Emergency Response: Planning Models

1. Emergency Response: Planning Models Reducing the Risks and Consequences of TerrorismCREATE ConferenceNovember 18, 2004Richard C. LarsonStructured Decisions Corporation andMassachusetts Institute of Technology

2. Within CREATE, Our Focus Risk Assessment Consequences Emergency Response Our focus Economics (direct/indirect)

3. Research Objectives • To understand the dynamics of emergency response to catastrophic events. • To identify key planning activities that must be addressed prior to a major emergency event. • Leveraging past research and building new research results, create one or more downloadable planning models that can aid local decision makers in optimizing their own response plans.

4. Catastrophe Any emergency event so large that local first responders are overwhelmed. Event may be an act of nature (earthquake, tornado, hurricane), industrial accident or terrorist act.

5. Planning for Catastrophes Building on OR models of urban police, fire and ambulances, we discuss modeling response to catastrophes including terrorist attacks. First responder resources are overwhelmed, so 2nd and possibly 3rd tier responders are deployed. In queueing parlance, rho (r) is significantly greater than one. We build from the Hypercube Queueing Model for spatial deployments, cut-off priority queues and related work. The goal is to provide a local decision tool to assess the quality of alternative emergency response plans.

6. This is a Work in Progress!

7. Some References Created by the Emergency Response Project Team • Decision Models for Emergency Response Planning, Richard C. Larson, a chapter to appear in The McGraw-Hill Handbook of Homeland Security, David Kamien, editor. • Also see abbreviated version of this in the current (October) issue of OR/MS Today, O.R.Models for Homeland Security: Emergency Response. • Emergency Response for Homeland Security: Lessons Learned and the Need for Analysis, Richard C. Larson, Michael D. Metzger, Michael F. Cahn, September, 2004 (draft)

8. Decision Models for Emergency Response Planning Richard C. Larson, a chapter to appear in The McGraw-Hill Handbook of Homeland Security, David Kamien, editor. Operations Research (O.R.), born in World War II (WWII), has for 65 years proved invaluable as a decision-planning tool. Known as the science and technology of decision-aiding, O.R. is an empirical science that uses the scientific method to assess the consequences of alternative decisions, be they long-term strategic planning decisions or shorter range tactical or operational decisions. Since a decision can be viewed as an allocation of resources, Operations Research is the science of resource allocation. In WWII O.R. helped guide the allocation of scarce resources against the enemy. Successful applications ranged from finding optimal locations for new and expensive radar installations in Great Britain (in order to detect incoming enemy aircraft and missiles), to the invention of ‘optimal search theory,’ used to deploy aircraft and ships in search of enemy submarines [32]. The search theory results were deemed so important that the original papers by Bernard Koopman remained classified for 15 years. Today O.R. is ideally suited for evaluating and guiding our operational strategies and actions with regard to large scale emergency incidents, be they acts of terrorism, acts of Mother Nature (e.g., earthquakes, floods, tornadoes, hurricanes) or industrial accidents. Following WWII, Operations Research found widespread applications in civilian sectors, both in private companies and in the nonmilitary government sectors. The collective result has been savings of billions of dollars from costs of operation and significant increases in the quality of services provided – in both the public and private sectors. The majority of Fortune 500 companies have utilized O.R. inside to help them in their decision making, long, medium and short term. The O.R. civilian sector includes the US Postal Service, which for decades has used O.R. extensively for designing routes, scheduling personnel and designing its national distribution network. It also includes the City of New York, which as a result of 30 years of successful O.R. experience, created its own permanent O.R. group within the City’s Office of Management and Budget. And the military has its own Military Operations Research Society, whose 3,000 members must have a security clearance to attend its meetings.

10. As a nation, we are beginning to develop a collective science and technology of resource allocation in a homeland security setting.

