust stochastic optimization framework is developed by incorporating production characteristics into scheduling decisions. Data-driven methods are employed to predict processing times, while setup-time uncertainties are characterized through event-based uncertainty sets constructed from production features. The resulting optimization model is integrated with a branch-and-price algorithm to efficiently solve large-scale industrial instances.Experiments based on real-world PCBA production data demonstrate that the proposed approach achieves superior scheduling performance compared with sample average approximation (SAA) and conventional distributionally robust optimization (DRO), while maintaining good scalability for practical applications. This talk will combine methodological developments with industrial practices to illustrate how data-driven modeling and robust optimization can support intelligent scheduling decisions in smart manufacturing. |
| Prof. Juanjuan PengZhejiang University of Finance & Economics, ChinaJuanjuan Peng is a Professor and Chongshi Scholar at Zhejiang University of Finance and Economics and a PhD Supervisor. Her main research interests include smart healthcare, intelligent decision-making methods, large-scale group decision-makings, multi-sided matching decision making, as well as data analysis and mining. In recent years, She has presided over more than ten research projects, including the National Natural Science Foundation of China, the National Social Science Fund of China, Major Cultivation Project for Leading Talents of Zhejiang Philosophy and Social Science Planning, Humanities and Social Sciences Foundation of the Ministry of Education of China, China Postdoctoral Science Foundation, and Zhejiang Provincial Natural Science Foundation of China, and so on. She has published over 70 papers in high-quality SCI/SSCI-indexed journals such as TEM, TM and INS. Among these publications, one paper was selected into the “Top 100 Most Influential International Academic Papers in China”, and 17 papers are ESI Hot Papers or Highly Cited Papers. She has been listed in Stanford University’s World’s Top 2% Scientists for five consecutive years. She has received numerous awards, including Hubei Provincial Humanities and Social Sciences Achievement Award, Hunan Provincial Natural Science Award, and so on. Speech Title: Group Consensus Decision‑Making Method Based on Personality Traits and Granular Balls Abstract: Although existing large-scale group decision-making (LSGDM) methods consider the behavioral differences among decision-makers, they seldom address the impacts of psychological characteristics reflected by personality traits, such as emotional stability and cognitive rigor, on decision-making behaviors. Therefore, this study integrates spherical picture fuzzy theory and proposes a group consensus decision-making method based on personality traits and granular ball clustering. First, a generation mechanism of spherical picture fuzzy numbers is constructed using large language models and personality traits to objectively quantify the cognitive volatility of decision-makers. Second, a dynamic granular ball clustering method based on spherical picture fuzzy distance is developed to effectively partition opinion subgroups. Meanwhile, combined with the diverse personality traits of decision-makers, a personality trait-based minimum adjustment feedback model is designed to implement differentiated guidance and opinion revision for different types of decision-makers. Furthermore, the evaluation and ranking of alternatives are realized using a classical decision ranking method. Finally, a case study is conducted in the context of treatment scheme selection for patients with chronic diseases. Experimental results demonstrate that the proposed method can efficiently reach group consensus with a small amount of adjustment. |
| Assoc. Prof. Kaizhou GaoMacau University of Science and Technology, ChinaKaizhou Gao is an Associate Professor, Program Director, and supervisor of Ph.D. at Macau University of Science and Technology. He obtained his Ph.D. from Nanyang Technological University, Singapore. He serves as an Associate Editor for several SCI journals, including IEEE Trans. ITS, SWEVO, ESWA, and CAEE. He has presided over 10 projects and published over 150 SCI papers in top-tier international journals, including IEEE Trans. TEVC, TCYB, SMC-System, TITS, et al. Among them, more than 10 papers are ESI Hot or Highly Cited Papers. He was listed in the World's Top 2% Scientists in 2021-2025. His academic honors include the Second Prizes of Shandong Provincial Natural Science Award in 2021 and 2023, the 2022 Norbert Wiener Review Award from IEEE/CAA Journal of Automatica Sinica, the Outstanding Paper Award from TSINGHUA Science and Technology, and the 2024 "Research Star" of Macau University of Science and Technology. He has organized over 10 special issues for international journals and special sessions at conferences and delivered 3 invited keynote speeches at international conferences. Speech Title: Scheduling of Unmanned/Autonomous Systems Under Ocean and Maritime Scenarios: Learning Enabled Intelligent Optimizers Abstract: This talk will start from introduce the scheduling problems of Unmanned/Autonomous Systems in ocean and maritime scenarios. Then, several applications will be explained in detail, including unmanned surface vehicles scheduling problems, and collaborative scheduling of unmanned surface vehicles and unmanned aerial vehicles. Further, concepts and principle of learning enabled intelligent optimizers will be explained. After that, a framework of the learning assisted swarm intelligence and evolutionary algorithms will be summarized as a methodology. Finally, some future directions will be supplied. |
