Church, Richard L., Scaparra, Maria Paola (2007) Protecting critical assets: The r-interdiction median problem with fortification. Geographical Analysis, 39 (2). pp. 129-146. ISSN 0016-7363. (doi:10.1111/j.1538-4632.2007.00698.x) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:2177)
The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. | |
Official URL: http://dx.doi.org/10.1111/j.1538-4632.2007.00698.x |
Abstract
Many systems contain bottlenecks, critical linkages, and key facilities. Such components, when lost due to a man-made or natural disaster, may imperil a system in performing its intended function. This article focuses on reducing the impact of an intentional strike against a supply system where supply facilities can be fortified in order to prevent such events. It is assumed that fortification resources are limited and must be used in the most efficient manner. In a recent article, Church, Scaparra, and Middleton (2004) introduced the r-interdiction median problem, which can be used to identify the most important facilities in a supply system. In this article, we extend that model to address the option of fortifying such sites against possible interdiction. We present a new integer-linear programming model that optimally allocates fortification resources in order to minimize the impact of interdiction. Computational results are presented in using this model for several hypothetical problems. We also discuss the general properties of fortification and demonstrate that the presence of fortification can impact which system elements are considered critical.
Item Type: | Article |
---|---|
DOI/Identification number: | 10.1111/j.1538-4632.2007.00698.x |
Subjects: |
G Geography. Anthropology. Recreation > G Geography (General) H Social Sciences > HD Industries. Land use. Labor > HD29 Operational Research - Applications |
Divisions: | Divisions > Kent Business School - Division > Department of Analytics, Operations and Systems |
Depositing User: | Louise Dorman |
Date Deposited: | 19 Dec 2007 19:31 UTC |
Last Modified: | 19 Sep 2023 15:04 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/2177 (The current URI for this page, for reference purposes) |
- Export to:
- RefWorks
- EPrints3 XML
- BibTeX
- CSV
- Depositors only (login required):