Homeland Security

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Vol. 36, No. 6, November–December 2006, pp. 514–529 issn 0092-2102 eissn 1526-551X 06 3606 0514


doi 10.1287/inte.1060.0253 © 2006 INFORMS

A Survey of Operations Research Models and Applications in Homeland Security
Department of Decision and Information Technologies, Villanova University, Villanova, Pennsylvania 19085 {[email protected], [email protected], [email protected]}

P. Daniel Wright, Matthew J. Liberatore, Robert L. Nydick

Operations research has had a long and distinguished history of work in emergency preparedness and response, airline security, transportation of hazardous materials, and threat and vulnerability analysis. Since the attacks of September 11, 2001 and the formation of the US Department of Homeland Security, these topics have been gathered under the broad umbrella of homeland security. In addition, other areas of OR applications in homeland security are evolving, such as border and port security, cyber security, and critical infrastructure protection. The opportunities for operations researchers to contribute to homeland security remain numerous. Key words: government: agencies; planning: government, homeland security. History: This paper was refereed.


ince September 11, 2001, the term homeland security has entered the vernacular of the United States and of countries around the world. In the US, it is defined as “a concerted national effort to prevent terrorist attacks within the United States, reduce America’s vulnerability to terrorism, and minimize the damage and recover from attacks that do occur” (Office of Homeland Security 2002, p. 2). Despite the recency of the term, for decades the operations research community has been exploring issues that we now classify under homeland security. As far back as 1960, OR researchers were working on such issues. At the seventh international meeting of the Institute of Management Sciences (TIMS), Wood (1961) highlighted US vulnerability and potential responses to nuclear attack. He called on the OR community to develop techniques and programs to maintain freedom. Since that time, operations researchers have focused on such topics as emergency preparedness and response, airline security, hazardous material transportation, and cyber security. All of these areas are increasingly important to homeland security. While they have done much, operations researchers still have rich opportunities available. The US Department of Homeland Security (DHS), formed in October 2001, has a broad set of responsibilities that contribute to securing the homeland. To

form it, the government reorganized several agencies and programs and evaluated its existing security efforts (National Commission on Terrorist Attacks upon the United States 2004). It put several existing agencies under one domain to unite their efforts to better protect the country. The department is organized into five directorates: border and transportation security, emergency preparedness and response, science and technology, information analysis and infrastructure protection, and management. Each directorate contains several agencies that were formerly housed in different departments of the federal government. For instance, the border and transportation security directorate now includes the US Customs Service, the Transportation Security Administration, and the Animal and Plant Health Inspection Service, which were originally the responsibility of the Treasury, Justice, and Agriculture Departments, respectively. For all of its directorates, the Department of Homeland Security states its mission as follows:
We will lead the unified national effort to secure America. We will prevent and deter terrorist attacks and protect against and respond to threats and hazards to the nation. We will ensure safe and secure borders, welcome lawful immigrants and visitors, and promote the free-flow of commerce (Department of Homeland Security 2005).

The mission is reinforced by several strategic goals, including awareness, prevention, protection, response,

Wright, Liberatore, and Nydick: Survey of Operations Research Models and Applications in Homeland Security
Interfaces 36(6), pp. 514–529, © 2006 INFORMS


recovery, service, and organizational excellence. Both the mission and the strategic goals of the DHS provide exciting opportunities for operations research.

Countermeasures portfolios Biological Chemical Reduce the probability and consequences of a biological attack Reduce the nation’s vulnerability to chemical attacks Develop and deploy techniques for detection of radiological materials Provide the concepts, technologies, systems analysis, and procedures to interdict terrorists’ use of explosives Prevent the entry of terrorists while ensuring efficient flow of traffic and commerce Develop tools to anticipate, identify, and assess the risks in the nation’s critical infrastructure Research, develop, test, and evaluate activities for improving cyber security Plan for, prevent, respond to, and recover from natural and man-made disasters and terrorism Evaluate extensive and diverse threat information

Literature Framework
Many research agendas contribute directly or indirectly to homeland security. Organizing the literature concerning homeland security is challenging. Research in homeland security falls under the science and technology directorate, which is the primary research-and-development arm of the DHS. It describes three main research areas that contribute to the state of the art in homeland security: (1) countermeasures portfolios, (2) component-support portfolios, and (3) cross-cutting portfolios. The science and technology directorate conducts and funds research in all of these portfolios. A few authors have reported on the impact of science and technology, including information technology, on terrorism response (Branscomb and Klausner 2003, Hennessy et al. 2003). The main purpose of the countermeasures portfolio is to protect the US from weapons of mass destruction. Research in this area concerns vulnerabilities and risks surrounding biological, chemical, radiological, and nuclear weapons, and high explosives. The research invites collaboration between operations researchers and physical scientists. The component-support portfolios focus on increasing the capabilities of DHS components and helping them to secure the homeland. The components include border and transportation security, critical infrastructure protection, cyber security, emergency preparedness and response, threat and vulnerability testing and assessment, the US Coast Guard, and the US Secret Service. In this portfolio, OR has the greatest history and perhaps the most potential to improve homeland security. The cross-cutting portfolios focus on other vulnerabilities and risks that extend across the countermeasures and component support portfolios. They include emerging threats, rapid prototyping, standards, and university programs. Although OR could contribute to cross-cutting, most previous work is better categorized in the other two portfolios. We focus on the first two portfolios because most OR-related research has fallen in those portfolios.

Radiological and nuclear High explosives

Component-support portfolios Border and transportation security Critical infrastructure protection Cyber security Emergency preparedness and response Threat analysis

Table 1: The countermeasures and component-support portfolios of the Department of Homeland Security research portfolios protect against weapons of mass destruction and support department components, respectively (adapted from www.dhs.gov).

In addition, they offer the greatest opportunity for contributions combining OR and homeland security (Table 1). We sought articles on OR and homeland security throughout the world published in academic journals. While some military research concerns homeland security, it does not fall under the US Department of Homeland Security and its research agenda. Jaiswal (1997) reviewed military OR models and techniques, and Miser (1998), Bonder (2002), and Hughes (2002) discussed the historical impact of OR on the military. Operations researchers have contributed in many ways to homeland security. We use the DHS science and technology framework to discuss previous work.

Countermeasures Portfolios
Countermeasure efforts address the risks of biological, chemical, radiological, and nuclear weapons, and high explosives. The OR community has studied problems in this area with notable results. Sullivan and Perry (2004) developed a useful framework for


Wright, Liberatore, and Nydick: Survey of Operations Research Models and Applications in Homeland Security
Interfaces 36(6), pp. 514–529, © 2006 INFORMS

categorizing terrorist groups’ development of chemical, biological, radiological, and nuclear weapons. They investigated three classification approaches, including a heuristic pattern-recognition method, classification trees, and discriminant analysis. Dyer et al. (1998) and Butler et al. (2005) addressed the problem of disposing of weapons-grade plutonium. Dyer et al. (1998) used multiple attribute utility theory (MAUT) to develop a hierarchy of objectives, to evaluate 13 alternatives, and to conduct sensitivity analyses. Butler et al. (2005) used MAUT to help the US and Russia to evaluate alternatives for disposing of stockpiles of weapons-grade plutonium. They recommended converting the plutonium for use as fuel in nuclear power plants. Munera et al. (1997) described the safety and security concerns posed by transporting highly enriched uranium used in nuclear reactors. They used stochastic dominance to evaluate the risks of road and air alternatives. Hupert et al. (2002) used discrete-event simulation to determine staffing levels for entry, triage, medical evaluation, and drug dispensing in a hypothetical distribution center under conditions of low, medium, and high bioterrorism attack. Craft et al. (2005) created a series of differential equations to determine the potential number of deaths from an aerosol bioterror attack. Their method included an atmospheric-release model, a spatial array of biosensors, a dose-response model, a disease-progression model, and an antibiotics model with a queue. Kaplan et al. (2002, 2003) also used differential equations to study response to a smallpox attack. Walden and Kaplan (2004) used a Bayesian approach to estimate the size and time of an anthrax attack to determine the number of persons who might require medical care. Wein et al. (2003) also modeled emergency response to an anthrax attack. Jenkins (2000) used integer programming to identify a small subset of oil spills that are similar to all potential categories of spills to predict the type of pollutant a terrorist group might use. Buckeridge et al. (2005) classified bioterrorism outbreak algorithms and found that spatial and other covariate information can improve measures for detecting and evaluating outbreaks. Stuart and Wilkening (2005) used first- and second-order catastrophic decay models to study the impact of degradation of biological-weapons agents leaked into the environment.

