On designing public sector systems in emergency medical services, disaster response, and homeland security

On designing
public sector systems
Laura Albert
Industrial & Systems Engineering
University of Wisconsin-Madison
laura@engr.wisc.edu
punkrockOR.com
@lauraalbertphd
This work was in part supported by the U.S. Department of the Army under Grant Award Number W911NF-10-1-0176
and by the National Science Foundation under Award No. 1054148, 1444219, 1541165.
5 June 2019 Laura Albert at EWGLA XXV in Brussels 1

Applied optimizer who studies problems in
the public sector in the United States
Discrete optimization and location modeling in:
• Fire and emergency medical services (EMS)
• Homeland security
• Infrastructure protection / cybersecurity

One sentiment in the 1960s in the US
“If we can land a man on the moon…”
...why can't we address fundamental societal problems using
math and operations research?

A golden age of public safety research began
in the 1960s
• The President’s Commission on Law
Enforcement and the Administration of
Justice (1965)
• Al Blumstein chaired the Commission’s
Science and Technology Task Force (CMU)
• New York City / RAND Institute
Collaboration
• Between 1963 – 1968, fire alarms in NYC
increased 96% while operating expenses
remained the same.
• City planners willing to give math a try.
• New York City used simulation for the first
time!

A golden age of public safety research began
in the 1960s
• Research was put into practice
• Papers appeared in the best operations
research journals
• Research won major awards
• Lanchester, Edelman, NATO Systems
Science Prize

Early urban operations research models
Set cover / maximum cover models
How can we “cover” the maximum
number of locations with
ambulances?
Church, R., & ReVelle, C. (1974). The maximal covering
location problem. Papers in regional science, 32(1),
101-118.
Markov models
How many fire engines should we send?
Swersey, A. J. (1982). A Markovian decision model for deciding how
many fire companies to dispatch. Management Science, 28(4), 352-
365.
Analytics
How far will a fire
engine travel to a call?
Kolesar, P., & Blum, E. H.
(1973). Square root laws
for fire engine response
distances. Management
Science, 19(12), 1368-1378.
Hypercube queueing models
What is the probability that our first choice
ambulance is unavailable for this call?
Larson, R. C. (1974). A hypercube queuing model for facility location
and redistricting in urban emergency services. Computers &
Operations Research, 1(1), 67-95.

New York Police CompStat (1994)
Photo: New York Daily News Archive/NY Daily News via Getty Images

September 11, 2001
2977 total victims, including 412 fire fighters/emergency workers

Anthrax mail attacks (2001)

Hurricane Katrina (2005)

The term “big data” coined (2005)

H1N1 pandemic (2009)

Hurricane Sandy (2012)
https://obamawhitehouse.archives.gov/photos-and-video/photogallery/vice-president-
joe-biden-tours-new-jersey-after-hurricane-sandy

White House asserts importance
cybersecurity (2013)

Large public data sets (2009-2015)
Open government: data.gov (2015)

Road map
Fire and emergency
medical services
Aviation security
policy
Infrastructure protection
/ cybersecurity

Emergency medical services (EMS)
2007 – present

EMS design varies by community:
One size does not fit all
Fire and EMS vs. EMS
Paid staff vs. volunteers
Emergency medical technician
(EMT) vs. Paramedic (EMTp)
Mix of vehicles
Communities dictate
rules of operation:
Ambulance location,
relocation, and
relocation on-the-fly
Mutual aid

Ambulance services
5 June 2019 Laura Albert at EWGLA XXV in Brussels
911 call
Unit
dispatched
Unit arrives
at scene
Service/care
provided
Unit leaves
scene
Unit arrives
at hospital
Patient
transferred
Unit returns
to service
Goal: Faster
Response times
Response time from the patient perspective
19
112 in Belgium?

Ambulance dispatching must consider
tradeoffs across time and space
911 call
Unit
dispatched
Unit arrives
at scene
Service/care
provided
Unit leaves
scene
Unit arrives
at hospital
Patient
transferred
Unit returns
to service
Ambulance unavailable for other patients
Goal: Faster
Response times
Response time from the patient perspective
20
112 in Belgium?

All EMS systems use a coverage objective
function that evaluates response times
Most common response time threshold (RTT)*:
9 minutes for 80% of calls
• Easy to measure
• Intuitive
• Unambiguous
* National Fire Protection Agency (NFPA)

Response times vs. cardiac arrest survival
CDF of
actual
response
times
Response time (minutes) 9
80%
Survival decreases ~10% per minute after collapse
What the data actually look like

Response times vs. cardiac arrest survival
CDF of
actual
response
times
Response time (minutes) 9
80%
Survival decreases ~10% per minute after collapse

What is the best response time threshold?
• Guidelines suggest 9 minutes

• Medical research suggests ~5 minutes
Responses
no longer
“count”

• Medical research suggests ~5 minutes
• Which RTT is best for design of the system?

