Further Enquiries

School of Computer Science
Ingkarni Wardli Building
The University of Adelaide
SA 5005

Telephone: +61 8 8313 5586
Facsimile: +61 8 8313 4366

Optimisation and Logistics at the University of Adelaide

We 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.
The optimisation and logistics group has links to SolveIT Software – an Australian company specialising in advanced planning and scheduling, supply and demand optimisation, predictive modelling and mining solutions. Their mining software applications cover mining exploration management, mining logistics, and mine planning. Customers include Rio Tinto Iron Ore, Rio Tinto Simandou, Xstrata Coal, Xstrata Copper, Xstrata Zinc, BHP Billiton Iron Ore, BMA Coal, Fortescue Metals Group, and Pacific National Coal.


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. We regard evolutionary computing techniques as general problem solvers and want to understand how problems can be solved by general approaches that consist of the following three steps:

  • Choose a representation of possible solutions.
  • Determine a function to evaluate the quality of a solution.
  • Define operators that produces from a current set of solutions a new set of solutions.
Our research includes mathematical investigations that analyze the runtime of heuristics in a rigorous way. Furthermore, we use statistical approaches to understand the difficuly of interesting problems for certain classes of algorithms.