Optimisation and Logistics at the University of AdelaideWe research optimisation methods that are frequently used to solve hard and complex optimization problems. These include linear programming, branch and bound, genetic algorithms, evolution strategies, genetic programming, ant colony optimization, particle swarm optimization, local search, and other related approaches.
In our research related to real-world applications, we pay particular attention to constraint-handling techniques, multi-objectivity, and dynamic environments. These aspects are always present in large-scale industrial problems, in particular, they are important in integrated planning and scheduling decision-support systems which relate to supply-chain operations. Furthermore, we focus on the development of new algorithms for applications in the area of renewable energy and the use of optimisation methods in the area of sofware engineering.
In our theoretical research we analyze how heuristic methods work and show in a rigorous way how these algorithms are able to deal with different types of problems. The theoretical research aims to build up a theory of heuristic methods including evolutionary algorithms and ant colony optimisation that helps to develop new effective approaches based on theoretical insights. Furthermore, we investigate problems in the areas of mechanism design and social choice and develop new approaches for dealing with game theoretic problems.