An Efficient Approach for Solving Hub Location Problems Using Network Autocorrelation Structures.
The properties of spatial information have been shown to aid in identifying optimal solutions for location–allocation problems. Little effort, though, has been made to develop a spatially informed approach to solving hub location problems, as this class of problems entails a more complex model struc...
| Publicado en: | Annals of the American Association of Geographers Vol. 115; no. 6; pp. 1263 - 1286 |
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| Autores principales: | , , |
| Formato: | Artículo |
| Publicado: |
Taylor & Francis Ltd
2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=186912554&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 186912554 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 24694452 JRMH jtl: Annals of the American Association of Geographers issn: 24694452 maglogo: N pubinfo: dt: 2025 vid: 115 iid: 6 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 186912554 10.1080/24694452.2025.2482105 ppf: 1263 ppct: 23 formats: tig: atl: An Efficient Approach for Solving Hub Location Problems Using Network Autocorrelation Structures. aug: au: Oh, Changwha Kim, Hyun Chun, Yongwan affil: Department of Geography and Sustainability, University of Tennessee, Knoxville, USA School of Economic, Political and Policy Sciences, The University of Texas at Dallas, USA su: Location problems (Programming) sug: subj: Location problems (Programming) ab: The properties of spatial information have been shown to aid in identifying optimal solutions for location–allocation problems. Little effort, though, has been made to develop a spatially informed approach to solving hub location problems, as this class of problems entails a more complex model structure and greater challenges in terms of solving capability. To address this issue, this research proposes the spatially informed hub location problem (SI-HLP), derived from investigating the behavior of hub location problems in determining hubs and their allocations to nonhubs to achieve optimal solutions leveraged by underlying spatial characteristics among nodes, links, and routes. The performance of SI-HLP is achieved with two strategies to distinguish essential and nonessential decision variables for location and allocation decision variables, using an innovative convex-hull-based method, HUBI-COV, to capture nodes with high positive network autocorrelations and their allocated links. Simulation experiments under robustly designed settings were conducted to generalize the findings and assess the effectiveness of SI-HLP, indicating that SI-HLPs provide a novel avenue for advancing the solution of large-scale hub location problems. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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