The placement of certain facilities, such as radars and wind turbines, requires careful planning according to very specific geographical and spatial requirements. Such placement problems are often solved by metaheuristics which find near-optimal solutions within a fraction of the time required to solve these problems exactly. The use of high-resolution representations of the feasible search space generally ensures a high level of solution quality and accuracy, but involves evaluation of a larger number of candidate solutions than lower resolution representations, and is therefore more time-consuming. A trade-off between solution quality and time requirements must therefore be achieved when choosing an appropriate resolution of data to include in geospatial facility location models. In this paper, we propose a novel explore-and-exploit, multi-resolution solution approach that takes advantage of the reduced computational requirements associated with lower resolution terrain data, while simultaneously benefitting from the quality of solutions returned at higher resolutions. Our multi-resolution approach is capable of outperforming analyses in which only highest resolution data are considered, both in terms of solution quality and solution time requirements.
|Peer-reviewed scientific journal||Computers, Environment and Urban Systems|
|Publication status||Published - 2016|
|MoE publication type||A1 Journal article - refereed|
- 113 Computer and information sciences
- facility location
- multi-objective optimisation
- evolutionary algorithms