Reduced target-resolution strategy for rapid multi-observer site location optimisation

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Sammanfattning

Covering location problems aim to find optimal site locations for the placement of systems of facilities, e.g. cellular transmitters, surveillance sensors, military equipment, and weather radar. The aim is to maximise system coverage over demand regions that are modelled as demand points (targets), which are discretised representations of the terrain surface contained within specified geographical boundaries. Covering problems are computationally difficult to solve and determining globally optimal solutions is typically not possible within realistic computation times. Reducing the number of targets is one strategy that can be considered to reduce computational complexity, but preceding research into this approach has been limited to modestly sized study areas, outdated facility specifications, and simplified and impractical modelling approaches without consideration of topography – the practical and computational challenges associated with solving modern facility location problems have been overlooked. A reduced target-resolution strategy is investigated in this paper to solve large, real-world facility location problems with requirements beyond those typically encountered in the literature. Drastic reductions in optimisation computation times are achieved, while improving on the solution quality of previous best efforts. The strategy offers a simple and easy-to-replicate process and does not require any elaborate site/demand abstraction processes or heuristics, and may be beneficial to various modern site-selection problems – particularly in environments in which rapid decision-making is required, and when the problem instance is outside the bounds of tractability for global optimisation.
OriginalspråkEngelska
Referentgranskad vetenskaplig tidskriftIEEE Access
Volym8
Sidor (från-till)203252-203269
Antal sidor18
ISSN2169-3536
DOI
StatusPublicerad - 10.11.2020
MoE-publikationstypA1 Originalartikel i en vetenskaplig tidskrift

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  • 512 Företagsekonomi
  • 113 Data- och informationsvetenskap

Styrkeområden och områden med hög potential (AoS och AoHP)

  • AoHP: Humanitär och samhällslogistik

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