A Spatial Modeling Framework for Siting Critical Supply Infrastructures

Abstract

Since the launch of the U.S. Department of Homeland Security in 2002, research needs have been established in the areas of disaster preparedness and critical infrastructure protection. Disaster preparedness seeks to lessen the adverse effects of catastrophes by planning in advance and instituting a proper response. Critical infrastructures are those entities deemed necessary for society to function correctly and thus require protection. Recent studies have used location models to aid in the management of many of these crucial establishments, but few efforts have modeled where to geographically site a future critical supply facility. Furthermore, no research has explored a multihazards approach for siting critical supply facilities and the explicitly spatial factors that make a potential target vulnerable to attack. In this article, we propose a strategic multiobjective modeling framework for siting critical supply facilities that incorporates explicitly spatial disaster preparedness directives and critical infrastructure protection demands. We detail the spatial data requirements and modeling assumptions and present results from an application involving the placement of critical vaccine stores in Orlando, Florida. Key Words: critical infrastructure protection, critical supplies, disaster preparedness, facility location, vulnerability.Desde el lanzamiento del Departamento de Seguridad Nacional de los EE.UU. en 2002, se han establecido las necesidades de investigación en los campos de la preparación para desastres y protección de la infraestructura crítica. Con la preparación para desastres se busca aminorar los efectos adversos de las catástrofes, planeando con anticipación y determinando las respuestas apropiadas. Las infraestructuras críticas son aquellas entidades que se consideran necesarias para que la sociedad funcione correctamente y que por ello mismo requieren protección. En estudios recientes se han aplicado modelos locacionales para ayudar a manejar muchos de estas instalaciones cruciales, aunque pocos han sido los esfuerzos hechos para modelar la ubicación geográfica de una futura instalación crítica de abastecimiento. Además, hasta ahora en ninguna investigación se exploró un enfoque de multi-riesgo para ubicar ese tipo de instalaciones críticas, ni se estudiaron los factores explícitamente * The authors thank the editor and anonymous reviewers for comments that helped to improve the article. espaciales que puedan hacer vulnerable al ataque un objetivo potencial. En este artículo proponemos un marco de modelado estratégico multi-objetivos para situar instalaciones críticas de abastecimiento, en el cual se incorporan directivas explícitamente espaciales para la preparación contra desastres, lo mismo que demandas críticas para la protección de infraestructura. En el artículo detallamos los datos espaciales requeridos y los supuestos para la construcción del modelo, y presentamos los resultados logrados en una aplicación relacionada con el emplazamiento de tiendas de vacunas críticas en Orlando, Florida....

Multiobjective Optimization Methods

“…Several GIS-MODA applications have used the weighting method (e.g., Church et al 1992;Kao and Lin 1996;Wu and Murray 2005;Farhan and Murray 2008;Herzig 2008;Ligmann-Zielinska and Jankowski 2010;Maliszewski and Horner 2010;Maliszewski et al 2012). Church et al (1992) integrated the weighting method into a raster based GIS for generating and exploring spatial alternatives for a corridor location problem.…”

The Analytic Hierarchy Process in GIS-Driven Military Operation Base Selection: A Case Study in Sri Lanka

“…This research presents a case study of a location analysis procedure that is an integral part of military intelligence operations on the battlefield during the base selection stage. Site selection using GIS is a well-established use of geospatial data [5,[14][15][16][17]. Armed forces use GIS in cartography, combat area management, military deployment, intelligence, and terrain analysis, allowing decision makers and commanders to obtain useful data and information.…”

Risk-based optimization of emergency rescue facilities locations for large-scale environmental accidents to improve urban public safety

“…At present, the approaches proposed for solving large-scale ERFLs problem are generally exact approaches, such as linear, nonlinear or other mathematical programming methods (Jia et al 2007a;Maliszewski and Horner 2010;Horner and Downs 2010;Maliszewski et al 2012;Bell et al 2011). Unfortunately, there are practical limitations associated with the use of exact methods in the large-scale ERFLs problem, as it is actually a geographical optimization problem.…”

“…These actions are often implemented by setting up a number of emergency rescue facilities (Jia et al 2007a, b;Georgiadou et al 2010;Horner and Downs 2010). Locations of these emergency rescue facilities are crucial for ensuring the efficiency of emergency relief distribution and casualty transportation (FEMA 2008;Horner and Downs 2010;Maliszewski and Horner 2010;Caunhye et al 2012;Maliszewski et al 2012). Although these emergency rescue facilities play a role in the emergency response phase after disasters occurrence, the planning of their locations should be carried out in the emergency preparedness phase before disasters occur.…”

“…Research on facility location problem is abundant, and many decision models have been developed to solve various facility location problems, including the ones in the context of regular emergency services (e.g., fire stations, medical centers, etc.) (Chrissis 1980;Revelle and Snyder 1995;Liu et al 2006;Alçada-Almeida et al 2009;Indriasari et al 2010;Maliszewski and Horner 2010), as well as the ones for large-scale disasters emergency services (e.g., earthquakes, floods, hurricanes, etc.) (Jia et al 2007a(Jia et al , 2007bHorner and Downs 2010;Bell et al 2011;Caunhye et al 2012;Maliszewski et al 2012).…”