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

Description:

This article discusses a novel space partitioning algorithm to improve current practices in facility placement.

Creator(s):
Creation Date: March 2011
Partner(s):
UNT College of Engineering
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UNT Scholarly Works
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Creator (Author):
Jimenez, Tamara

University of North Texas

Creator (Author):
Mikler, Armin R.

University of North Texas

Creator (Author):
Tiwari, Chetan

University of North Texas

Publisher Info:
Place of Publication: [New York, New York]
Date(s):
  • Creation: March 2011
Description:

This article discusses a novel space partitioning algorithm to improve current practices in facility placement.

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© 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.

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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.

Physical Description:

15 p.

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Subject(s):
Keyword(s): epidemics | pandemics | biological threats | public health preparedness | response analysis | optimization | algorithm | algorithmic optimization
Source: IEEE Transactions on System, Man, And Cybernetics Part A, 2011, New York: Institute of Electrical and Electronics Engineers
Partner:
UNT College of Engineering
Collection:
UNT Scholarly Works
Identifier:
  • ARK: ark:/67531/metadc132975
Resource Type: Article
Format: Text
Rights:
Access: Public
Citation:
Publication Title: IEEE Transactions on System, Man, and Cybernetics Part A
Volume: 42
Issue: 5
Page Start: 1194
Page End: 1205
Peer Reviewed: Yes