1. San Quentin Prison Break LT Matt Mooshegian Capt Bryan Jadro

2. 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

3. Agenda Problem Statement Assumptions Network Introduction Model Introduction Summary Future Research Questions

4. 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?

5. 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

6. 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

7. Network Model (Edges) Start to Police Departments Police Departments to Checkpoints Checkpoints to End

16. s s t t SFPD Oaklan d Richmond Marin County Vallejo San Rafael El Cerrito B1 B2 B3 R1 R2 R3 R4 R5 CP1 CP2 CP3 CP4 CP5 CP6

17. 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

18. 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

19. Final Model Primal LP: min Individual officer travel time s.t.Network flow constraints Capacity constraints Lower bounds

21. 1 Attack

22. 2 Attacks

23. 3 Attacks

24. Operator Resilience Curve

26. 1 Attack

27. 2 Attacks

28. 3 Attacks

29. 4 Attacks

30. 5 Attacks

31. 6 Attacks

32. 7 Attacks

33. Operator Resilience Curve

34. 4 Attacks

35. 5 Attacks

36. 6 Attacks

37. 7 Attacks

38. 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

39. Message to Stakeholders Law Enforcement – Ensure each police department has enough units to satisfy most demanding checkpoint Prisoner/Accomplices – Focus on attacking routes to SFO

40. 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

41. Other Applications Expandable to multi-response scenarios – Fire Department response to multiple fires – EMT response to multiple accidents