What is the best response time threshold
based on retrospective survival rates?
Decision context is locating and dispatching ALS ambulances
• Coverage model to locate ambulances
• Markov decision process model to dispatch ambulances

Ambulance Locations, N=7
Best for patient survival / 8 Minute RTT
= one ambulance
= two ambulances
Suburban area –>
(vs. rural areas)
<– Interstates

10 Minute RTT
= one ambulance
= two ambulances

5 Minute RTT
= one ambulance
= two ambulances

Survival and dispatch decisions
Across different ambulance configurations
Minimize un-survivability when altering dispatch decisions

Coverage is a good metric for evaluating
EMS performance!
How should we route ambulances to calls?
What stations should we use?
How should we design response districts
around each ambulance?

How to match vehicles to patients?
What is best for patient health?
Tiered ambulance systems
Mix of vehicles
Emergency medical
technician (EMT) vs.
Paramedic (EMTp)
33

Routing ambulances to patients in
real-time: Paramedic or EMT?
Smaller paramedic
(ALS) response
regions when more
paramedic
ambulances busy
34

Should we send multiple vehicles?
Paramedic and/or EMT?
911 call
Unit
dispatched
Unit arrives
at scene
Service/care
provided
Unit leaves
scene
Unit arrives
at hospital
Patient
transferred
Unit returns
to service
35
and/or
Patient requires EMT
(Low Priority)
EMT ambulance
Paramedic non-transport vehicle

Paramedic and/or EMT?
911 call
Unit
dispatched
Unit arrives
at scene
Service/care
provided
Unit leaves
scene
Unit arrives
at hospital
Patient
transferred
Unit returns
to service
36
Patient requires Paramedic
(High Priority)
EMT ambulance
Paramedic non-transport vehicle

Multiple response acts as a kind of triage to better match
vehicles to patients.
Vehicles are
busier
Best matches
resources to
health needs
More vehicles.
37

Should we replace an EMT ambulance
with two paramedic quick response vehicles?
Application in a real setting: 5% more high-priority calls were responded to in
less than 9 minutes without an increase in cost!
McLay, L.A., Moore, H. 2012. Hanover County Improves Its Response to Emergency Medical 911 Calls. Interfaces 42(4),
380-394.
YES!

Aviation Security
2001 - 2006

Passenger screening in the US
• 1996
• Checked baggage for high-risk
passengers screened for
explosives (run by airlines)
• Goal: use limited
baggage screening devices

Checked baggage security models were
coverage models
A flight is:
• covered if all selectee bags screened
• uncovered if 1+ selectee bags on the flight not screened
Baggage screening performance measures developed in
conjunction with the Federal Aviation Administration:
Policy implications?
Cover the most
flights
Cover the most
passengers on
covered flights
Cover flights by fully
utilizing the devices

Coverage models to the rescue!
What if you have to
• Take transferring passengers into account?
• Distribute screening capacity to airports?
• Distribute screening capacity to airports in discrete pieces?
• Consider weapons of mass destruction (WMD)?
Coverage models can answer all of these questions!

Passenger screening in the US
• November 2001 – Aviation
Transportation & Security Act
• Required all checked baggage to
be screened for explosives
• December 2001
• Remove shoes
• August 2006
• Liquids bans
• 2009 - 2010
• Explosive trace portals
• September 2012
• Less screening for seniors (75+)
and children (<12)
• December 2013
• TSA PreCheck for reduced
security

Risk-based Screening Framework
• How do match limited screening resources to passengers?
• Know everyone’s risk before they enter security screening;
allocate security resources to match risk.
• Risk-based security: Captured in the Dynamic Aviation Risk
Management System (DARMS) paradigm.
Assumptions:
• Most passengers are low-risk.
• Security resources are limited.
• Screening procedures make errors (False alarms, False
clears)

How can passengers be assigned?
Ahead of time (static models):
Integer programming models
Select the security classes to
use (location)
Assign passengers to security
classes in use (allocation)
Subject to budget or capacity
constraint(s)
In real-time (dynamic models for
allocation decisions):
Markov Decision Processes &
Control Theory models

Key policy insights
(1) Risk based screening more effective than random or
uniform screening in a resource-constrained environment.
(2) Better security is achieved by targeting scarce screening
resources at the “riskiest” passengers and doing less
screening on most passengers.
(3) TSA Precheck implicitly does this, which is why it reduces
risk in low risk, cost-constrained environments.

Infrastructure protection and
cybersecurity
2013 – present

Protecting critical information technology (IT)
infrastructure
• IT infrastructure relies on a globalized supply chain that is
vulnerable to numerous risks.
• Goal: reduce risk to critical infrastructure by identifying a
mix of security mitigations that enhance supply chain
security.
Supply chain
layer
Physical
infrastructure
layer
Designer
SupplierSupplier
Manufacturer Assembly
Distribution
Steal@A
Change@C Inject@
DESteal@B
Entry
Insertion
Points
Attack layer
Mitigations
Steal@B
Entry
Insertion
Points

Attack graph with mitigations
1
2
6 7
21
3
9
14 15 16
22
10
17 23 18
4 5
8
11 12
19 20
24
13
And node
Or node
Possible mitigations

Attack paths
1 2 6
1 2 7
1 3 9 14 15 16
1 3 10 17
1 3 10 18
1 4
1 5 13
1 5 18
1 5 8 11 12 18
1 5 8 11 12 19
1 5 8 11 12 20

Attack paths with mitigations
1 2 6
1 2 7
1 3 9 14 15 16
1 3 10 17
1 3 10 18
1 4
1 5 13
1 5 18
1 5 8 11 12 18
1 5 8 11 12 19
1 5 8 11 12 20

Models with adaptive adversaries
Attack paths
Expected coverage models
Attack graphs
Project management / interdiction
With adaptive adversaries

Final messages

Location analysis methods are applicable
to a wide range of public sector
problems.