Border and Transportation Security
Border and transportation security problems have been and continue to be of great interest to operations researchers particularly because these types of problems are a good match for OR techniques. Within the DHS framework, border security includes improving the security of the nation’s borders to prevent the entry of terrorists, criminals, and illegal aliens while maintaining the safe flow of commerce and travelers. Transportation security includes the safety of airlines, railroads, ships, and trucks. Border Security Papers on border security are just beginning to appear. Wein and Baveja (2005) studied two programs: the US visitor program and the immigrant-status-indicatortechnology program. These two programs aim to reduce visa fraud and detect the entry of watch-listed criminals and suspected terrorists into the United States. Using a game-theoretic model, the authors show that the quality of fingerprint images is important to detection probability and thus system performance. They discussed fingerprint-scanning strategies that help counter terrorists’ attempts to minimize detection. Airline Security After the hijacking of commercial airlines that led to the catastrophic events of September 11th, 2001, the US Federal Aviation Administration and Transportation Security Administration tightened security measures at airports around the country. Barnett (2004) described a dynamic computer system that uses probability models and data-mining techniques to classify airline-passenger threats. However, many airline security issues still need attention (Turney et al. 2004). Coincidentally many OR researchers addressed airline security before 2001, focusing primarily on scanning passengers or baggage. Gilliam (1979) employed queuing theory for passenger screening. Kobza and Jacobson (1997) discussed the design of access security systems in airports. They developed performance measures based on the probabilities of false alarms and false clears that determine the effectiveness of single-device and multiple-device security systems. Jacobson et al. (2000) developed a sampling procedure that estimates false-alarm and false-clear probabilities.

Wright, Liberatore, and Nydick: Survey of Operations Research Models and Applications in Homeland Security
Interfaces 36(6), pp. 514–529, © 2006 INFORMS


Kobza and Jacobson (1996) studied security-system design by addressing the dependence between the responses of security devices in multiple-device systems. These articles could help managers to improve decisions on airport security systems and are somewhat generalizable to other types of security systems. Jacobson et al. (2001) described aviation security as a knapsack problem and proposed a model that determines how to minimize the false-alarm rate of a given security system. Barnett et al. (2001) conducted an experiment to evaluate the costs of bag-match strategies to the airlines and to the passengers in terms of monetary cost and passenger delay. Jacobson et al. (2003) discussed three important performance measures of baggage screening: the number of passengers on flights with unscanned bags, the number of flight segments with unscanned bags, and the total number of unscanned bags. Using examples based on real data, they showed how a greedy algorithm can minimize the performance measures. Jacobson et al. (2005) discussed the optimization of the first two measures through the allocation and utilization of screening devices. The cost of airline security concerns airports and commercial airlines. Candalino et al. (2004) discussed a software system for screening checked baggage that uses data on purchase and operating costs to allocate security devices around the country. They proposed an alternative cost function that includes indirect costs related to scanning errors. Virta et al. (2003) considered the direct and indirect costs of scanning policies based on the passengers selected by the screening software. Long customer waits are an important issue for airports who want to keep passengers happy and reduce congestion. Leone and Liu (2003) developed a simulation model that investigates passenger traffic and throughput rates for scanning devices. They discovered that the machines’ throughput rates were far lower than their advertised scan rates. As policies and scanning technologies change, operations researchers should find further opportunities in airline security. Port and Rail Security Currently, little operations research deals with the security of ports and railroads. Harrald et al. (2004)

identified US ports as vulnerable and as very attractive targets for terrorists. Lewis et al. (2003) formulated a shortest-path model for container-security operations at US seaports. They identified and analyzed trade-offs between the number of containers inspected and the costs of delayed vessel departures. The US railway system must be protected to prevent the sabotage of passenger or cargo trains and to prevent terrorists’ gaining control of hazardous shipments. Glickman and Rosenfield (1984) formulated models to evaluate the risks associated with train derailments and the release of hazardous materials, issues that could become important in the event of a terrorist attack. Truck Security The main issue for the trucking system is the transportation of hazardous materials. Many of the articles in the OR literature on transporting hazardous materials do not focus on homeland security, that is, preventing terrorists from hijacking these materials and using them in weapons. The literature focuses on two related issues: routing vehicles and analyzing risk. Routing hazardous vehicles involves determining what paths vehicles should take to minimize population exposure in the event of an accident. Many authors have developed algorithms and heuristics for solving various cases of the routing problem (Batta and Chiu 1986, 1988; Berman et al. 2000; Beroggi and Wallace 1994, 2005; Erkut and Ingolfsson 2000; Giannikos 1998; Jin et al. 1996; Karkazis and Boffey 1995; Lindner-Dutton et al. 1991; List and Turnquist 1998; van Steen 1987; Zografos and Androutsopoulos 2004). Related to the routing problem are methods for treating and analyzing risk (Erkut and Ingolfsson 2005, Erkut and Verter 1998, Gopalan et al. 1990, Kara et al. 2003, Raj and Pritchard 2000).

Critical Infrastructure Protection
To protect the critical infrastructure, analysts develop tools to anticipate, identify, and assess the risks in the nation’s critical infrastructure and attempt to reduce the risks and the consequences of an attack. Potential infrastructure targets include agriculture and food, banking and finance, dams, high-profile events, information systems, public health, national monuments, nuclear power plants, and water systems.


Wright, Liberatore, and Nydick: Survey of Operations Research Models and Applications in Homeland Security
Interfaces 36(6), pp. 514–529, © 2006 INFORMS

Apostolakis and Lemon (2005) used MAUT to prioritize the vulnerabilities in an infrastructure that they modeled using interconnected diagraphs and applied graph theory to identify candidate scenarios. Brown et al. (2004) applied simulation to study the impacts of disruptions and used risk analysis to assess infrastructure interdependencies. Their purpose was to identify infrastructure risks and ways to reduce them. Baskerville and Portougal (2003) developed a possibility model that suggests that, during an optimal length of time, the possibility of attack on information system infrastructures is very low. The risk associated with major utilities, such as water systems, is important to homeland security (Grigg 2003). Zografos et al. (1998) developed a datamanagement module, a vehicle-monitoring and communications module, and a modeling module and applied them to an emergency-repair operation for an electric utility company. They used a combined optimization and simulation approach to minimize service unavailability. Salmeron et al. (2004) developed a max-min model to help determine weaknesses in the electric grid to prepare for terrorist attacks. Through decomposition, they solved the problem with a heuristic on two test systems.

a chemical plant failure and showed that they could greatly improve risk assessment. Chowdhury et al. (1999) used linear programming to limit the availability of confidential information in a database while providing access to those who need it. They developed two transportation flow algorithms that are computationally efficient and insightful. Muralidhar et al. (1999) developed a model to explain how to use data-perturbation methods to protect information from unwanted access while allowing maximum access to genuine inquiries and maintaining the relationships between attributes.

Emergency Preparedness and Response
Emergency preparedness and response include such topics as planning for, preventing, responding to, and recovering from natural disasters and terrorism. Larson (2004, 2006) reviewed the literature on police, fire, and emergency medical services, and provided some coverage of hazardous materials, bioterrorism, and private-sector response to emergencies. The literature can be categorized into (1) early work, (2) location and resource allocation, (3) evacuation models, and (4) disaster planning and response. Early Work Green and Kolesar (2004) described a number of papers on emergency-response systems. Much of the OR work on managing emergency services originated with the New York City–Rand Institute. Its work with the New York City Fire Department included a simulation model of firefighting operations (Carter and Ignall 1970); queuing models of fire company availability (Carter et al. 1972); the “square root law” for the location of fire companies based on response distance, with a response time-distance function to predict response time (developed by Kolesar and Blum 1973 and applied by Rider 1976); an empirical Bayes approach to alarm forecasting (Carter and Rolph 1974); a stochastically-based integer linear programming model and a heuristic algorithm for fire company relocation (Kolesar and Walker 1974); a set covering approach for locating two types of ladder fire trucks (Walker 1974); heuristics for identifying high-priority alarm boxes (Ignall et al. 1975); Markovian decision models of initial dispatch of fire companies (Ignall et al. 1982, Swersey 1982); and a

Cyber Security
Research in cyber security helps prevent, protect against, detect, respond to, and recover from largescale cyber attacks on the information infrastructure. Although many studies concern network security, few can be considered operations research. Krings and Azadmanesh (2005) developed a model for transforming security and survivability applications so that they can be solved with graph and scheduling algorithms. Chen et al. (2005) explained how shared networks and the Internet have focused interest on IT security, particularly intrusion detection. They used data-mining methods (artificial neural networks and support vector machine) to identify potential intrusions. Abouzakhar and Manson (2002) addressed network security using two intelligent fuzzy agents to respond to denial-of-service attacks. Shindo et al. (2000) generated fault-tree and event-tree structures between a computer-network access point and a process plant. They applied their analysis to

