Proceedings of the 2005 Systems and Information Engineering Design Symposium Ellen J. Bass, ed.

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1 Proceedings of the 2005 Systems and Information Engineering Design Symposium Ellen J. Bass, ed. SIMULATION AND ANALYSIS OF THE INSPECTION PROCESS OF CARGO CONTAINERS ABSTRACT This project simulates and analyzes the inspection process of cargo containers as they enter the United States Seaports. The purpose for this project is to create a more efficient inspection process allowing more containers to be inspected without too significant of a cost. The inspection process that was simulated has four major parts. The different stages in the process include: targeting containers, passive inspection, active inspection, and a manual inspection. The different stages included times, costs, and probabilities. These were used as input data for the model. Different scenarios of the simulation model were then created to find a more efficient system in which a greater number of containers could be inspected. 1 INTRODUCTION Jason Sharpe Engineering Science School of Engineering and Applied Science University of Virginia Charlottesville, VA This section discusses why simulating the cargo inspection process is necessary. It also gives background information on the inspection process and the social and ethical impacts that may result due to failures in the inspection process. The purpose of this project was to simulate the inspection process of cargo containers as they arrive at United States seaports. The project analyzed the simulation to try to find a more efficient method at detecting the containers for contraband. The motivation for this project was the threat of cargo containers carrying undetected weapons of mass destruction into the United States Seaports. The inspection process used today to check for contraband includes only a small percentage of the containers that come into the United States. With the ongoing war with terrorism, it is evident that more security measures need to be set in place to prevent another situation like September 11 th This simulation model will allow users to analyze the process and determine where it can be manipulated in order to increase the probability of detecting illegal objects that enter the United States borders. Another important rationale for building this simulation model is because it is much less costly to reconfigure and test a model than a full-scale operational system. Barb Tawney K. Preston White, Jr Systems and Information Engineering School of Engineering and Applied Science University of Virginia Charlottesville, VA According to Jonathan Medalia, a specialist in National Defense, Terrorists have been trying to obtain weapons of mass destruction, including chemical, radiological, and biological weapons for some time (Medalia, 2002). Recent attacks like the terrorist attack of September 11 th, 2001 illustrated how vulnerable U.S. facilities were to an attack. One target in particular that has attracted Congress attention, according to Medalia (2002) is the nation s seaports. Medalia (2002) states that if a terrorist smuggled a Hiroshima-sized bomb into a port and set it off, the attack would destroy buildings out to a mile or two; start fires, especially in a port that handled petroleum and chemicals; spread fallout over many square miles; and disrupt commerce (Medalia, 2002). The resulting fallout would be extremely difficult and expensive to clean up. The inspection process needs to be made more efficient so that they can inspect a greater percentage of incoming containers. The attack of 9/11 should make it apparent that the United States is not invincible and is capable of being attacked by a terrorist. The fact that the nation s seaports are considered vulnerable to an attack is what influenced this project. According to Lawrence Wein from Stanford University who did a study on the Prevention of Illicit Nuclear Materials in Shipping Containers there are two strategies being used to inspect cargo containers: Strategy A: Passive (neutron and gamma-ray) radiation monitoring of all containers followed by active (gamma radiography) testing of all untrusted containers and of trusted containers failing radiation monitoring (Wein, 2004). Strategy U: Trusted containers are not tested. Passive radiation monitoring of all un-trusted containers, followed by active testing of un-trusted containers failing radiation monitoring (Wein, 2004). According to Wein (2004), the U.S. government has not formulated a specific strategy, it is using a variant of strategy U, i.e., only containers that are flagged by the ATS undergo any passive or active testing (Wein, 2004).

