Scaparra, Maria Paola, Church, Richard L. (2012) Protecting supply systems to mitigate potential disaster: a model to fortify capacitated facilities. International Regional Science Review, 35 (2). pp. 188-210. ISSN 0160-0176. (doi:10.1177/0160017611435357) (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:29485)
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/10.1177/0160017611435357 |
Abstract
Planning to mitigate the impacts of a disaster can be an important activity for both private companies and public agencies. In this article, the authors consider a supply system that provides needed goods or services to a region that may be the subject of some type of disaster, such as an attack by a terrorist or the result of a natural event or accident. The supply system is represented by a set of existing capacitated facilities. The authors assume that the loss of one or more facilities to a disaster will tighten available supply and increase the distances over which the service or good must be delivered, thereby increasing operation costs and reducing service. Such a disaster may even reduce the capacity of the supply/storage to the extent that the goods must be rationed as remaining supply may be outstripped by demand. The authors consider the case where resources may be available to mitigate some of the impacts of a possible disaster by the advanced protection of one or more facilities. The authors show how this problem can be formulated as a “tri-level” optimization model and propose a solution approach based on a tree search strategy. The authors demonstrate the policy implications of this model using a hypothetical planning problem. Through this example, the authors show how the results of our model can be used to inform planners and policy makers in disaster mitigation planning.
Item Type: | Article |
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DOI/Identification number: | 10.1177/0160017611435357 |
Subjects: | Q Science > Operations Research - Theory |
Divisions: | Divisions > Kent Business School - Division > Department of Analytics, Operations and Systems |
Depositing User: | Paola Scaparra |
Date Deposited: | 16 May 2012 12:39 UTC |
Last Modified: | 05 Nov 2024 10:11 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/29485 (The current URI for this page, for reference purposes) |
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