Wright, Liberatore, and Nydick: Survey of Operations Research Models and Applications in Homeland Security
Interfaces 36(6), pp. 514–529, © 2006 INFORMS


book pulling together the accumulated work on fire deployment analysis (Walker et al. 1979) under support from the US Department of Housing and Urban Development (HUD). The New York City–Rand Institute’s work with the New York Police Department included work on deployment related to the 911 emergency telephone system (Larson 1972, 2002); scheduling patrol cars using queuing and linear programming (Kolesar et al. 1975); and the patrol car allocation model (PCAM) based on queuing and linear programming (Chaiken and Dormont 1978a, b). The multicar dispatch queuing model (Green 1984) was later incorporated into a revised version of PCAM (Green and Kolesar 1989) and applied to the proposed mergers of police and fire departments in several cities (Chelst 1988, 1990). Chaiken and Larson (1972) reviewed methods for allocating emergency units (vehicles). Chaiken (1978) described six models (including PCAM) developed with HUD support for fire and police operations and the challenges of implementing them. Location and Resource Allocation The early literature on locating emergency service facilities is based on the location set covering problem (LSCP) formulated by Toregas et al. (1971). In this problem, a population is served or covered when a facility is located within an acceptable service distance. The objective is to minimize the number of facilities while covering all demand points. Walker (1974) applied the LSCP to the location of ladder trucks in the boroughs of New York City. Plane and Hendrick (1977) applied the LSCP to the location of fire companies in Denver, Colorado. Daskin and Stern (1981) extended the LSCP to address multiple coverage of demand nodes. The maximal covering location problem (MCLP) developed by Church and ReVelle (1974) relaxes the LSCP’s requirement that all demand nodes are covered. The MCLP seeks to maximize the total population served within a maximum service distance, given a fixed number of facilities. Because Church and ReVelle leave some population uncovered, they include mandatory closeness constraints in their formulation. The MCLP is related to the p-median problem (Hakimi 1964), which seeks to locate p facilities to minimize total demand-weighted travel distances between demands and facilities.

Eaton et al. (1985) applied the MCLP model in Austin, Texas when EMS officials sought to improve operating efficiency. Saccomanno and Allen (1988) used a modified MCLP algorithm to locate emergency response capability for potential spills of dangerous goods on a road network. Belardo et al. (1984b) extended the MCLP to locate oil-spill-response equipment on Long Island Sound. Chung (1986) described other applications of the MCLP. Hogan and ReVelle (1986) modified set covering models to maximize the percentage of the population that receives backup coverage. Pirkul and Schilling (1988) developed a backup coverage model for facilities with limited workloads or capacities. Batta and Mannur (1990) extended the set covering models to include some demand points requiring responses from multiple units (for example, fire trucks or ambulances). Church et al. (1991) formulated a bicritera maximal covering location model that maximizes the demand covered within the maximal distance and also minimizes the distance traveled from the uncovered demand to the nearest facility. Schilling et al. (1979) developed the tandem equipment allocation model (TEAM) and the facility location, equipment, and emplacement technique (FLEET) model to allocate equipment with varying capabilities and demands. The FLEET model has been effectively applied to locate fire stations and allocate equipment. With some modifications, Tavakoli and Lightner (2004) applied Bianchi and Church’s (1988) multiple coverage, one-unit FLEET model (MOFLEET) to Cumberland County, North Carolina’s emergency medical services (EMS) system. Current and O’Kelly (1992) applied covering models to locate emergency warning sirens in a midwestern city. Building on covering-model research, Akella et al. (2005) addressed the problem of locating cellular base stations and allocating channels, while explicitly considering emergency coverage of areas known for vehicle crashes and sites prone to potential enemy attacks. The Lagrangean heuristic performed very well on test problems and in rural Erie County, New York. Several authors have developed optimization models that include stochastic elements. Daskin (1983) developed the maximal expected coverage location


Wright, Liberatore, and Nydick: Survey of Operations Research Models and Applications in Homeland Security
Interfaces 36(6), pp. 514–529, © 2006 INFORMS

model (MECLM), which seeks to locate emergency vehicles to maximize the expected coverage area, even when multiple vehicles are in use. Batta et al. (1989) offered an extended version of MECLM. They assumed that the probability that a randomly chosen vehicle is busy is independent of any other vehicle in use. ReVelle and Hogan (1989) proposed a variation of MECLM called the maximum availability location model (MALM) in which each constraint guarantees that the probability that a demand point receives service within an acceptable time is no less than a required value. In these latter two models, the analysts estimated probabilities that vehicles are busy in advance. Ball and Lin (1993) developed a model similar to MALM except that they directly model the source of the randomness. Goldberg and Paz (1991) developed an optimization model that seeks to maximize the expected number of emergency callers reached within a specified time. Analytical queuing models have been used to evaluate the performance of emergency service systems. In his hypercube model (1974, 1975, 2001), Larson characterizes the operation of an emergency service system as a multiserver queuing system in which the states correspond to all combinations of servers busy and idle. This model provides a set of output measures, such as vehicle utilization and average travel time, and has been used to deploy ambulances and police cars in various cities (Brandeau and Larson 1986, Larson and Rich 1987). Extensions include improving the accuracy of the model’s output measures by allowing the service rates to be server dependent (Halpern 1977), estimating the probability distribution of travel times (Chelst and Jarvis 1979), and allowing the dispatch of multiple units (Chelst and Barlach 1981). Researchers have suggested that these queuing models can be used as subroutines in optimization heuristics (Berman and Larson 1982, Benveniste 1985, and Berman et al. 1987). Carter et al. (1972), Hall (1972), and Chelst (1981) developed other analytic approaches. Savas (1969) used simulation analysis to evaluate proposed changes to the number and location of ambulances in New York. Fitzsimmons (1973) combined queuing and simulation to estimate the probabilities of particular ambulances being busy. This approach was combined with a pattern search routine

in the ambulance deployment method CALL (computerized ambulance location logic), which located ambulances to minimize mean response time. CALL was successfully applied in central Los Angeles to locate firehouses and in Melbourne, Australia to plan an emergency ambulance system. Later CALL was combined with a contiguous zone search routine (CZSR) that uses an existing database on interzone travel times to locate ambulances in Austin, Texas (Fitzsimmons and Srikar 1982). Swoveland et al. (1973) used simulation coupled with optimization to locate ambulances in Vancouver, Canada. Evacuation Models Researchers have developed optimization, queuing, and simulation models to plan emergency evacuations of buildings and areas. Most of such work relies on queuing or simulation, although some uses optimization. Chalmet et al. (1982) applied transshipment and dynamic network optimization models to planning the evacuation of large buildings. They applied the models to the evacuation of 322 people from an 11-story building with four elevators and two stairwells and compared the results with an observed evacuation to reveal possible improvements. Smith and Towsley (1981) applied analytical queuing network models to evacuating buildings using several examples. They modeled the buildings as hierarchical queuing networks. Talebi and Smith (1985) modeled the evacuation of a hospital as a finite closed queuing network model using mean-value analysis. Bakuli and Smith (1991, 1996; Smith 1991) used state-dependent queuing networks that incorporate a mean-value-analysis algorithm and unconstrained optimization to solve problems in which the widths of circulation paths in buildings can vary. Analysts have developed micro-, macro-, and mesosimulation models for planning evacuations. Micro-simulations track the detailed movements of individual entities (cars, trucks, or people) on the road network. Pidd et al. (1996) and de Silva and Eglese (2000) describe their development of a spatial decision-support system (SDSS) for contingency planning in emergency evacuations using a microsimulation model linked to a geographical information system (GIS). Mould (2001) used discrete-event simulation to plan the emergency evacuation of an