2 Strategy U was chosen to simulate with this project as it is being used most widely in the United States. There are four core activities associated with this inspection process when the containers enter the United States seaports. The different parts of the inspection process include targeting of containers that are considered high-risk, passive inspection, active inspection, and manual/intrusive inspection. A diagram of the inspection process can be seen in Figure 1.1 below to get a better understanding of how the inspectors attempt to detect contraband as the containers arrive in the United States. The first action that the inspectors take when the containers reach the U.S. seaports is trying to identify containers that pose a high risk. The targeting is aided by an Automated Targeting System (ATS), which is designed to guide the inspectors in picking containers that are considered high-risk. According to Richard Stana, the ATS system relies heavily on the manifest that is provided by the shipper, which requires one to trust the shipper to know what they are carrying (Stana, 2004). It is estimated that only 2-4 percent of the containers that come into the United States are targeted according to Stephen Flynn (Flynn, 2004). The inspectors are also trained to look for tampering of the containers, such as broken seals on the doors or odd markings on the containers. If the container is targeted, then it can go through the passive inspection. The passive inspection monitors the emissions of neutrons and gamma rays from the containers. This stage in the process is the least time consuming and least effective at detecting contraband. If the container fails this stage or the container is still thought to contain contraband it is sent on to the active inspection. The active inspection process utilizes the Vehicle and Cargo Inspection System (VACIS), which takes an image of the inside of the container through the use of gamma rays. The image provided by this technology allows the inspectors to detect visual anomalies, such as different densities in the containers that might be narcotics or other illegal goods. Figure 1.2 in the appendix shows an illustration of a mobile VACIS, which requires ten minutes to set up and six seconds to scan a 40 foot container. An illustration of the image that is produced by this machine can be seen in Figure 1.3 of the appendix. If the targeted containers fail the active inspection, they can then go on to the intrusive inspection process where the inspectors open the container and examine the container for illegal goods. The problem with this process is that there aren t really any firm standards set for inspection according to Flynn (Lok, 2004). Stored/Shipped to Destination No Containers unloaded from ships Targeted? Yes Passive Inspection Pass Inspection? No Active Inspection Pass Inspection? No Intrusive Inspection Pass Inspection? No Fail Inspection Yes Yes Yes Overflow Overflow Overflow Figure 1.1: Flow Diagram of the Inspection Process A recent study in March of 2004 by Professor Lawrence Wein and his graduate students at Stanford University along with Stephen Flynn examine the current inspection process. They examine how the existing protocols would fare against a nuclear warhead with 4 kg of weapons-grade plutonium or 12 kg of weapons-grade uranium, shielded with tungsten and lithium hydride, and which is shipped within a container with other cargo (Flynn, 2004). Their study found, according to Flynn, that the probability of detection of a weapon from an un-trusted shipper is 9.75 percent, where the probability of detection of a weapon from a trusted shipper has a maximum of 25 percent (Flynn, 2004). The first step in this project was to research the inspection process and the social and ethical contexts that were related to this project. The next stage in the project was to build the model in a simulation application, Arena, to simulate the inspection process using strategy U. After these steps have been completed, the next step is to collect data such as time and costs associated with the inspection process and use it as input for Arena s model. Once the data is in the model, it can be simulated and analyzed to find the most efficient inspection process. The final stage of the project will involve writing up the technical docu- 142

3 ment which includes the results as well as the recommendations that might be evident after completion. The next chapter discusses the data that was used in this simulation model and how it was obtained. Section three goes into detail about the different parts of the simulation model, the variables and attributes used, and the constraints used in making the model. Section four describes the results of the project and the analysis that was done on the model. Section five concludes the project by stressing the importance of the project as well as reiterating the results that were found. 