title: Single hub location-allocation problem under robustness clustering concept
metadata.name: dc.contributor.author
metadata.value: Lozkins, Aleksejs
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Bure, Vladimir M.
metadata.lang: -
metadata.name: dc.date.accessioned
metadata.value: 2018-01-31T09:08:00Z
metadata.lang: -
metadata.name: dc.date.available
metadata.value: 2018-01-31T09:08:00Z
metadata.lang: -
metadata.name: dc.date.issued
metadata.value: 2017-12
metadata.lang: -
metadata.name: dc.identifier.citation
metadata.value: Lozkins A., Bure V. M. Single hub location-allocation problem under robustness clustering concept. Vestnik of Saint Petersburg University. Applied Mathematics. Computer Science. Control Processes, 2017, vol. 13, iss. 4, pp. 398–406.
metadata.lang: en_GB
metadata.name: dc.identifier.other
metadata.value: 10.21638/11701/spbu10.2017.406
metadata.lang: -
metadata.name: dc.identifier.uri
metadata.value: http://hdl.handle.net/11701/9049
metadata.lang: -
metadata.name: dc.description.abstract
metadata.value: This paper presents an algorithm of hub number p robustness estimation using specific
  simulations in the single allocation hub location problem. The simulation has to model the
  service demand trends in each origin node to each destination node. This idea is based on the hub
  network dependence on service demand forecasting, which is modeled by random values from
  random distribution with parameters reflecting the demand changes. The algorithm includes
  the mixed integer programming model which describes the hub location-allocation problem with
  single allocation (each node is connected exactly to one hub). The model chooses the optimal
  locations for the fixed number of hubs p from the fixed possible location set in the problem.
  The perturbed data simulate the changes in the service need and present the perspectives
  of the network changes, and the algorithm fixes these changes. The number of changes in
  the network is consolidated into the variety frequencies which describe the variabilities in the
  set of simulations. The algorithm is implemented on Python 3.5 and model optimization is
  fulfilled using Gurobi Optimizer 7.0.1 software. The results in the real dataset are illustrated
  and discussed. Refs 18. Fig. 1. Tables 3.
metadata.lang: en_GB
metadata.name: dc.language.iso
metadata.value: en
metadata.lang: en_GB
metadata.name: dc.publisher
metadata.value: St Petersburg State University
metadata.lang: en_GB
metadata.name: dc.relation.ispartofseries
metadata.value: Vestnik of St Petersburg University. Applied Mathematics. Computer Science. Control Processes;Volume 13; Issue 4
metadata.lang: -
metadata.name: dc.subject
metadata.value: hub location-allocation
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: network stability
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: cluster number robustness
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: linear programming
metadata.lang: en_GB
metadata.name: dc.title
metadata.value: Single hub location-allocation problem under robustness clustering concept
metadata.lang: en_GB
metadata.name: dc.type
metadata.value: Article
metadata.lang: en_GB
resources.filename: 06-Lozkins.pdf
resources.url: https://dspace.spbu.ru/bitstream/11701/9049/1/06-Lozkins.pdf
resources.format: Adobe PDF
resources.size: 515,66 kB