Wright, Liberatore, and Nydick: Survey of Operations Research Models and Applications in Homeland Security
Interfaces 36(6), pp. 514–529, © 2006 INFORMS


offshore oil installation. He considered environmental conditions, such as wind speed and wave height, while using a prespecified routine for evacuation and assessed the use of helicopters alone or in conjunction with fast rescue craft. He applied the model to a fictitious incident using randomly generated weather data. Jha et al. (2004) developed a micro-simulation model to evaluate five scenarios for evacuation planning at Los Alamos National Laboratory. Helbing et al. (2005) developed simulation models of pedestrian flows and used the results of these models as well as experiments to recommend designs to increase the efficiency and safety of facilities and egress routes. Using behavioral information, Stern and SinuanyStern (1989) used micro-simulation to plan evacuation under a radiological event. Macro-simulations do not track individual entities but use equations based on analogies with fluid flows in networks (Sheffi et al. 1982). Southworth and Chin (1987) used macro-simulation to study the evacuation of a population threatened by flooding from a failed dam based on empirical data from urban and rural areas. A compromise approach is to use meso-simulators that usually track the movement of groups of entities. Disaster Planning and Response How individuals and organizations respond to disasters is important in preparing for emergencies. Belardo et al. (1984a) discussed response problems faced by four organizations: the American Red Cross, the US Coast Guard, the Regional Emergency Medical Organization in Albany, New York, and the New York State Office of Disaster Preparedness. Averett (2005) discussed four examples of responding to and preparing for disasters using various modeling tools: (1) discrete optimization and simulation models for locating and configuring vaccination centers and redirecting the flow of patients, (2) a graphics tool for visualization and collaboration, (3) simulation for disaster management training, and (4) game theory to anticipate terrorist attacks and defend against them. Kananen et al. (1990) extended standard input-output models and used multiobjective linear programming to evaluate the potential impact of emergencies or disasters on the Finnish economy. Routing emergency vehicles is important in responding to emergencies. Recognizing the importance

of considering the stochastic and time-varying nature of travel conditions in emergency situations, MillerHooks and Mahmassani (1998) developed and tested two algorithms for determining the shortest path under such conditions. Several authors have modeled the problem of transporting vital first-aid commodities and emergency personnel to disaster-affected areas. Haghani and Oh (1996) used a deterministic multicommodity, multimodal network flow model to plan disaster relief. Barbarosoglu and Arda (2004) extended this approach to include random arc capacity, supply, and demand. They formulated the problem as a two-stage stochastic program and used data from the August 1999 earthquake in Marmara, Turkey. Srinivasa and Wilhelm (1997) and Wilhelm and Srinivasa (1997) developed a model that prescribes an effective response to an oil spill, which requires such decisions as which components to dispatch, how many, and when. They formulated the problem as a general integer program, using graph theory to generate response systems (components and their locations) needed by the model. They applied their approach to actual data representing the Galveston Bay area. They applied a heuristic (Wilhelm and Srinivasa 1997) and an exact procedure based on strong cutting-plane methods (Srinivasa and Wilhelm 1997). A few researchers have modeled human behavior in emergency situations. Reer (1994) developed a probabilistic procedure to analyze human reliability in emergency situations using time windows and organizational input data. Reer used the loss of main feedwater at a pressurized water reactor plant as an example to investigate several organizational alternatives. Doheny and Fraser (1996) developed a software tool that can be used to model human decision making during emergency situations. Their model includes frames to represent a person’s characteristics and perception of the environment, and scripts to define typical behaviors for particular situations. They used their model to simulate an offshore emergency scenario.

Threat Analysis
Threat analysis develops the capabilities to evaluate and disseminate extensive information about threats


Wright, Liberatore, and Nydick: Survey of Operations Research Models and Applications in Homeland Security
Interfaces 36(6), pp. 514–529, © 2006 INFORMS

and to identify planned attacks. The US government obtains extensive information on threats daily, and threat analysis research attempts to detect and document terrorists’ intentions. Raghu et al. (2005) discussed a collaborative decision-making framework for homeland security and a connectionist modeling approach that fuses disparate information from several sources. Popp et al. (2004) approached threat analysis from an information technology (IT) perspective. They argued that improved IT can reduce the time needed for searching for data, harvesting data, preprocessing data, and turning the results into reports and briefs. They discussed three core IT areas in depth: collaboration and decision tools, foreign-language tools, and pattern-analysis tools. These areas offer operations researchers opportunities to work in conjunction with information technologists to reduce terrorist attacks and their effects. Wang et al. (2004) developed an algorithm that looks for similarities in criminal identities. Using real data from a police department, they created a model that develops disagreement values for each pair of criminal records. The intent is to use IT to determine whether two criminal records represent the same person. Sheth et al. (2005) devised a process of semantic association that links disparate information to establish relationships between terrorists. They incorporated this methodology into a program that provides a 360-degree look at each passenger boarding a flight and develops a threat score for use in deciding about additional security screening. Pate-Cornell (2002) studied the fusion of intelligence information from different sources and used Bayesian analysis to rank threats and to prioritize safety measures. Dombroski and Carley (2002) used Bayesian analysis and biased network theory to estimate patterns of links between different cells of a terrorist organization to predict the structure of the terrorist network. Other researchers who developed Bayesian methods to aid in decision making are Santos (1996), Santos and Young (1999), and Santos et al. (2003). Santos and Haimes (2004) used input-output and decomposition analysis to provide a framework for describing how various terrorist activities are connected. They prioritize sectors based on the economic impact of terrorist activities. Haimes

and Horowitz (2004) modeled counterterrorism intelligence using a two-player hierarchical holographic modeling game. Kaplan et al. (2005) introduced a terror-stock model that estimates the size of terrorist groups and how that size changes when the terrorists themselves are attacked.

Discussion and Future Research
Operations research has contributed to issues related to homeland security in the United States even before 2001, when homeland security was defined. We adopted the research framework used by the US Department of Homeland Security’s science and technology directorate to classify the literature. As a result, we have not included some topics, such as the military that in some cases could be considered homeland security. We used a two-dimensional framework in examining the previous OR work in homeland security: the areas specified by the US Department of Homeland Security and the four phases in the disaster life cycle: planning, prevention, response, and recovery (Table 2). Planning is generally strategic and long term in nature and relates to preparing for a disaster. Planning examples include policy analysis, risk analysis, systems design, and resource allocation. Prevention efforts aim to identify and eliminate threats, for example, screening airline passengers or patrolling borders. Response activities occur immediately after a disaster and include stabilizing affected areas, immediate medical care, and evacuation. Finally, recovery focuses on returning the affected areas and populations to their pre-event status and includes restoring critical infrastructures, assisting affected persons, and coordinating relief efforts. Despite the attention paid to component support, many critical issues remain to be addressed within this category. Green and Kolesar (2004) described how component support problems are evolving. They suggested that analysts need to work on nonroutine emergencies and coordinating emergency service providers, and preparing for and responding to terroristic acts. A wealth of literature concerns the security of trucks transporting hazardous materials, largely such issues as routing them to avoid exposing populations

Wright, Liberatore, and Nydick: Survey of Operations Research Models and Applications in Homeland Security
Interfaces 36(6), pp. 514–529, © 2006 INFORMS


Category Countermeasures portfolios Biological





Hupert et al. (2002)

Sullivan and Perry (2004)

Chemical Radiological and nuclear High explosives Component support portfolios Border and transportation security Border security Airline security

Jenkins (2000)

Buckeridge et al. (2005), Craft et al. (2005), Kaplan et al. (2002, 2003), Stuart and Wilkening (2005), Wein et al. (2003)

Butler et al. (2005), Dyer et al. (1998), Munera et al. (1997)

Wein and Baveja (2005) Barnett et al. (2001), Candalino et al. (2004), Leone and Liu (2003), Virta et al. (2003)

Port and rail Truck

Glickman and Rosenfield (1984), Harrald et al. (2004), Lewis et al. (2003)

Barnett (2004), Gilliam (1979), Jacobson et al. (2000, 2001, 2003, 2005), Kobza and Jacobson (1996, 1997)

Batta and Chiu (1986, 1988), Berman et al. (2000), Beroggi and Wallace (1994, 2005), Erkut and Ingolfsson (2000, 2005), Erkut and Verter (1998), Giannikos (1998), Gopalan et al. (1990), Jin et al. (1996), Kara et al. (2003), Karkazis and Boffey (1995), Lindner-Dutton et al. (1991), List and Turnquist (1998), Raj and Pritchard (2000), van Steen (1987), Zografos and Androutsopoulos (2004) Baskerville and Portougal (2003) Chowdhury et al. (1999), Muralidhar et al. (1999) Zografos et al. (1998) Abouzakhar and Manson (2002), Chen et al. (2005), Shindo et al. (2000)

Critical infrastructure protection Cyber security

Apostolakis and Lemon (2005), Brown et al. (2004), Salmeron et al. (2004) Krings and Azadmanesh (2005)

Emergency preparedness and response Early work

Carter and Ignall (1970), Carter and Rolph (1974), Chaiken and Dormont (1978a, b), Chelst (1988, 1990), Green (1984), Green and Kolesar (1989), Kolesar et al. (1975), Ignall et al. (1975), Kolesar and Blum (1973), Kolesar and Walker (1974), Larson (1972), Rider (1976), Walker (1974)

Carter et al. (1972), Ignall et al. (1982), Swersey (1982)

Table continues next page

Table 2: Homeland security literature classified along two dimensions reveals opportunities for future research. We show where each paper fits within the Department of Homeland Security’s research framework and its position within the disaster life cycle. The two-dimensional framework in this table illustrates where the bulk of OR models and applications in homeland security exist. This table also highlights important gaps for future research. Focusing first on the rows in the table, we see that the topics that have received the most attention fall under the component support portfolios. Emergency preparedness and response contains significant amounts of work, with emphasis on emergency services location and resource allocation. Other highly important issues that have seen significant attention are evacuation models and disaster planning and response.