11. The Need • Plans for integrated three-wave response: • Local, Regional and Federal planners and decision makers need quantitative tools to design and operate emergency response systems, especially in times of crisis. Local systems will be overwhelmed. • Key to the success of a coordinated response is up-front planning. • New methodology creates coherent and effective allocation of resources. • Identifies potential inter-agency incompatibilities and suggests ways to fix them.

12. Learning from Experience: Studying Past Catastrophes http://www.itu.int/newsarchive/wtd/1997/photos/940047.jpg

13. Oklahoma City Bombing http://dart2.arc.nasa.gov/Deployments/OklahomaCityBombing1995/images/Oklahoma-12a.jpg

14. United Airlines Flight 232 http://www.airdisaster.com/special/special-ua232.shtml; http://www.airodyssey.net/graph/ual232rear.jpg

15. Tokyo Subway Sarin Attack http://www.cnn.com/WORLD/9608/01/japan.oly.security/link.tokyo.attack.jpg

16. Bhopal Gas Tragedy www.m-web.com/ bhopal2photos.html

17. Hurricane Floyd www2.ncsu.edu/.../research/ nws/cases/19990915/ ; http://www.tldm.org/news/447149.jpg

18. Hurricane Charlie http://www.amsat.org/amsat-new/lab/

19. Lessons Learned for Decision Support • Pre-positioning of Supplies and Equipment • 911 Inference Algorithms • The Evacuation Decision • Triage • Second- and Third-Tier Responders • Use of Volunteers and Off-Duty Personnel • Near-the-Scene Logistics (for Personnel and Donated Goods) • Handling of Routine 911 Calls from the Rest of the City • Reducing Traffic Congestion on Telephones and Radios

20. Ours is a Model-Based Planning System The Hypercube Queueing Model is a First-Level Building Block

21. The Hypercube Queueing Model: • Has been implemented in many communities • Generated many graduate theses and journal articles • Reduced via approximation a system with 2N simultaneous linear equations to a set of N simultaneous nonlinear equations • Incorporates a locate-allocate heuristic to locate optimally ambulances • Is a basis for our new CREATE work in emergency response to major catastrophes, such as acts of nature, industrial accidents or terrorist attacks. • New generalized version will be available for demo next month http://www.cs.virginia.edu/~mngroup/hypercast/images/hcube.gif

22. Hypercube Queueing Model 1,1,0 1,1,1 0,1,0 0,1,1 1,0,0 1,0,1 0,0,0 0,0,1

23. http://www.ci.houston.tx.us/hfd/general/images/ambulance.gif

29. Issues for Discussion • Customer expectations and requirements • What customer wants – a model-based system to assess preparedness for disastrous events… suggested areas for improvement. We are visiting and communicating with prospective customers. • Available training in use of product • Need focus groups and other hands-on ways of determining customer needs • Do we want to include non-model-based checklist information, such as for radio frequency compatibility?

30. Issues for Discussion • Building from the case studies, can we anticipate plausible scenarios and the ‘physics’ of events. • Can we obtain detailed histories of the responses to other catastrophic events? • Can we classify events (e.g., focused location vs. city-wide, bio, radiation, explosive, etc.) What are the appropriate event attributes? • Assumptions about surviving infrastructure (esp. transportation and electricity)

31. Issues for Discussion • Possibility of ‘layered set of models,” where every user acquires and uses aggregate layer #1, and only advanced users move to deeper level layers. In this possibility, the data-intensive and generalized Hypercube model may become a level 2 or 3 option. • Leveraging from the case studies, possible areas of modeling scope expansion: • Triage (policy and location) • Evacuation policy and decision • Facilities location (CREATE research work already underway, Maged Dessouky and Fernando Ordonez) • Nonstandard personnel (e.g., volunteer citizens) • Congestion of communications systems 1 2 3 4 5

32. Conclusions • Model-based planning for emergency response is an important need. • Quantitative models are the only tools that can be made generic across jurisdictions. • By studying past major events and speaking with planners, we have identified a first-pass set of user needs. • With the Hypercube model as an initial building block, we are well on our way to creating the desired planning tools.