Speaker: Ahmed Kheiri, Senior Lecturer, Lancaster University, UK
Date: 13 May 2024
Time: 9:00 - 10:00am (London Time, UTC+1) [Convert to your local time]
Zoom link: https://vuw.zoom.us/j/97526790339
Dr Ahmed Kheiri is a Senior Lecturer in Operations Research at Lancaster University Management School, Department of Management Science. Dr Kheiri is the Co-leader of the Fundamentals Theme of the Lancaster Intelligent, Robotic and Autonomous systems (LIRA) Research Centre. His research interests lie at the intersection of operational research and computer science, investigating general cross domain optimisation methods to solve NP-hard optimisation problems. He has published more than 50 refereed papers in reputable journals and highly respected international conferences. During his career, he received several academic awards some are awarded from participation in international optimisation challenges. He has co-chaired several workshops, edited several books, given many invited talks, organised several international conferences, reviewed numerous academic journal articles and has served on many program/technical committees in major international conferences.
In this webinar, Ahmed will discuss a real-world inventory routing problem with a focus on healthcare services delivering large volumes of liquid oxygen to large numbers of hospitals worldwide subject to a variety of constraints. The problem instances have been provided by AirLiquide, a French multinational company which supplies industrial gases and services to various industries. The goal is to assign delivery and loading shifts to match the demand requirements subject to a set of soft and hard constraints in order to minimise the total distribution cost and maximise the total quantity delivered over the planning horizon. He will describe the state-of-the-art selection hyper-heuristic method for solving this scheduling and routing problem, and describe the advantages of using data science techniques for the heuristic selection.
Speaker: Weiyao Meng, Data Scientist (KTP Associate), Nottingham University, UK
Date: 10 June 2024
Time: 9:00 - 10:00am (London Time, UTC+1) [Convert to your local time]
Zoom link: https://vuw.zoom.us/j/95757466815
Weiyao Meng is a Data Scientist (KTP Associate) at Nottingham University Business School, specialising in applying data science techniques to address challenges in sustainable food systems. Before taking on her role in the Knowledge Transfer Partnership (KTP) in 2024, Weiyao served as a Teaching Associate in the School of Computer Science at the University of Nottingham, where she completed her PhD in 2023. Her main research interests include machine learning into automated algorithm design, and data-driven decision-making for critical sustainability challenges, particularly for sectors such as Transportation and Food. She has also served as a reviewer for leading journals including IEEE Transactions on Evolutionary Computation, Engineering Applications of Artificial Intelligence, and Journal of the Operational Research Society. She is also a member of the IEEE Task Force on Automated Algorithm Design, Configuration and Selection and the IEEE Task Force on Evolutionary Scheduling and Combinatorial Optimisation at the IEEE Computational Intelligence Society.
Designing effective search algorithms for solving Combinatorial Optimisation Problems (COPs) presents a challenge for researchers due to the time-consuming experiments and experience required in decision-making. Automated algorithm design removes the heavy reliance on human experts and allows the exploration of new algorithm designs. This talk delves into the integration of machine learning techniques into the automated design of local search-based meta-heuristics, a main theme of my PhD research. I will discuss the key studies conducted during my PhD, focusing on the utilisation of machine learning techniques ranging from rule mining to classification, and sharing insights gained from the investigation at the intersection of machine learning and combinatorial optimisation. The proposed methodology is evaluated using the vehicle routing problem with time windows as a testbed.
Speaker: Yaoxin Wu, Assistant Professor, Eindhoven University of Technology, The Netherlands
Date: 8 July 2024
Time: 9:00 - 10:00am (Central European Summer Time, UTC+2) [Convert to your local time]
Zoom link: https://vuw.zoom.us/j/96385586267
Dr. Yaoxin Wu is an assistant professor in the Information Systems group at Eindhoven University of Technology. He received his Ph.D. degree in computer science from Nanyang Technological University, Singapore, in 2023. He was a Research Associate with the Singtel Cognitive and Artificial Intelligence Lab for Enterprises (SCALE@NTU). He has published research papers in leading journals (IEEE TNNLS, IEEE TKDE, IEEE TITS, IEEE TVT, etc.) and top-tier AI conferences (NeurIPS, ICML, ICLR, AAAI, UAI, AAMAS, etc.). In addition, he has served as PC member in NeurIPS, ICML, ICLR, AAAI, IJCAI, ECAI, etc, and Area Chair in IEEE Conference on Artificial Intelligence. He has also served as reviewer for prestigious journals in AI/OR fields, such as Transportation Research Part E, IEEE Transactions on Cybernetics, Annals of Operations Research, Transactions on Neural Networks and Learning Systems, etc. His research interests mainly include deep learning, combinatorial optimization, multi-objective optimization, and integer programming.
Existing deep reinforcement learning (DRL) methods for multi-objective vehicle routing problems (MOVRPs) typically decompose an MOVRP into subproblems with respective preferences and then train policies to solve corresponding subproblems. However, such a paradigm is still less effective in tackling the intricate interactions among subproblems, thus holding back the quality of the Pareto solutions. To counteract this limitation, we introduce a collaborative deep reinforcement learning method. We first propose a preference-based attention network (PAN) that allows the DRL agents to reason out solutions to subproblems in parallel, where a shared encoder learns the instance embedding and a decoder is tailored for each agent by preference intervention to construct respective solutions. Then, we design a collaborative active search (CAS) to further improve the solution quality, which updates only a part of the decoder parameters per instance during inference. In the CAS process, we also explicitly foster the interactions of neighboring DRL agents by imitation learning, empowering them to exchange insights of elite solutions to similar subproblems. Extensive results on random and benchmark instances verified the efficacy of PAN and CAS, which is particularly pronounced on the configurations (i.e., problem sizes or node distributions) beyond the training ones.
Speaker: Setyo Tri Windras Mara, The University of New South Wales, Australia
Date: 2 September 2024
Time: 6:00 - 7:00pm (AET, UTC+10) [Convert to your local time]
Zoom link: https://vuw.zoom.us/j/96161300622
Setyo Tri Windras Mara is a PhD student at the University of New South Wales, Australia. His broad research interests include operational research (OR), transportation science, evolutionary optimizations, and healthcare management. To this day, his works have been published in various widely respected publishing avenues at the intersection between OR and computational intelligence. He also serves as a reviewer in several top journals, such as Computers & Operations Research, Computers & Industrial Engineering, and Expert Systems with Applications. In addition, Setyo is the co-initiator and a board member of OPSID, a community of young Indonesian researchers in OR and operations management.
Driven by the pursuit of sustainability, the integration of modern transportation modes such as electric vehicles (EVs) and drones has been a rising trend in logistics. Assessing the real-life implementation of such innovative systems requires significant capital investments. Thus, modelling and optimization approaches are preferred to assist the decision-making in this area. Still, challenges occur as the arising optimization models from the coordination between EVs and drones are computationally expensive to solve in practical problem sizes. Therefore, in my PhD research, evolutionary approaches are consulted to handle these challenges. In this talk, the effective strategies and lessons learned from the implementation of evolutionary approaches for solving EV-drone logistics models will be discussed.
If you have any questions or queries, please email Yi Mei or Fangfang Zhang.
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