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Category Location and resource allocation

Planning Akella et al. (2005), Ball and Lin (1993), Batta and Mannur (1990), Batta et al. (1989), Belardo et al. (1984b), Benveniste (1985), Berman et al. (1987), Berman and Larson (1982), Bianchi and Church (1988), Brandeau and Larson (1986), Carter et al. (1972), Chelst (1981), Chelst and Barlach (1981), Chelst and Jarvis (1979), Church and ReVelle (1974), Current and O’Kelly (1992), Daskin (1983), Daskin and Stern (1981), Eaton et al. (1985), Fitzsimmons (1973), Fitzsimmons and Srikar (1982), Goldberg and Paz (1991), Hall (1972), Halpern (1977), Hogan and ReVelle (1986), Larson (1974, 1975, 2001), Larson and Rich (1987), Pirkul and Schilling (1988), Plane and Hendrick (1977), ReVelle and Hogan (1989), Saccomanno and Allen (1988), Savas (1969), Schilling et al. (1979), Swoveland et al. (1973), Tavakoli and Lightner (2004), Toregas et al. (1971), Walker (1974) Bakuli and Smith (1991, 1996), de Silva and Eglese (2000), Helbing et al. (2005), Jha et al. (2004), Mould (2001), Pidd et al. (1996), Sheffi et al. (1982), Smith (1991), Smith and Towsley (1981), Southworth and Chin (1987), Stern and Sinuany-Stern (1989), Talebi and Smith (1985) Barbarosoglu and Arda (2004), Doheny and Fraser (1996), Haghani and Oh (1996), Kananen et al. (1990), Miller-Hooks and Mahmassani (1998), Reer (1994) Dombroski and Carley (2002), Haimes and Horowitz (2004), Kaplan et al. (2005), Pate-Cornell (2002), Popp et al. (2004), Raghu et al. (2005), Santos and Haimes (2004)




Evacuation models

Chalmet et al. (1982)

Disaster planning and response

Averett (2005), Belardo et al. (1984a), Srinivasa and Wilhelm (1997), Wilhelm and Srinivasa (1997) Sheth et al. (2005), Wang et al. (2004) Santos (1996), Santos and Young (1999), Santos et al. (2003)

Threat analysis

Table 2: Continued.

unnecessarily and analyzing risks. Researchers should extend these models to incorporate homeland security issues, for example, protecting trucks against terrorist hijacking. Much of the extensive research on airline security has limited applicability because of changes in security systems and policies. Operations researchers have many new opportunities to contribute in this area. The countermeasures and component support portfolios offer many other opportunities for contribution. The literature on the countermeasures portfolio is increasing, but many issues still need exploration. Some would benefit from collaboration between operations

researchers and physical scientists, similar to that of Craft et al. (2005). OR methods are well suited for problems pertaining to cyber security, critical infrastructure protection, threat analysis, and border security; however, work in these areas so far is limited (Table 2). We classified papers in the disaster life cycle based on their main focus (Table 2). The literature has gaps with respect to some phases in the disaster life cycle. Most OR research in homeland security concerns planning. Some concerns prevention, but we uncovered no papers on the recovery phase and very few on the response phase. There is a need for more research on

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decision making after the disaster. As the 2005 hurricanes made clear, we need to respond effectively to large-scale disasters. Operations research modeling incorporating the complex interconnections between relief agencies and government entities can clarify these chaotic situations and help relief agencies to coordinate their efforts. Operations researchers have not developed models that support real-time decision making, such as those proposed by Tien (2003, 2005) in the decision informatics area. In addition, operations researchers should extend to disaster response such collaborative decision-making frameworks as Raghu et al. (2005). Research has begun on supply chain security, an important topic for many corporations. Chopra and Sodhi (2004) categorized supply chain risk and riskmitigation strategies. Russell and Saldanha (2003) discussed increased costs and the changes needed in operating supply chains as a result of terrorist threats. Asbjornslett and Rausand (1999) described how to reduce the vulnerability of production systems. Another important issue in need of OR work is private sector response to disasters. For example, Larson (2006) discussed the need for corporations, such as commercial airlines and manufacturers, to resume normal operations quickly after a disaster. The failures of critical infrastructures subjected to hurricanes and other natural disasters highlight the need for research exploring the interdependence of critical infrastructures and for OR models to predict the likelihood of subsequent failures. Most models assume that infrastructures operate in isolation. We need to understand the relationships among infrastructures to prevent one failure from causing additional failures. Research on homeland security can have far-reaching effects because of its importance to government and private agencies. Although these agencies offer research funding for homeland security research, the resulting studies may be classified or published in technical reports, and not released for publication in traditional academic outlets. The funding agencies may also see the details of the work as beneficial to terrorist groups. For instance, in a Washington Post article, Weiss (2005) reported that a paper concerning the security of the US milk supply by Lawrence M. Wein and Yifan Liu was removed from the

Proceedings of the National Academy of Sciences Web site. Despite such possible restrictions, operations researchers have many opportunities to contribute to homeland security. References
Abouzakhar, N. S., G. A. Manson. 2002. An intelligent approach to prevent distributed systems attacks. Inform. Management Comput. Security 10(5) 203–209. Akella, M. R., R. Batta, E. M. Delmelle, P. A. Rogerson, A. Blatt, G. Wilson. 2005. Base station location and channel allocation in a cellular network with emergency coverage requirements. Eur. J. Oper. Res. 164(2) 301–323. Apostolakis, G. E., D. M. Lemon. 2005. A screening methodology for the identification and ranking of infrastructure vulnerabilities due to terrorism. Risk Anal. 25(2) 361–376. Asbjornslett, B. E., M. Rausand. 1999. Assess the vulnerability of your production system. Production Planning and Control 10(3) 219–229. Averett, S. 2005. Building a better bulwark. Indust. Engrg. 37(2) 24–29. Bakuli, D. L., J. M. Smith. 1991. Optimal routing and resource allocation within state dependent evacuation networks. D. Sullivan, A. B. Clymer, eds. Simulation in Emergency Management and Engineering. SCS Multiconference, Arlington, 23–30. Bakuli, D. L., J. M. Smith. 1996. Resource allocation in state-dependent emergency evacuation networks. Eur. J. Oper. Res. 89(3) 543–555. Ball, M. O., F. L. Lin. 1993. Reliability model applied to emergency service vehicle location. Oper. Res. 41(1) 18–36. Barbarosoglu, G., Y. Arda. 2004. A two-stage stochastic programming framework for transportation planning in disaster response. J. Oper. Res. Soc. 55 43–53. Barnett, A. 2004. CAPS II: The foundation of aviation security. Risk Anal. 24(4) 909–916. Barnett, A., R. Shumsky, M. Hansen, A. Odoni, G. Gosling. 2001. Safe at home? An experiment in domestic airline security. Oper. Res. 49(2) 181–195. Baskerville, R. L., V. Portougal. 2003. A possibility theory framework for security evaluation in national infrastructure protection. J. Database Management 14(2) 1–13. Batta, R., S. S. Chiu. 1986. Locating 2-medians on tree network with continuous link demands. Ann. Oper. Res. 6 223–253. Batta, R., S. S. Chiu. 1988. Optimal obnoxious paths on a network: Transportation of hazardous materials. Oper. Res. 36(1) 84–92. Batta, R., N. R. Mannur. 1990. Covering-location models for emergency situations that require multiple response units. Management Sci. 36(1) 16–23. Batta, R., J. Dolan, N. Krishnamurthy. 1989. The maximal expected covering location problem: Revisited. Transportation Sci. 23 277–287. Belardo, S., K. R. Karwan, W. A. Wallace. 1984a. Managing the response to disasters using microcomputers. Interfaces 14(2) 29–39. Belardo, S., J. Harrald, W. A. Wallace, J. Ward. 1984b. A partial covering approach to siting response resources for major maritime oil spills. Management Sci. 30(10) 1184–1196.


