Salhi, Said and Brimberg, Jack (2019) Neighbourhood Reduction in Global and Combinatorial Optimization: The Case of the p-Centre Problem. In: Eiselt, H. A. and Marianov, Vladimir, eds. Contributions to Location Analysis. International Series in Operations Research & Management Science . Springer, pp. 195-220. ISBN 978-3-030-19110-8. (doi:10.1007/978-3-030-19111-5_8) (KAR id:77583)
PDF
Author's Accepted Manuscript
Language: English |
|
Download this file (PDF/1MB) |
![]() |
Request a format suitable for use with assistive technology e.g. a screenreader | |
Official URL: https://doi.org/10.1007/978-3-030-19111-5_8 |
Abstract
Neighbourhood reductions for a class of location problems known as the vertex (or discrete) and planar (or continuous) p-centre problems are presented. A brief review of these two forms of the p-centre problem is first provided followed by those respective reduction schemes that have shown to be promising. These reduction schemes have the power of transforming optimal or near optimal methods such as metaheuristics or relaxation-based procedures, which were considered relatively slow, into efficient and exciting ones that are now able to find optimal solutions or tight lower/upper bounds for larger instances. Research highlights of neighbourhood reduction for global and combinatorial optimisation problems in general and for related location problems in particular are also given.
Item Type: | Book section |
---|---|
DOI/Identification number: | 10.1007/978-3-030-19111-5_8 |
Uncontrolled keywords: | Neighbourhood reduction, p-centre problem, Continuous and discrete spaces, Heuristic search, Optimal methods |
Divisions: | Divisions > Kent Business School - Division > Department of Analytics, Operations and Systems |
Depositing User: | Said Salhi |
Date Deposited: | 18 Oct 2019 11:30 UTC |
Last Modified: | 19 Sep 2023 15:03 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/77583 (The current URI for this page, for reference purposes) |
- Link to SensusAccess
- Export to:
- RefWorks
- EPrints3 XML
- BibTeX
- CSV
- Depositors only (login required):