The University of Southampton
University of Southampton Institutional Repository

Computational comparison of five maximal covering models for locating ambulances

Record type: Article

This article categorizes existing maximum coverage optimization models for locating ambulances based on whether the models incorporate uncertainty about (1) ambulance availability and (2) response times. Data from Edmonton, Alberta, Canada are used to test five different models, using the approximate hypercube model to compare solution quality between models. The basic maximum covering model, which ignores these two sources of uncertainty, generates solutions that perform far worse than those generated by more sophisticated models. For a specified number of ambulances, a model that incorporates both sources of uncertainty generates a configuration that covers up to 26% more of the demand than the configuration produced by the basic model

Full text not available from this repository.

Citation

Erkut, Erhan, Ingolfsson, Armann, Sim, Thaddeus and Erdogan, Gunes (2009) Computational comparison of five maximal covering models for locating ambulances [in special issue: In Honor of and in Memory of Charles S. ReVelle edited by Mark S. Daskin, John R. Current, and Richard L. Church] Geographical Analysis, 41, (1), pp. 43-65. (doi:10.1111/j.1538-4632.2009.00747.x).

More information

e-pub ahead of print date: 4 January 2009
Organisations: Centre of Excellence for International Banking, Finance & Accounting

Identifiers

Local EPrints ID: 204813
URI: http://eprints.soton.ac.uk/id/eprint/204813
ISSN: 0016-7363
PURE UUID: 7e0a25c6-39b4-4965-8e85-363c20ec38ea

Catalogue record

Date deposited: 02 Dec 2011 09:04
Last modified: 18 Jul 2017 11:05

Export record

Altmetrics

Contributors

Author: Erhan Erkut
Author: Armann Ingolfsson
Author: Thaddeus Sim
Author: Gunes Erdogan

University divisions


Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×