Wright, Liberatore, and Nydick: Survey of Operations Research Models and Applications in Homeland Security
Interfaces 36(6), pp. 514–529, © 2006 INFORMS

Benveniste, R. 1985. Solving the combined zoning and location problem for several emergency units. J. Oper. Res. Soc. 36(5) 433–450. Berman, O., R. C. Larson. 1982. Median problem with congestion. Comput. Oper. Res. 9(2) 119–126. Berman, O., Z. Drezner, G. O. Wesolowsky. 2000. Routing and location on a network with hazardous threats. J. Oper. Res. Soc. 51(9) 1093–1099. Berman, O., R. Larson, C. Parkan. 1987. The stochastic queue p-median problem. Transportation Sci. 21 207–216. Beroggi, G. E. G., W. A. Wallace. 1994. Prototype decision support system in hypermedia for operational control of hazardous material shipments. Decision Support Systems 12(1) 1–12. Beroggi, G. E. G., W. A. Wallace. 2005. Operational control of the transportation of hazardous materials: An assessment of alternative decision models. Management Sci. 41(12) 1962–1977. Bianchi, G., R. L. Church. 1988. A hybrid fleet model for emergency medical design. Soc. Sci. Medicine 26 163–171. Bonder, S. 2002. Army operations research—Historical perspectives and lessons learned. Oper. Res. 50(1) 25–34. Brandeau, M., R. Larson. 1986. Extending and applying the hypercube queueing model to deploy ambulances in Boston. TIMS Studies in the Management Sciences 22. TIMS, Providence, RI, 121–153. Branscomb, L. M., R. D. Klausner. 2003. Making the nation safer: The role of science and technology in countering terrorism. Committee on science and technology for countering terrorism. The National Academies Press, National Research Council, Washington, D.C. Brown, T., W. Beyeler, D. Barton. 2004. Assessing infrastructure interdependencies: The challenge of risk analysis for complex adaptive systems. Internat. J. Critical Infrastructures 1(1) 108–117. Buckeridge, D. L., H. Burkom, M. Campbell, W. R. Hogan, A. W. Moore. 2005. Algorithms for rapid outbreak detection: A research synthesis. J. Biomedical Informatics 38(2) 99–113. Butler, J. C., A. N. Chebeskov, J. S. Dyer, T. A. Edmunds, J. Jia, V. I. Oussanov. 2005. The United States and Russia evaluate plutonium disposition options with multiattribute utility theory. Interfaces 35(1) 88–101. Candalino, T. J., S. H. Jacobson, J. E. Kobza. 2004. Designing optimal aviation baggage screening strategies using simulated annealing. Comput. Oper. Res. 31(10) 1753–1767. Carter, G. M., E. J. Ignall. 1970. Simulation model of fire department operations. Design and preliminary results. IEEE Trans. Systems Sci. Cybernetics SSC-6(4) 282–293. Carter, G., J. Rolph. 1974. Empirical Bayes methods applied to estimating fire alarm probabilities. J. Amer. Statist. Assoc. 69(348) 880–885. Carter, G. M., J. M. Chaiken, E. Ignall. 1972. Response areas for two emergency units. Oper. Res. 20(3) 571–594. Chaiken, J. M. 1978. Transfer of emergency service deployment models to operating agencies. Management Sci. 24(7) 719–731. Chaiken, J. M., P. Dormont. 1978a. A patrol car allocation model: Background. Management Sci. 24(12) 1280–1290. Chaiken, J. M., P. Dormont. 1978b. A patrol car allocation model: Capabilities and algorithms. Management Sci. 24(12) 1291–1300. Chaiken, J. M., R. C. Larson. 1972. Methods for allocating urban emergency units: A survey. Management Sci. 19(4, Part 2) 110–130.

Chalmet, L. G., R. L. Francis, P. B. Saunders. 1982. Network models for building evacuation. Management Sci. 28(1) 86–105. Chelst, K. 1981. Deployment of one- vs. two-officer patrol units: A comparison of travel times. Management Sci. 27(2) 213–230. Chelst, K. 1988. A public safety merger in Grosse Pointe Park, Michigan—A short and sweet study. Interfaces 18(4) 1–11. Chelst, K. 1990. Queueing models for police-fire merger analysis. Queueing Systems 7 101–124. Chelst, K., Z. Barlach. 1981. Multiple unit dispatches in emergency services: Models to estimate system performance. Management Sci. 27(12) 1390–1409. Chelst, K., J. P. Jarvis. 1979. Estimating the probability distribution of travel times for urban emergency service systems. Oper. Res. 27(1) 199–204. Chen, W., S. Hsu, H. Shen. 2005. Application of SVM and ANN for intrusion detection. Comput. Oper. Res. 32(10) 2617–2634. Chopra, S., M. S. Sodhi. 2004. Managing risk to avoid supply chain breakdown. MIT Sloan Management Rev. 46(1) 53–61. Chowdhury, S. D., G. T. Duncan, R. Krishnan, S. F. Roehrig, S. Mukherjee. 1999. Disclosure detection in multivariate categorical databases: Auditing confidentiality protection through two new matrix operators. Management Sci. 45(12) 1710–1723. Chung, C. 1986. Recent applications of the maximal covering location planning model. J. Oper. Res. Soc. 37(8) 735–746. Church, R., J. Current, J. Storbeck. 1991. A bicriterion maximal covering formulation which considers the satisfaction of uncovered demand. Decision Sci. 22 38–52. Church, R. L., C. ReVelle. 1974. The maximal covering location problem. Papers Regional Sci. 32 101–118. Craft, D. L., L. M. Wein, A. H. Wilkins. 2005. Analyzing bioterror response logistics: The case of anthrax. Management Sci. 51(5) 679–694. Current, J., M. O’Kelly. 1992. Locating emergency warning sirens. Decision Sci. 23(1) 221–234. Daskin, M. S. 1983. Maximum expected covering location model: Formulation, properties and heuristic solution. Transportation Sci. 17(1) 48–70. Daskin, M. S., E. N. Stern. 1981. A hierarchical objective set covering model for emergency medical service deployment. Transportation Sci. 15(2) 137–152. Department of Homeland Security. 2005. Retrieved July 1, 2005. http:/ / www.dhs.gov/dhspublic/interapp/editorial/editorial_0413.xml. de Silva, F. N., R. W. Eglese. 2000. Integrating simulation modeling and GIS: Spatial decision support systems for evacuation planning. J. Oper. Res. Soc. 51(4) 423–430. Doheny, J. G., J. L. Fraser. 1996. MOBEDIC—A decision modeling tool for emergency situations. Expert Systems Appl. 10(1) 17–27. Dombroski, M. J., K. M. Carley. 2002. NETEST: Estimating a terrorist network’s structure—Graduate student best paper award, CASOS 2002 Conference. Comput. Math. Organ. Theory 8(3) 235–241. Dyer, J. S., T. Edmunds, J. C. Butler, J. Jia. 1998. A multiattribute utility analysis of alternatives for the disposition of surplus weapons-grade plutonium. Oper. Res. 46(6) 749–762. Eaton, D. J., M. S. Daskin, D. Simmons, B. Bulloch, G. Jansma. 1985. Determining emergency medical service vehicle deployment in Austin, Texas. Interfaces 15(1) 96–108. Erkut, E., A. Ingolfsson. 2000. Catastrophe avoidance models for hazardous materials route planning. Transportation Sci. 34(2) 165–179.

Wright, Liberatore, and Nydick: Survey of Operations Research Models and Applications in Homeland Security
Interfaces 36(6), pp. 514–529, © 2006 INFORMS


