A Novel Space Partitioning Algorithm to Improve Current Practices in Facility Placement Metadata

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Title

  • Main Title A Novel Space Partitioning Algorithm to Improve Current Practices in Facility Placement

Creator

  • Author: Jimenez, Tamara
    Creator Type: Personal
    Creator Info: University of North Texas
  • Author: Mikler, Armin R.
    Creator Type: Personal
    Creator Info: University of North Texas
  • Author: Tiwari, Chetan
    Creator Type: Personal
    Creator Info: University of North Texas

Publisher

  • Name: Institute of Electrical and Electronics Engineers
    Place of Publication: [New York, New York]

Date

  • Creation: 2011-03

Language

  • English

Description

  • Content Description: This article discusses a novel space partitioning algorithm to improve current practices in facility placement.
  • Physical Description: 15 p.

Subject

  • Keyword: epidemics
  • Keyword: pandemics
  • Keyword: biological threats
  • Keyword: public health preparedness
  • Keyword: response analysis
  • Keyword: optimization
  • Keyword: algorithm
  • Keyword: algorithmic optimization

Source

  • Journal: IEEE Transactions on System, Man, And Cybernetics Part A, 2011, New York: Institute of Electrical and Electronics Engineers

Citation

  • Publication Title: IEEE Transactions on System, Man, and Cybernetics Part A
  • Volume: 42
  • Issue: 5
  • Page Start: 1194
  • Page End: 1205
  • Peer Reviewed: True

Collection

  • Name: UNT Scholarly Works
    Code: UNTSW

Institution

  • Name: UNT College of Engineering
    Code: UNTCOE

Rights

  • Rights Access: public

Resource Type

  • Article

Format

  • Text

Identifier

  • Archival Resource Key: ark:/67531/metadc132975

Degree

  • Academic Department: Computer Science and Engineering
  • Academic Department: Center of Computational Epidemiology and Response Analysis

Note

  • Display Note: © 2011 IEEE. Reprinted, with permission, from [Tamara Jimenez, Armin R. Mikler, and Chetan Tiwari, A Novel Space Partitioning Algorithm to Improve Current Practices in Facility Placement, IEEE Transactions on System, Man, and Cybernetics Part A, March 2011]. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of North Texas' products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.
  • Display Note: Abstract: In the presence of naturally occurring and man-made public health threats, the feasibility of regional bio-emergency contingency plans plays a crucial role in the mitigation of such emergencies. While the analysis of in-place response scenarios provides a measure of quality for a given plan, it involves human judgement to identify improvements in plans that are otherwise likely to fail. Since resource constraints and government mandates limit the availability of service provided in case of an emergency, computational techniques can determine optimal locations for providing emergency response assuming that the uniform distribution of demand across homogeneous resources will yield and optimal service outcome. This paper presents an algorithm that recursively partitions the geographic space into sub-regions while equally distributing the population across the partitions. For this method, the authors have proven the existence of an upper bound on the deviation from the optimal population size for sub-regions.