Dr Simon Bowly
My main research interests are optimisation algorithms: their real-world applications and how they perform on a wide range of problems with different structures.
In algorithm performance analysis, I have a particular focus on linear and mixed-integer programming methods. These problem formulations can be applied to many optimisation problems in transport, power generation and distribution, and network design (to name a few). My work focuses on generating test cases which effectively stress-test optimisation algorithms and determine their relative strengths and weaknesses, enabling algorithm selection and automated configuration methodologies to improve algorithm performance.
My applied work is currently focused on analysing passenger and freight movement in South Sulawesi, Indonesia. We aim to improve understanding of the potential role of rail transport in the region by applying mixed integer programming and decomposition methods to solve logistics problems.
 Bowly, Simon. "A genetic algorithm hybridisation scheme for effective use of parallel workers". GECCO 2019 - Proceedings of the 2019 Genetic and Evolutionary Computation Conference: 2019 Genetic and Evolutionary Computation Conference, GECCO 2019; Prague; Czech Republic; 13 July 2019 through 17 July 2019. New York NY USA : Association for Computing Machinery (ACM), 2019. pp. 745-752 (GECCO 2019 - Proceedings of the 2019 Genetic and Evolutionary Computation Conference). https://doi.org/10.1145/3321707.3321877
 Bowly, Simon ; Smith-Miles, Kate ; Baatar, Davaatseren ; Mittelmann, Hans. "Generation techniques for linear programming instances with controllable properties". In: Mathematical Programming Computation. 2019. https://doi.org/10.1007/s12532-019-00170-6
 Smith-Miles, Kate Amanda ; Bowly, Simon. "Generating new test instances by evolving in instance space". In: Computers and Operations Research. 2015 ; Vol. 63. pp. 102-113. https://doi.org/10.1016/j.cor.2015.04.022