2 DATA COLLECTION This section discusses the data that was used to make the simulated model of the inspection process. The data includes times, costs, probabilities, and assumptions of the inspection process. How the data was obtained and the accuracy of the data is also discussed in this chapter. Since the data for this project was hard to obtain because of governmental regulations, Data from previous studies and data from experts in the field were used. One source that was used was a study done by Lawrence Wein and his graduate students at Stanford University on Preventing the Importation of Illicit Nuclear Materials in Shipping Containers. This study involves a mathematical model about the probability of detecting a nuclear weapon with today s inspection technology. Stephen Flynn, who was also involved in this study, provided expert advice on the inspection process. Another source used in building the model was Professor Michael Smith from the Systems Engineering and Information Engineering Department at University of Virginia. He was formerly employed by the Science Applications International Corporation (SAIC), which is a company that makes the Vehicle and Contraband Inspection Systems (VACIS) machines for the intrusive inspections. He has also done studies and simulations of inspection processes from different ports around the United States. Figure 1.1 in the introduction, shows that 2-3% of the incoming containers are targeted and go on to the passive inspection. The study by Wein (2004) assumed that 40% of the containers that passed the passive inspection would go onto the active inspection since very few containers will set off the radiation alarms. This study also assumed that 5% of the actively tested containers would set off alarms because of an unexpected object and that 10% of the containers would be too dense to penetrate with the VACIS machines (Wein, 2004). This means that 15% of the actively tested containers will go onto the manual inspection. Since the probability of the failed manual inspections was hard to come by, I assumed it was 50% in the simulation model. According to Michael Smith (2005), about 60 containers are removed from a ship per hour. The time needed to perform the passive inspection is only 5.5 seconds, where as the active inspection takes 3 minutes per container to complete (Wein, 2004). It takes approximately 2.5 hours for the manual inspection to be completed according to Smith (2005). The study by Wein (2004) assumed that the port was open for two 8 hour shifts per day. Since no data was present for the routing times between different testing stations, 20 minutes was assumed. This is a high approximation, but Smith (2005) states that some ports use off site inspection warehouses to do their manual inspection so this will include them as well. There are several costs included in the simulation model to generate a better analysis of the real world system. These costs represent the total annual global cost associated with the process. The study by Wein (2004) used a factor of 0.2 to be multiplied by the inspection machines to represent the lifetime of the machines and the costs associated with them. Also in this study was a finding that the number of passive testing machines was related to the number of terminals, which are about 50 in the 30 largest US seaports (Wein, 2004). The passive portal monitor costs $80k and requires two employees with a salary of $75,000 per year (Wein, 2004). An equation used to calculate the cost due to the passive inspection is below: Passive Cost = 50 terminals * (0.2*80, employees * 2 shifts * 75,000) The active portal monitor costs $100k and requires three employees earning $75k per year according to Wein (2004). The study by Wein (2004) stated that the number of portal monitors was related to the number of ship cranes which was approximately 100 in the 30 largest US seaports. An equation used to calculate the cost due to the active inspection is below: Active Cost = 100 Machines * (0.2* employees * 2 shifts * 75000) The study by Wein (2004) also found that the number of manual inspection sites was related to the number of ship cranes, which was assumed 100. A team of 5 workers earning $80k per year is the assumption that was made by Wein (2004) to understand the manual inspection costs. According to Smith (2005), the cost of a container going to the manual inspection is around 300 dollars to the shipper of the container. An equation for the annual manual inspection cost can be seen below: Manual Cost = 100 sites * (2 teams * 5 employees * 80000) The data collected was based on expert assumptions, like those by Wein and Smith, and will have some error associated with them. Another error that can be associated with this data is that each US seaport varies in size and the inspection process for each port may vary as well. Though there maybe some error associated with this project, how- 143

4 ever, the assumptions made were close enough to give a reasonable representation of the inspection process. 3 THE SIMULATED INSPECTION PROCESS This section gives a brief introduction to the simulation application, Arena, and why it was used. This chapter also explains how the model was made in Arena and the different attributes and variables that were used. Arena is a software application that allows a user to create a simulation that will mimic the behavior of real systems. This type of software is needed because many real world systems can not be solved analytically. The model that is created in Arena is based on logical models made up of approximations and assumptions. The input logic for the inspection process model will be based on the data in section two. To begin a simulation, Arena provides a Create block to generate the entities that will travel through the system. In this project, the entities created are the containers. Another logic block used in Arena is the Assign block, which can assign values to variables or