My research interests lie at the interface of artificial intelligence and operations research, on algorithm design and mathematical models for resource planning and scheduling problems in logistics, transportation, and related domains. Recently, I became interested in solving combinatorial optimisation problems with hybrid classical-quantum and quantum-inspired algorithms. A common thread running through my research is to go beyond publications to build novel software tools, a number of which have been field-tested and deployed in industry.
Website: http://www.mysmu.edu/faculty/hclau/
Selected publications:
- A Feasibility-Preserved Quantum Approximate Solver for the Capacitated Vehicle Routing Problem. In 27th International Conf. on Quantum Information Processing (QIP), Taipei, January 2024. (poster presentation) https://arxiv.org/abs/2308.08785
- Quantum Enhanced-Simulation Based Optimization for Newsvendor Problems. In 27th International Conf. on Quantum Information Processing (QIP), Taipei, January 2024. (poster presentation) https://arxiv.org/abs/2403.17389
- Techniques to Enhance a QUBO Solver For Permutation-Based Combinatorial Optimization. In Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO ’22). Association for Computing Machinery, New York, NY, USA, 2223–2231.
- QROSS: QUBO Relaxation Parameter Optimisation via Learning Solver Surrogates. In IEEE 41st International Conference on Distributed Computing Systems Workshops (ICDCSW), pages 35–40. 2021. https://arxiv.org/abs/2103.10695
- Quantum-inspired algorithm for Vehicle Sharing Problem. In IEEE Intl. Conf. on Quantum Computing and Engineering (QCE), October, 2021 (download PDF).
