Scheduling and combinatorial optimization is an important research area with many real-world applications such as supply chain, smart manufacturing, and cloud computing. This domain has attracted great attention of researchers and practitioners, from the traditional operations research, optimization, artificial intelligence, and evolutionary computation communities. Evolutionary Computation (EC) and other Computational Intelligence (CI) techniques, such as reinforcement learning and neural networks, are suitable for these problems since they are highly flexible regarding handling constraints, dynamic changes, and multiple conflicting objectives. With the growth of new technologies and business models, researchers in these fields must continuously face new challenges, which required innovated solution methods.
This special session focuses on both practical and theoretical aspects of EC and other CI techniques for Scheduling and Combinatorial Optimization (ECCISCO). Examples of methods include genetic algorithm, genetic programming, evolutionary strategies, ant colony optimization, particle swarm optimization, hyper-heuristics, memetic algorithms, and neural network-based reinforcement learning algorithms. Novel hybrid approaches that combine machine learning and optimization techniques to solve difficult scheduling and combinatorial optimization problems are highly encouraged. Examples include using machine learning to improve surrogate-assisted evolutionary algorithms, designing neural network reinforcement learning algorithms to automatically design scheduling and combinatorial optimization solvers/heuristics, and designing evolutionary algorithms for enhancing reinforcement learning and transfer learning.
We welcome the submissions of quality papers that effectively use the power of EC and other CI techniques to solve hard and practical scheduling and combinatorial optimization problems. Papers with rigorous analyses of EC and other CI techniques and innovative solutions to handle challenging issues in scheduling and combinatorial optimization problems are also highly encouraged.
Topics of interest include, but not limited to:
Please follow the submission guideline from the IEEE WCCI 2024 Submission Website. Special session papers are treated the same as regular conference papers. Please specify that your paper is for the Special Session on Evolutionary Computation and Computational Intelligence for Scheduling and Combinatorial Optimization. All papers accepted and presented at WCCI2024 will be included in the conference proceedings published by IEEE Explore, which are typically indexed by EI.
A/Prof. Yi Mei, Victoria University of Wellington, New Zealand (yi.mei@ecs.vuw.ac.nz)
Dr. Yi Mei is an Associate Professor at the School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand. He received his BSc and PhD degrees from University of Science and Technology of China in 2005 and 2010, respectively. Yi has more than 200 fully referred publications, including the top journals, such as IEEE Transactions on Evolutionary Computation, IEEE Transactions on Cybernetics, European Journal of Operational Research, ACM Transactions on Mathematical Software, and received Best Paper Awards on top/major EC conferences (GECCO and EuroGP). He is the Chair of the IEEE Taskforce on Evolutionary Scheduling and Combinatorial Optimization, and the Chair of IEEE New Zealand Central Section. He was a Vice-Chair of the IEEE CIS Emergent Technologies Technical Committee, and a member of three IEEE CIS Task Forces and two IEEE CIS Technical Committees. He is and Associate Editor of the IEEE Transactions on Evolutionary Computation, an Editorial Board Member/Associate Editor of other four international journals. He has organised a number of special sessions in international conferences such as IEEE CEC and SSCI. He serves as a reviewer of over 50 international journals including the top journals in EC and OR. He was an Outstanding Reviewer for Applied Soft Computing in 2015 and 2017, and IEEE Transactions on Cybernetics in 2018. He is a Fellow of Engineering New Zealand and an IEEE Senior Member.
Dr. Su Nguyen, RMIT University, Australia (su.nguyen@rmit.edu.au)
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).
Dr. Gang (Aaron) Chen, Victoria University of Wellington, New Zealand (aaron.chen@ecs.vuw.ac.nz)
Dr Gang (Aaron) Chen is currently a Senior Lecturer in the School of Engineering and Computer Science at Victoria University of Wellington (VUW). He is co-leading the strategic research direction on Evolutionary Scheduling and Combinatorial Optimization of the Evolutionary Computation Research Group at VUW. Dr Chen’s primary research interests include reinforcement learning, multi-agent system, evolutionary computation, and cloud and service computing. He has more than 160 fully referred publications, including top journal and conference publications, such as IEEE and ACM Transactions. Dr Chen is a senior member of IEEE and the IEEE Computational Intelligence Society. He served as the programme committee members and senior members of more than 80 international conferences, including top AI and EC conferences such as IJCAI, ICML, NeurIPS, ILCR, AAAI, ICRA and GECCO. He served as the guest editor of a special issue on "Evolutionary Optimisation, Feature Reduction and Learning" in the Soft Computing journal. He is also a regular reviewer of many internationally reputed journals in the research field of evolutionary computation, machine learning, and distributed computing.
Dr. Fangfang Zhang, Victoria University of Wellington, New Zealand (fangfang.zhang@ecs.vuw.ac.nz)
Dr. Fangfang Zhang is currently a Research Fellow in Artificial Intelligence with the School of Engineering and Computer Science, Victoria University of Wellington. She received the B.Sc. and M.Sc. degrees from Shenzhen University, Shenzhen, China, and the Ph.D. degree in Computer Science from Victoria University of Wellington, New Zealand, in 2014 and 2017, and 2021, respectively. She has over 55 journal and conference papers. Her current research interests include evolutionary computation, hyper-heuristic learning/optimisation, job shop scheduling, surrogate and multitask learning. Dr. Zhang is a member of the IEEE Computational Intelligence Society and Association for Computing Machinery, and has been severing as reviewers for top international journals such as the IEEE Transactions on Evolutionary Computation and the IEEE Transactions on Cybernetics, and conferences including the Genetic and Evolutionary Computation Conference and the IEEE Congress on Evolutionary Computation. She is an Associate Editor of Expert Systems with Applications. She is a Vice-Chair of the IEEE Task Force on Evolutionary Scheduling and Combinatorial Optimisation. She is the Secretary of IEEE New Zealand Central Section, treasurer of Young Professional Affinity Group, and the Secretary of Women-in-Engineering Affinity Group of IEEE New Zealand Central Section. She was the Chair of the IEEE Student Branch at VUW, the chair of Professional Activities Coordinator. In addition, she is the diversity chair and Post-Doc member representative of AI Researchers Association in New Zealand.