San Q uentin Prison Break
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Transcript of San Q uentin Prison Break
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San Quentin Prison Break
LT Matt MooshegianCapt Bryan Jadro
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Background
• November 2012 SFPD arrest notorious drug kingpin Jose “El Torro” Velasquez.
• Velasquez is sent to San Quentin Prison while the U.S and Mexico begin extradition talks.
• Prison officials fear an escape attempt
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Agenda• Problem Statement• Assumptions• Network Introduction• Model Introduction• Summary• Future Research• Questions
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Problem Statement
• Analyze the current configuration of police roadblocks in order to determine which ones possess the greatest risk of facilitating a high profile prisoners escape.
-How will attacks on the network affect the police’s ability to meet their goal of establishing all checkpoints within 60 minutes?
-How many attacks are necessary to significantly impact response times?
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Assumptions
• Police force available is proportional to size of respective police department
• 1 police unit consists of one police officer and 1 police car• Demand at checkpoints are predetermined• Once informed, all police stations respond at an equal rate• No lag time from dispatch to the deployment of the police
force• Police move at speed limit• Traffic is not a factor in time to reach checkpoint
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Network Model (Nodes)
• Police Departments (7)– San Francisco PD– Oakland PD– El Cerrito PD– Richmond PD– Marin County PD– Vallejo PD– San Rafael PD
• Checkpoints (14)– 3 Bridges– 5 Roadblock Locations– 6 Checkpoint Locations
• Start Node• End Node
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Network Model (Edges)
• Start to Police Departments• Police Departments to Checkpoints• Checkpoints to End
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s t
SFPD
Oakland
Richmond
Marin County
Vallejo
San Rafael
El Cerrito
B1
B2
B3
R1
R2
R3
R4
R5
CP1
CP2
CP3
CP4
CP5
CP6
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Min-Cost or Multi-Commodity Flow
• First tackled problem as a min-cost problem– GAMS output was in in total time– Did not provide the insight we desired
• Re-analyzed as a multi-commodity flow problem where each police officer represents a different commodity
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Multi-Commodity Flow
• Purpose: Minimize response times for surrounding police departments to establish a network of checkpoints
• Each police officer is a different commodity– Want to determine which police officer
(commodity) is taking the longest to reach their checkpoint
– Subsequently determine the number and location of attacks to break network
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Final Model
• Primal LP:min Individual officer travel time
s.t. Network flow constraintsCapacity constraintsLower bounds
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1 Attack
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2 Attacks
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3 Attacks
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Operator Resilience Curve
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1 Attack
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2 Attacks
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3 Attacks
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4 Attacks
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5 Attacks
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6 Attacks
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7 Attacks
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Operator Resilience Curve
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4 Attacks
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5 Attacks
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6 Attacks
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7 Attacks
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Conclusions
• Network is highly susceptible to failure with a minimal number of attacks and a concentration of police units at 1 or 2 departments
• Attacks center on police departments with the most manpower– SFPD and Oakland PD
• Remaining police departments contribute an insufficient number of police officers to handle the checkpoints
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Message to Stakeholders
• Law Enforcement– Ensure each police department has enough units
to satisfy most demanding checkpointPrisoner/Accomplices– Focus on attacking routes to SFO
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Future Research
• Increase level of network granularity– More police stations and more checkpoints
• Better estimates of the number of police units available from each police department and requirements per checkpoint
• Consideration for prisoner movements• Mobile dispatch/command centers
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Other Applications
• Expandable to multi-response scenarios– Fire Department response to multiple fires– EMT response to multiple accidents