Research Proposal: Meta-heuristics in Robust Optimisation
Lead Research Organisation:
Lancaster University
Department Name: Management Science
Abstract
Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.
Organisations
People |
ORCID iD |
| Marc Goerigk (Primary Supervisor) | |
| Martin Hughes (Student) |
Publications
Goerigk M
(2018)
Representative scenario construction and preprocessing for robust combinatorial optimization problems
in Optimization Letters
Hughes M
(2020)
Particle swarm metaheuristics for robust optimisation with implementation uncertainty
in Computers & Operations Research
Hughes M
(2019)
A Largest Empty Hypersphere Metaheuristic for Robust Optimisation with Implementation Uncertainty
in Computers and Operations Research
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| EP/N509504/1 | 30/09/2016 | 29/09/2021 | |||
| 1767326 | Studentship | EP/N509504/1 | 30/09/2016 | 30/03/2020 | Martin Hughes |
| Description | We have identified improved approaches for performing optimisation searches on black-box problems (e.g. simulation models) which are expensive to run (thereby limiting the number of models runs that can be undertaken), under uncertainty. Here implementation undertainty only is considered, in a robust - as opposed to a stochastic - setting. |
| Exploitation Route | Robust optimisation on expensive to run black-box problems is both a difficult undertaking and one that has not been widely considered, therefore the development of improving metaheuristics should be seen as an ongoing process. |
| Sectors | Aerospace Defence and Marine Energy Manufacturing including Industrial Biotechology Transport |