attributes as the entity passes through the system. An attribute assigns a value to a specific entity, where as a variable refers to a global value. Stations and Route Blocks are used to show movement through the system through the use of animation. Stations represent locations in a system where a delay may occur and the Route Blocks are used to move from station to station. A Decide Block is used in Arena when a choice has to be made in the system. The Process Block allows the user to delay an entity through the system by limited resources that are able to serve the entity. The Dispose Block is where the entity exits the system. In Appendix A, it shows how the logic for the creation and targeting of the containers in the simulation is constructed. The Creation of Containers in Figure 3.1 is a Create block that generates containers entering the system with an exponential distribution and a mean of one per minute. The exponential distribution was used, because it is often used with entities entering a system. The Assign 1 Block assigns an attribute to each container for the beginning time that it enters the system. If the container is targeted, it will be routed to the Passive Inspection station as can be seen in Figure 3.2 in the appendix. The Assign 2 Block keeps a counter of the containers that go through the passive inspection. Passive in Figure 3.2 is a Process Block that represents the delay of the passive inspection, which is represented by a triangular distribution of (3.16, 5.50, 7.84) or (minimum, mean, maximum) values in seconds. The mean value comes from the values listed in chapter two. The min and max values for the triangular distributions in this model were calculated by taking the square root of the mean and adding it to the mean to get the maximum and subtracting it to get the minimum. Pass Passive? in Appendix B represents the Decide Block where 40% are assigned to fail the passive inspection and go onto the active inspection. Once the containers fail the passive inspection, they will then be routed to the Active Inspection station as can be seen above in Appendix C. Assign 3 keeps a counter of how many containers are going through the active inspection. Active in Appendix C refers to the process block that represents the delay of a container that would be sent through the active inspection stage. The delay is represented by a triangular distribution of (1.27, 3.00, 4.73) in minutes. The mean value comes from the value listed in chapter two, and the minimum and maximum values were calculated the same way as for the passive inspection. Pass Active? the figure in Appendix C represents the Decide Block where 15% of the containers are designed to fail and go onto the intrusive inspection. Once the containers fail the active inspection, they will then be routed to the Intrusive Inspection station as can be seen in Appendix.D The Assign 4 Block will then assign the 300-dollar additional cost to the Global Cost Variable and keep a counter of the containers that go through the manual inspection. The Global Cost Variable in the model is initialized to the sum of the cost of the passive inspection, active inspection, and the manual inspection. The Pass Intrusive? is a Decide Block that is designed to make 50% of the containers fail the manual inspection. The 50% of the containers that fail will then be sent on to the Failure Station, which can be seen in the figure in Appendix. E. The containers that pass the passive, active, and manual inspections will be routed to the Overflow Station in the figure of appendix E. Containers that are not targeted will be routed to the Ready to Ship station in Appendix E. After the containers have been through the inspection process, their times in the system will be calculated and then the containers will exit the simulation through the Dispose Blocks. 4 ANALYSIS OF THE SIMULATED MODEL This section presents the results of the simulated inspection process model. Different scenarios of the simulated model were used to try to find an optimized model. The different models are analyzed in this chapter by cost, time, and the number of containers that are possible to be inspected. The simulated inspection process that resembles the current inspection process was run for a period of a year with 10 replications. The number of containers that went through the port in this simulation averaged out to be 525,972 per year. The total annual cost, including the additional manual inspection costs to the shipper was $143,088,390. The only part of the inspection process that had a significant queue was the manual inspection, which was a mean of hours. The average number of containers that were inspected by the manual inspection was 144