Erkut, E., A. Ingolfsson. 2005. Transport risk models for hazardous materials: Revisited. Oper. Res. Lett. 33(1) 81–89. Erkut, E., V. Verter. 1998. Modeling of transport risk for hazardous materials. Oper. Res. 46(5) 625–642. Fitzsimmons, J. A. 1973. A methodology for emergency ambulance deployment. Management Sci. 19(6) 627–636. Fitzsimmons, J. A., B. N. Srikar. 1982. Emergency ambulance location using the contiguous zone search routine. J. Oper. Management 2(4) 225–237. Giannikos, I. 1998. Multiobjective programming model for locating treatment sites and routing hazardous wastes. Eur. J. Oper. Res. 104(2) 333–342. Gilliam, R. 1979. An application of queueing theory to airport passenger security screening. Interfaces 9(4) 117–123. Glickman, T. S., D. B. Rosenfield. 1984. Risks of catastrophic derailments involving the release of hazardous materials. Management Sci. 30(4) 503–511. Goldberg, J., L. Paz. 1991. Locating emergency vehicle bases when service time depends on call location. Transportation Sci. 25(4) 264–280. Gopalan, R., K. S. Kolluri, R. Batta, M. H. Karwan. 1990. Modeling equity of risk in the transportation of hazardous materials. Oper. Res. 38(6) 961–973. Green, L. 1984. A multiple dispatch queueing model of police patrol operations. Management Sci. 30(6) 653–664. Green, L., P. Kolesar. 1989. Testing the validity of a queueing model of police patrol. Management Sci. 35(2) 127–148. Green, L. V., P. J. Kolesar. 2004. Improving emergency responsiveness with management science. Management Sci. 50(8) 1001–1014. Grigg, N. S. 2003. Water utility security: Multiple hazards and multiple barriers. J. Infrastructure Systems 9(2) 81–88. Haghani, A. S., C. Oh. 1996. Formulation and solution of a multicommodity, multi-modal network flow model for disaster relief operations. Transportation Res., Part A 30(3) 231–250. Haimes, Y. Y., B. M. Horowitz. 2004. Adaptive two-player hierarchical holographic modeling game for counterterrorism intelligence analysis. J. Homeland Security and Emergency Management 1(3) 1–21. Hakimi, S. L. 1964. Optimum locations of switching centers and the absolute centers and medians on a graph. Oper. Res. 12 450–459. Hall, W. K. 1972. The application of multifunction stochastic service systems in allocating ambulances to an urban area. Oper. Res. 20(3) 558–570. Halpern, J. 1977. Accuracy of estimates for the performance criteria in certain emergency service queueing systems. Transportation Sci. 11(3) 223–242. Harrald, J. R., H. W. Stephens, J. R. van Dorp. 2004. A framework for sustainable port security. J. Homeland Security Emergency Management 1(2) 1–21. Helbing, D., L. Buzna, A. Johansson, T. Werner. 2005. Self-organized pedestrian crowd dynamics: Experiments, simulations, and design solutions. Transportation Sci. 39(1) 1–24. Hennessy, J. L., D. A. Patterson, H. S. Lin. 2003. Information technology for counterterrorism: Immediate actions and future possibilities. Committee on the role of information technology in responding to terrorism. The National Academies Press, National Research Council, Washington, D.C. Hogan, K., C. ReVelle. 1986. Concepts and applications of backup coverage. Management Sci. 32(11) 1434–1444.

Hughes, W. P. 2002. Navy operations research. Oper. Res. 50(1) 103–111. Hupert, N. A., I. Mushlin, M. A. Callahan. 2002. Modeling the public health response to bioterrorism: Using discrete event simulation to design antibiotic distribution centers. Medical Decision Making 22 S17–S25. Ignall, E., G. Carter, K. Rider. 1982. An algorithm for the initial dispatch of fire companies. Management Sci. 28(4) 366–378. Ignall, E., P. Kolesar, W. Swersey, W. E. Walker, G. Blum, G. Carter, H. Bishop. 1975. Improving the deployment of New York City’s fire companies. Interfaces 5(2) 48–61. Jacobson, S. H., J. E. Kobza, A. S. Easterling. 2001. A detection theoretic approach to modeling aviation security problems using the knapsack problem. IIE Trans. 33(9) 747–759. Jacobson, S. H., J. E. Kobza, M. K. Nakayama. 2000. Sampling procedure to estimate risk probabilities in access-control security systems. Eur. J. Oper. Res. 122(1) 123–132. Jacobson, S. H., L. A. McLay, J. E. Kobza, J. M. Bowman. 2005. Modeling and analyzing multiple station baggage screening security system performance. Naval Res. Logist. 52(1) 30–45. Jacobson, S. H., J. L. Virta, J. M. Bowman, J. E. Kobza, J. J. Nestor. 2003. Modeling aviation baggage screening security systems: A case study. IIE Trans. 35(3) 259–269. Jaiswal, N. K. 1997. Military Operations Research: Quantitative Decision Making. Kluwer Academic Publishers, Boston, MA. Jenkins, L. 2000. Selecting scenarios for environmental disaster planning. Eur. J. Oper. Res. 121(2) 275–286. Jha, M., K. Moore, B. Pashaie. 2004. Emergency evacuation planning with microscopic traffic simulation. Transportation Res. Record 1886 40–48. Jin, H., R. Batta, M. Karwan. 1996. On the analysis of two new models for transporting hazardous materials. Oper. Res. 44(5) 710–723. Kananen, I., P. Korhonen, J. Wallenius, H. Wallenius. 1990. Multiple objective analysis of input-output models for emergency management. Oper. Res. 38(2) 193–201. Kaplan, E. H., D. L. Craft, L. M. Wein. 2002. Emergency response to a smallpox attack: The case for mass vaccination. Proc. National Acad. Sci. 99 10935–10940. Kaplan, E. H., D. L. Craft, L. M. Wein. 2003. Analyzing bioterror response logistics: The case of smallpox. Math. Biosciences 185(1) 33–72. Kaplan, E. H., A. Mintz, S. Mishal, C. Samban. 2005. What happened to suicide bombings in Israel? Insights from a terror stock model. Stud. Conflict and Terrorism 28 225–235. Kara, B. Y., E. Erkut, V. Verter. 2003. Accurate calculation of hazardous materials transport risks. Oper. Res. Lett. 31(4) 285–292. Karkazis, J., T. B. Boffey. 1995. Optimal location of routes for vehicles transporting hazardous materials. Eur. J. Oper. Res. 86(2) 201–215. Kobza, J. E., S. H. Jacobson. 1996. Addressing the dependency problem in access security system architecture design. Risk Anal. 16(6) 801–812. Kobza, J. E., S. H. Jacobson. 1997. Probability models for access security system architectures. J. Oper. Res. Soc. 48(3) 255–263. Kolesar, P., E. H. Blum. 1973. Square root laws for fire engine response distances. Management Sci. 19(12) 1368–1378. Kolesar, P., W. E. Walker. 1974. An algorithm for the dynamic relocation of fire companies. Oper. Res. 22 249–274.


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Interfaces 36(6), pp. 514–529, © 2006 INFORMS

Kolesar, P. J., K. L. Rider, T. B. Crabill, W. E. Walker. 1975. Queueinglinear programming approach to scheduling police patrol cars. Oper. Res. 23(6) 1045–1062. Krings, A., W. A. Azadmanesh. 2005. A graph based model for survivability applications. Eur. J. Oper. Res. 164(3) 680–689. Larson, R. C. 1972. Urban Police Patrol Analysis. MIT Press, Cambridge, MA. Larson, R. C. 1974. A hypercube queueing model for facility location and redistricting in urban emergency services. Comput. Oper. Res. 1(1) 67–95. Larson, R. C. 1975. Approximating the performance of urban emergency service systems. Oper. Res. 23(5) 845–868. Larson, R. C. 2001. Hypercube queueing model. S. I. Gass, C. M. Harris, eds. Encyclopedia of Operations Research and Management Science, 2nd ed. Kluwer Academic Publishers, Boston, MA, 373–377. Larson, R. C. 2002. Public sector operations research: A personal journey. Oper. Res. 50(1) 135–145. Larson, R. C. 2004. O.R. models for homeland security. OR/MS Today 31(5) 22–29. Larson, R. C. 2006. Decision models for emergency response planning. D. Kamien, ed. The McGraw-Hill Handbook of Homeland Security McGraw-Hill, New York. Larson, R. C., T. Rich. 1987. Travel time analysis of New York City police patrol cars. Interfaces 17(2) 15–20. Leone, K., R. R. Liu. 2003. Measures of effectiveness for passengerbaggage security screening. Transportation Res. Record 1822 40–48. Lewis, B. M., A. L. Erera, C. C. White III. 2003. Optimization approaches for efficient container security operations at transshipment seaports. Transportation Res. Record 1822 1–8. Lindner-Dutton, L., R. Batta, M. H. Karwan. 1991. Equitable sequencing of a given set of hazardous materials shipments. Transportation Sci. 25(2) 124–137. List, G. F., M. A. Turnquist. 1998. Routing and emergency-responseteam siting for high-level radioactive waste shipments. IEEE Trans. Engrg. Management 45(2) 141–152. Miller-Hooks, E. D., H. S. Mahmassani. 1998. Least possible time paths in stochastic, time-varying networks. Comput. Oper. Res. 25(12) 1107–1125. Miser, H. J. 1998. What we learned early in the US Air Force about establishing and maintaining operational research groups. J. Oper. Res. Soc. 49(4) 336–346. Mould, G. I. 2001. Assessing systems for offshore emergency evacuation. J. Oper. Res. Soc. 52(4) 401–408. Munera, H. A., M. B. Canal, M. Munoz. 1997. Risk associated with transportation of spent nuclear fuel under demanding security constraints: The Colombian experience. Risk Anal. 17(3) 381–389. Muralidhar, K., R. Parsa, R. Sarathy. 1999. A general additive data perturbation method for database security. Management Sci. 45(10) 1399–1415. National Commission on Terrorist Attacks upon the United States. 2004. The 9/11 Commission Report. Washington, D.C. Office of Homeland Security. 2002. National strategy for homeland security. Washington, D.C. Pate-Cornell, E. 2002. Fusion of intelligence information: A Bayesian approach. Risk Anal. 22(3) 445–454. Pidd, M., F. N. de Silva, R. W. Eglese. 1996. A simulation model for emergency evacuation. Eur. J. Oper. Res. 90(3) 413–419.