Location models can influence policy.

The future of public sector operations
research involves coverage models

Public sector operations
research is optimistic

Questions?
Merci!
Dank ye!
Danke!
Laura Albert
University of Wisconsin-Madison
laura@engr.wisc.edu
punkrockOR.com
bracketology.engr.wisc.edu
@lauraalbertphd

References
Aviation security
1. Jacobson, S. H., J. E. Virta, L. A. McLay, J. E. Kobza, 2005. Integer Program
Models for the Deployment of Airport Baggage Screening Security Devices,
Optimization and Engineering 6(3) 339 – 359.
2. Jacobson, S. H., L. A. McLay, J. E. Kobza, J. M. Bowman, 2005. Modeling and
Analyzing Multiple Station Baggage Screening Security System
Performance, Naval Research Logistics 52(1), 30 – 45.
3. McLay, L. A., S. H. Jacobson, and J. E. Kobza, 2006. A Multilevel Passenger
Prescreening Problem for Aviation Security, Naval Research Logistics 53 (3),
183 – 197.
4. Lee, A.J., L.A. McLay, and S.H. Jacobson, 2009. Designing Aviation Security
Passenger Screening Systems using Nonlinear Control. SIAM Journal on
Control and Optimization 48(4), 2085 – 2105.
5. McLay, L. A., S. H. Jacobson, and A. G. Nikolaev, 2009. A Sequential
Stochastic Passenger Screening Problem for Aviation Security, IIE
Transactions 41(6), 575 – 591.
6. McLay, L.A., S.H. Jacobson, A.J. Lee, 2010. Risk-Based Policies for Aviation
Security Checkpoint Screening. Transportation Science 44(3), 333-349.
Infrastructure Protection
1. 6. Albert McLay, L., 2015. Discrete optimization models for
homeland security and emergency management, TutORial at the 2015
INFORMS Annual Meeting, November 1-4, 2015, Philadelphia, PA.
2. Zheng, K., Albert, L., Luedtke, J.R., Towle, E. 2019. A budgeted maximum
multiple coverage model for cybersecurity planning and management, To
appear in IISE Transactions. DOI: 10.1080/24725854.2019.1584832
3. Zheng, K., and Albert, L.A. 2019. Interdiction models for delaying
adversarial attacks against critical information technology infrastructure. To
appear in Naval Research Logistics.
Emergency Medical Services
1. McLay, L.A., 2009. A Maximum Expected Covering Location Model with
Two Types of Servers, IIE Transactions 41(8), 730 – 741.
2. McLay, L.A., 2010. Emergency Medical Service Systems that Improve
Patient Survivability. Encyclopedia of Operations Research in the area of
“Applications with Societal Impact,” eds. J.J. Cochran, L. A. Cox, Jr., P.
Keshinocak, J.C. Smith. John Wiley & Sons, Inc., Hoboken, NJ (published
online: DOI: 10.1002/9780470400531.eorms0296).
3. McLay, L.A. and M.E. Mayorga, 2010. Evaluating Emergency Medical
Service Performance Measures. Health Care Management Science 13(2),
124 – 136.
4. McLay, L.A., Mayorga, M.E., 2011. Evaluating the Impact of Performance
Goals on Dispatching Decisions in Emergency Medical Service. IIE
Transactions on Healthcare Service Engineering 1, 185 – 196
5. Ansari, S., McLay, L.A., Mayorga, M.E., 2015. A maximum expected
covering problem for locating and dispatching servers. To appear in
Transportation Science.
6. McLay, L.A., Moore, H. 2012. Hanover County Improves Its Response to
Emergency Medical 911 Calls. Interfaces 42(4), 380-394.
7. Ansari, S., McLay, L.A., Mayorga, M.E., 2015. A Maximum Expected
Covering Problem for District Design, Transportation Science 51(1), 376 –
390.
8. Grannan, B.C., Bastian, N., McLay, L.A. 2015. A Maximum Expected
Covering Problem for Locating and Dispatching Two Classes of Military
Medical Evacuation Air Assets. Operations Research Letters 9, 1511-1531.
9. Yoon, S. and Albert, L.A. 2018. Dynamic Resource Assignment for
Emergency Response with Multiple Types of Vehicles, Under review at
Operations Research, October 2018.
10. Yoon, S., and Albert, L.A. 2019. A dynamic ambulance routing model with
multiple response. Under review at Transportation Research Part E:
Logistics at Transportation Science.

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