5 960 per year. Table 4.1 below lists the values that were used in making the simulated model. The values that were generated after the simulation are listed in Table 4.2. Table 4.1: Base Values Base % Targeted 3% Passive Employees 4 # of Passive Machines 50 Active Employees 6 # of Active Machines 100 Manual Employees 5 Manual Teams 2 Table 4.2: Simulated Base Values Additional Manual Cost per Year $ 288,390 Global Annual Cost $143,088,390 # of Containers per year 525,972 Replications 10 Ave. Time in Queue (Passive) Hrs 0 Ave. Time in Queue (Active) Hrs 0 Ave. Time in Queue (Manual) Hrs Ave. # of Containers (Passive) Ave. # of Containers (Active) Ave. # of Containers (Manual) Table 4.3 Assumptions and Values for the Scenarios % Targeted 5% 5% 5% 10% 10% 10% 15% 15% 15% Passive Employees # of Passive Machines Active Employees # of Active Machines Manual Employees Manual Teams Table 4.4 Values From the Different Simulated Scenarios Additional Manual Cost per Year ($) 1.18E E E E E+06 Global Annual Cost ($) 7.55E E E E E+06 # of Containers per year 525, , , , ,589 Replications Ave. Time in Queue (Passive) Hrs Ave. Time in Queue (Active) Hrs Ave. Time in Queue (Manual) Hrs Ave. # of Containers (Passive) Ave. # of Containers (Active) 26, Ave. # of Containers (Manual) Additional Manual Cost per Year ($) 2.38E E E E+06 Global Annual Cost ($) 8.29E E E E+15 # of Containers per year 525, , , ,217 Replications Ave. Time in Queue (Passive) Hrs Ave. Time in Queue (Active) Hrs Ave. Time in Queue (Manual) Hrs Ave. # of Containers (Passive) Ave. # of Containers (Active) , , Ave. # of Containers (Manual)

6 Proceedings of the 2005 Systems and Information Engineering Design Symposium Ellen J. Bass, ed. Base Compared to 5% Targeted Containers Base Compared to 10% Targeted Containers 3.00.E E Global Annual Cost ($) 2.50.E E E E E Ave. Time in Queue (Hrs) Global Annual Cost Manual Queue Global Annual Cost ($) 2.50.E E E E E Ave. Time in Queue (Hrs) Global Annul Cost Manual Queue 0.00.E+00 Base Models E+00 Base Models 0 Figure 4.5 Base vs. 5% Targeted Scenarios Figure 4.6: Base vs. 10% Targeted Scenarios In the different scenarios, the passive inspection was left off. In Wein s (2004) study they found that if the nuclear weapon was being shipped in a container it would probably be sealed with a special impenetrable metal thatwould not be detected by the passive inspection. They stated that it was a good probability that if a terrorist was going to go through all the trouble to get the weapon over here then they would seal it in metal to shield the emit Table 4.3 Assumptions d radiation (Wein, 2004). The different scenarios that were created ranged in the percent of containers being targeted from 3-15%. The number of active machines used to inspect the containers was also a variable in the scenarios. Another variable that was used in the different models was the number of manual teams used to inspect the containers. The values and assumptions that were made in the different scenarios are listed in table 4.3. The results of the 9 different scenarios are listed in table 4.4. In analyzing the different scenarios with each other and the base case model, the most important things were cost, the number of containers able to go through the port each year, and the queuing times. The queuing time for the manual inspection was greatly increased as the targeting rate increased due to the time length of the manual inspection. Refering to Figure 4.5 you will find a graph depicting the base case scenario compared to the scenarios that were targeted at 5%. From this graph 3 would be selected as the best scenario since its cost and queuing time are only slightly larger than the base case. The number of containers per year with this scenario would be only 251 less at each port. If you refer to Figure 4.6 you will find the comparison between the cost and the queue time in the manual inspection between the base case and the containers that are targeted at 10%. As can be seen from the above graph, 6 would be the best scenario since its cost and queue for manual inspection is not much greater than the base case. 6 more than triples the number of containers being targeted each year. The number of containers that are able to go through the port in 6 is only 687 less than the base case. Figure 4.7 shows a comparison between the base case and the containers being targeted at 15% scenarios. From the chart it is shown how much difference the 5% of targeting effects the manual queue from the previous chart. From figure 4.7, I would choose the base case as the best scenario sense the manual inspection queue is too significant. From the 3 best scenarios that were chosen, 6 is chosen as the best scenario sense it has a low cost relative to the base case. The queue time for this scenario is a little over one hour, which is not unreasonable considering the threat that might be imposed if the container contained a weapon of mass destruction. The reduction in containers per year at the port will have little effect on the global economy since it is less than ½ % of the total containers that came into the ports each year on the base case scenario. 5 CONCLUSION This section concludes this paper with a summary of the results of the project. This chapter will also discuss the significance and accuracy of the results. Also provided in this chapter are recommendations for future research and changes for the current inspection process.

