Solving an optimisation problem is about finding solutions that satisfy constraints, and one is often interested in best solutions. A solution might be an allocation of resources (say a personnel roster, with work regulations and employee preferences as constraints), a packing (say of containers), a plan, a set of routes (say of vehicles in logistics, or of dataflows in a communication network), a schedule (say a school timetable), or energy usage (say for the charging of electric buses).
The challenge is to find good solutions fast. Our research focuses on identifying new and efficient optimisation models and methods, often driven by real-world applications.
- Constraint programming (CP) is an AI approach to optimisation: modelling languages, high-level constraints, high-level types for decision variables, symmetry breaking
- Local search (LS): modelling languages, search languages, solver design, autonomous search
- Mathematical optimisation (MP): efficient mathematical modelling, linear programming (LP), mixed integer (linear) programming (MIP)
- Propositional satisfiability (SAT) and SAT modulo theories (SMT): trustworthy and verified solvers, proofs and certificates, competitions and evaluations
- Surrogate-based optimisation and Bayesian optimisation (SBO): scalable acquisition functions/sampling algorithms, multi-objective and constrained black-box optimisation, optimisation of multi-fidelity (simulation-based) objective functions, evolutionary optimisation, etc
- Applied optimisation: air traffic management; resource allocation in networks and mobile communications; cutting patterns for sawmills; software testing, analysis, and verification; vehicle routing for waste management; vehicle routing for winter road maintenance; charging of electric buses; expectation-maximisation (EM) for hidden Markov models, ...
- Optimisation research group: CP, LS, MP, applications
- Pierre Flener (also see his homepage): CP, LS, applications
- Maria Andreina Francisco Rodriguez (also see her homepage): CP, applications
- Carl Nettelblad (also see his homepage): EM, SAT, applications
- Justin Pearson (also see his homepage): CP, LS, applications
- Prashant Singh: SBO, applications
- Tjark Weber (also see his homepage): SAT, SMT
- Di Yuan: MP, applications
- 1DL442: Combinatorial Optimisation and Constraint Programming (slides): CP, LS
- 1DL451: Modelling for Combinatorial Optimisation (slides): CP, LS, MP, SAT, SMT
- 1DL481: Algorithms and Data Structures 3: LS, MP, SAT, SMT
- 1TD184: [Continuous] Optimisation: MP
- 1RT242: Applied Systems Analysis: LP, MIP
- 1RT316: Systems Analysis and Operations Research: LP, MIP
- Numerical Optimisation (third cycle only): PDE-constrained optimisation, multi-objective and model-based optimisation