Pirkul, H., D. A. Schilling. 1988. The siting of emergency service facilities with workload capabilities and backup service. Management Sci. 34(7) 896–908. Plane, D. R., T. E. Hendrick. 1977. Mathematical programming and the location of fire companies for the Denver fire department. Oper. Res. 25(4) 563–578. Popp, R., T. Armour, T. Senator, K. Numrych. 2004. Countering terrorism through information technology. Comm. ACM 47(3) 36–43. Raghu, T. S., R. Ramesh, A. B. Whinston. 2005. Addressing the homeland security problem: A collaborative decision-making framework. J. Amer. Soc. Inform. Sci. Tech. 56(3) 310–324. Raj, P., K. E. W. Pritchard. 2000. Hazardous materials transportation on U.S. railroads: Application of risk analysis methods to decision making in development of regulations. Transportation Res. Record 1707 22–26. Reer, B. 1994. A probabilistic method for analyzing the reliability effect of time and organizational factors. Eur. J. Oper. Res. 75(3) 521–539. ReVelle, C., K. Hogan. 1989. The maximum availability location problem. Transportation Sci. 23 192–200. Rider, K. L. 1976. A parametric model for the allocation of fire companies in New York City. Management Sci. 23(2) 146–158. Russell, D. P., J. P. Saldanha. 2003. Five tenants of security-aware logistics and supply chain operation. Transportation J. 42(4) 44–54. Saccomanno, F. F., B. Allen. 1988. Locating emergency response capability for dangerous goods incidents on a road network. Transportation Res. Record 1193 1–9. Salmeron, J., K. Wood, R. Baldick. 2004. Analysis of electric grid security under terrorist threat. IEEE Trans. Power Systems 19(2) 905–912. Santos, E., Jr. 1996. On linear potential functions for approximating Bayesian computations. J. Assoc. Comput. Machinery 43(3) 399–430. Santos, E., Jr., J. D. Young. 1999. Probabilistic temporal networks: A unified framework for reasoning with time and uncertainty. Internat. J. Approximate Reasoning 20(3) 263–291. Santos, E., Jr., E. Santos Sr., S. E. Shimony. 2003. Implicitly preserving semantics during incremental knowledge base acquisition under uncertainty. Internat. J. Approximate Reasoning 33 71–94. Santos, J. R., Y. Y. Haimes. 2004. Modeling the demand reduction input-output (I-O) inoperability due to terrorism of interconnected infrastructures. Risk Anal. 24(6) 1437–1451. Savas, E. S. 1969. Simulation and cost-effectiveness analysis of New York’s emergency ambulance service. Management Sci. 15(12) B608–B627. Schilling, D., D. J. Elzinga, J. Cohon, R. Church, C. ReVelle. 1979. TEAM/FLEET models for simultaneous facility and equipment siting. Transportation Sci. 13(2) 163–175. Sheffi, Y., H. Mahmassani, W. B. Powell. 1982. Transportation network evacuation model. Transportation Res. A 16A(3) 209–218. Sheth, A., A. M. Boanerges, I. B. Arpinar, C. Bertram, Y. Warke, C. Ramakrishanan, C. Halaschek, K. Anyanwu, D. Avant, F. S. Arpinar, K. Kochut. 2005. Semantic association identification and knowledge discovery for national security applications. J. Database Management 16(1) 33–53. Shindo, A., H. Yamazaki, A. Toki, R. Maeshima, I. Koshijima, T. Umeda. 2000. Approach to potential risk analysis of networked chemical plants. Comput. Chemical Engrg. 24(2) 721–727.

Wright, Liberatore, and Nydick: Survey of Operations Research Models and Applications in Homeland Security
Interfaces 36(6), pp. 514–529, © 2006 INFORMS


Smith, J. M. 1991. State-dependent queueing models in emergency evacuation networks. Transportation Res. B 25(6) 373–389. Smith, J. M., J. Towsley. 1981. The use of queuing networks in the evaluation of egress from buildings. Environment and Planning 8 125–139. Southworth, F., S. M. Chin. 1987. Network modeling for flooding as a result of dam failure. Emergency and Planning 19 1542–1558. Srinivasa, A. V., W. E. Wilhelm. 1997. A procedure for optimizing tactical response in oil spill clean up operations. Eur. J. Oper. Res. 102(3) 554–574. Stern, E., Z. Sinuany-Stern. 1989. A behavioural based simulation model for urban evacuation. Papers Regional Sci. Assoc. 66(1) 87–103. Stuart, A. L., D. A. Wilkening. 2005. Degradation of biological weapons agents in the environment: Implications for terrorism response. Environ. Sci. Tech. 39(8) 2736–2743. Sullivan, T. J., W. L. Perry. 2004. Identifying indicators of chemical, biological, radiological, and nuclear (CBRN) weapons development activity in sub-national terrorist groups. J. Oper. Res. Soc. 55(4) 361–374. Swersey, A. J. 1982. A Markovian decision model for deciding how many fire companies to dispatch. Management Sci. 28(4) 352–365. Swoveland, C., D. Uyeno, I. Vertinsky, R. Vickson. 1973. Ambulance location: A probabilistic enumeration approach. Management Sci. 20(4, pt. 2) 686–698. Talebi, K., J. M. Smith. 1985. Stochastic network evacuation models. Comput. Oper. Res. 12(6) 559–577. Tavakoli, A., C. Lightner. 2004. Implementing a mathematical model for locating EMS vehicles in Fayetteville, NC. Comput. Oper. Res. 31(9) 1549–1563. Tien, J. M. 2003. Towards a decision informatics paradigm: A realtime, information-based approach to decision making. IEEE Trans. Systems, Man, Cybernetics 33(1) 102–113. Tien, J. M. 2005. Viewing urban disruptions from a decision informatics perspective. J. Systems Sci. Systems Engrg. 14(3) 257–288. Toregas, C., R. Swain, C. ReVelle, L. Bergman. 1971. The location of emergency service facilities. Oper. Res. 19(6) 1363–1373.

Turney, M. A., J. C. Bishop, P. C. Fitzgerald. 2004. Measuring the importance of recent airport safety interventions. J. Air Transportation 9(3) 56–66. van Steen, J. F. J. 1987. Methodology for aiding hazardous materials transportation decisions. Eur. J. Oper. Res. 32(2) 231–244. Virta, J. L., S. H. Jacobson, J. E. Kobza. 2003. Analyzing the cost of screening selectee and non-selectee baggage. Risk Anal. 23(5) 897–908. Walden, J., E. H. Kaplan. 2004. Estimating time and size of bioterror attack. Emerging Infectious Diseases 10(7) 1202–1205. Walker, W. 1974. Using the set-covering problem to assign fire companies to fire houses. Oper. Res. 22(2) 275–277. Walker W., J. Chaiken, E. Ignall. 1979. Fire Department Deployment Analysis: A Public Policy Case Study: The RAND Fire Project. North-Holland, New York. Wang, G., H. Chen, H. Atabakhsh. 2004. Automatically detecting deceptive criminal identities. Comm. ACM 47(3) 70–76. Wein, L., M. M. Baveja. 2005. Using fingerprint image quality to improve the identification performance of the U.S. visitor and migrant status indicator technology program. Proc. National Acad. Sci. 102(21) 7772–7775. Wein, L. M., D. L. Craft, E. H. Kaplan. 2003. Emergency response to an anthrax attack. Proc. National Acad. Sci. 100 4346–4351. Weiss, R. 2005. Report warns of threat to milk supply. Washington Post. Washington, D.C., June 29, A08. Wilhelm, W. E., A. V. Srinivasa. 1997. Prescribing tactical response for oil spill clean up operations. Management Sci. 43(3) 386–402. Wood, M. 1961. The national security dilemma: Challenge to management scientists. Management Sci. 7(3) 195–209. Zografos, K. G., K. N. Androutsopoulos. 2004. A heuristic algorithm for solving hazardous materials distribution problems. Eur. J. Oper. Res. 152(2) 507–519. Zografos, K. G., C. Douligeris, P. Tsoumpas. 1998. An integrated framework for managing emergency-response logistics: The case of the electric utility companies. IEEE Trans. Engrg. Management 45(2) 115–126.

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