7 Global Annual Cost ($) 3.00.E E E E E E E+00 Base Compared to 15% Targeted Containers Base Models Ave. Time in Queue (Hrs) Gobal Annual Cost Manual Queue Figure 4.7: Base vs. 15% Targeted Scenarios The results showed that the manual inspection queue was the main time consumer in the inspection process. With a targeting rate of 15%, the manual queue was much too great even when an additional team was added. Another finding from this report was that if the passive inspection was removed, it would decrease the global annual cost allowing more money to be spent in other parts of the inspection process. 6 was chosen as the best scenario for the inspection process with an Annual Global Cost expected to be 2.38 million dollars. The average queue time for the manual inspection was found to be hours, but the targeted containers increases from 3% to 10%. The significance of the results was the simulation model that was created allowed analysis of the inspection process for better efficiency in cost and time. The scenario that was found to be the best was one that required the dropping of the passive inspection process. This was necessary because the passive inspection only measured the emitted radiation from containers. It has been stated by experts in the field of Port Security, like Stephen Flynn, that nuclear weapons would most likely be shielded with a radiation impenetrable type of metal, making this part of the inspection useless. The accuracy of the results was based on the input data for the model. The input data came from assumptions made by experts in port security and a study done at Stanford by Wein and his graduate students. Another issue that was not considered in this project that may change the accuracy is the amount of area available for the inspection process to take place. There are several recommendations for future research as well as for the current inspection process. The first recommendation would be for a study to be done on the effects of changing the inspection process as this project recommends with the available area. Another recommended study to be done on the inspection process would be to try to find a better technology that could scan the inside of the container without the need of employees. Another recommendation would be that the ports stop using the passive inspection, and move directly to the active inspection. If there is a major concern with radiation being emitted, then there can be radiation sensors put in different locations to monitor the emissions. There should be a better targeting system for use so that a greater number of containers are being sent through the inspection process. This would increase the chances of detecting contraband in the containers. APPENDIX A: CREATION AND TARGETING IN ARENA 147

8 APPENDIX B: PASSIVE INSPECTION IN ARENA Sharpe, Tawney, White APPENDIX C: ACTIVE INSPECTION IN ARENA APPENDIX D: INTRUSIVE INSPECTION IN ARENA 148

9 APPENDIX E: DISPOSAL OF CONTAINERS REFERENCES Berman, Richard. (2003). Port Security: A Review of the Bureau of Customs and Border Protection's Targeting and Inspection Program for Sea Cargo. Retrieved from the World Wide Web: < 3hearing1140/Berman1820.htm> Flynn, Stephen. (2004). The limitations of the current cargo container targeting. Council on Foreign Relations. Retrieved Oct. 11, 2004 from the World Wide Web: < e_flynn/the_limitations_of_the_curre nt_cargo_container_targeting.php> Hecker, JayEtta Z. (2002, Nov 18). Container Security: Current Efforts to Detect Nuclear Materials, New Initiatives, and Challenges. Washington, D.C.: General Accounting Office. Retrieved Oct. 11, 2004 from the Virgo: < bin/getdoc.cgi?dbname=gao&docid=f:d0 3297t.pdf> Kroft, Steve. (2003). On the Waterfront. 60 Minutes. Retrieved Oct. 11, 2004 from the World Wide Web: < /07/25/60minutes/main shtml> Lok, Corie. (2004). Cargo security. (Point of impact: where technology collides with business and personal lives. (Interview). Technology Review (Cambridge, Mass.). Retrieved Oct.11, 2004 from the Info Trac database (Technology Review). Maritime Mobility. Travel and Transportation. Retrieved Sept. 22, 2004 from the World Wide Web: < y?theme=21&content=3380> Medalia, Jonathan. Aug. 23, Terrorist Nuclear Attacks on Seaports: Threat and Response. Retrieved Oct 20, 2004 from the World Wide Web: < nization/13840.pdf> Robinson, Colin. (2003, Jan. 1). Port and Maritime Security in the United States. Terrorism Project. Retrieved Sept. 22, 2004 from the World Wide Web: < mesecurity.cfm> SAIC. Mobile VACIS Inspection System. Retrieved Oct 18, 2004 from the World Wide Web: < ty/mobile-vacis/> Secure Seas, Open Ports. (2004, June 21). Keeping our waters safe, secure and open for business. Washington, D.C.: U.S. Department of Homeland Security. Retrieved Oct. 11, 2004 from the World Wide Web: < brary/dhsportsecurityfactsheet pdf> Sheridan, Ralph. June 4, Retrieved Oct 20, Report on Non-Intrusive Inspection Technologies from the World Wide Web: < oac/non-intrusive.pdf> Smith, Michael. Personal interview. Feb Stana, Richard M. (2004). Homeland Security: Summary of Challenges Faced in Targeting Oceangoing Cargo Containers for Inspection. Washington, DC: U.S. General Accounting Office. Retrieved Sept. 22, 2004 from the Virgo: < purl.access.gpo.gov/gpo/lps47241> Wein, Lawrence. (2004). Preventing the Importation of Illicit Nuclear Materials in Shipping Containers. Stanford University. Retrieved Nov 10, 2004 from the World Wide Web: < iner.pdf> 149

10 AUTHOR BIOGRAHIES JASON SHARPE is a 4 th year Engineering Science Major at the University of Virginia. He will graduate this May and is seeking a position where he can use his simulation expertise and his other engineering education. BARBARA TAWNEY is a Ph.D. Candidate who plans to graduate this year from the Department of Systems and Information Engineering at the University of Virginia. She is currently seeking a position where she can use her simulation, modeling and information engineering background. K. PRESTON WHITE, JR. is a Professor with the Department of Systems and Information Engineering at the University of Virginia. He is a renowned expert in simulation and modeling. 150