Most practically relevant discrete optimisation problems are NP-hard, which means that their worst-case solution times grow exponentially with the problem size. The practical consequence of this is that for large and challenging instances, even state-of-the-art optimisation software will frequently fail to deliver a solution within weeks or even hundreds of years of computational time.
Over the last decades, there has been an impressive development of methods for solving discrete optimisation problems. Thanks to this, many important planning and scheduling problems can be solved with a reasonable computational effort – however, many practically relevant problems still pose great challenges.
In our research projects, we both find new ways to address decision problems by optimisation and develop new methods for solving them. Some of our applied projects and PhD thesis projects are highlighted in the list of research projects below.
This research direction has been established through funding from the Center for Industrial Information Technology (CENIIT) and is carried out at the Division of Applied Mathematics (TIMA) in the Department of Mathematics (MAI). Project leader is Elina Rönnberg.