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Modeling Alabama Tornado Emergency Relief (MATER) Joe Cordell Spencer Timmons Michael Fleischmann.

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Presentation on theme: "Modeling Alabama Tornado Emergency Relief (MATER) Joe Cordell Spencer Timmons Michael Fleischmann."— Presentation transcript:

1 Modeling Alabama Tornado Emergency Relief (MATER) Joe Cordell Spencer Timmons Michael Fleischmann

2 Overview  Background  Problem Abstract  Network Overview (Nodes, Arcs)  Mathematical Model  Scenarios  Results  Conclusions  Further Work  Video Link Video Link 2

3 Background  State of Alabama Major Cities: Birmingham, Montgomerey, Huntsville, Mobile, Tuscaloosa Population: 4.7 million  Average 23 Tornados Per Year $13 million in average annual damages 3

4 Background  Tornado Outbreak on April 27 th tornados across the United States 248 fatalities Over $16 billion in damages over 3 days Listed by NOAA as the fourth deadliest in United States history 4

5 April 27 th, 2011 – Tornados  62 Tornados in Alabama alone  2219 injuries  192 fatalities  Only the second day in history that there were three or more F5 or EF5 tornadoes. 5

6 Background  Cordova Population: 2260 Two tornados Four fatalities 6

7 Problem Abstract  Relief supply flow as a Min-Cost Flow Model  Goal: To supply damaged cities in the least amount of time and determine if prepositioning of supplies will affect total travel time  Key modifications to the basic model Randomized delay Interdiction represented by arc delays  Measures of Effectiveness: Total travel time Access to damaged cities 7

8 Nodes 8 Huntsville Birmingham Tuscaloosa

9 Arcs 9 Huntsville Birmingham Tuscaloosa

10 Abstract Network 10

11 April 27 th, 2011 – Tornados 11 We Modeled Jasper, AL Area

12 Mathematical Model 12 MIN-COST FLOW Objective: Move humanitarian supplies to damaged towns in shortest time where costs are hours of movement required to deliver supplies. There is a demand for supplies at each damaged town. MATER looks at worst case scenario by implementation of a “smart” tornado which seeks to damage roads so as to inflict the greatest cost on the operator. AirPort City t s C=0 C=20 C=24 C=5

13 Mathematical Model 13 MIN-COST FLOW Objective: Move humanitarian supplies to damaged towns in shortest time where costs are hours of movement required to deliver supplies. There is a demand for supplies at each damaged town. MATER looks at worst case scenario by implementation of a “smart” tornado which seeks to damage roads so as to inflict the greatest cost on the operator. AirPort City s C=0 C=20 C=24 t C=50 C=5 1

14 Damaged City City Node Airport Node Scenario 1a Destroyed Roads-Jasper Only 14

15 Damaged City City Node Airport Node Scenario 1b Destroyed Roads-Jasper and Blount Springs 15

16 Damaged City City Node Airport Node Scenario 1b Destroyed Roads-Jasper and Blount Springs 16

17 Damaged City City Node Airport Node Scenario 1b Destroyed Roads-Jasper and Blount Springs 17

18 Damaged City City Node Airport Node Scenario 1b Destroyed Roads-Jasper and Blount Springs 18

19 Damaged City City Node Airport Node Scenario 1b Destroyed Roads-Jasper and Blount Springs 19

20 Damaged City City Node Airport Node Scenario 1b Destroyed Roads-Jasper and Blount Springs 20

21 Damaged City City Node Airport Node Scenario 1b Destroyed Roads-Jasper and Blount Springs 21

22 Damaged City City Node Airport Node Scenario 1c Destroyed Roads-Jasper, Blount Springs and Oneonta 22

23 Damaged City City Node Airport Node 23 Scenario 1c Destroyed Roads-Jasper, Blount Springs and Oneonta

24 Damaged City City Node Airport Node 24 Scenario 1c Destroyed Roads-Jasper, Blount Springs and Oneonta

25 Damaged City City Node Airport Node 25 Scenario 1c Destroyed Roads-Jasper, Blount Springs and Oneonta

26 Damaged City City Node Airport Node 26 Scenario 1c Destroyed Roads-Jasper, Blount Springs and Oneonta

27 Damaged City City Node Airport Node 27 Scenario 1c Destroyed Roads-Jasper, Blount Springs and Oneonta

28 Damaged City City Node Airport Node 28 Scenario 1c Destroyed Roads-Jasper, Blount Springs and Oneonta

29 Damaged City City Node Airport Node 29 Scenario 1c Destroyed Roads-Jasper, Blount Springs and Oneonta

30 Damaged City City Node Airport Node 30 Scenario 1c Destroyed Roads-Jasper and Blount Springs

31 Damaged City City Node Airport Node Scenario 2c Delays Roads-Jasper, Blount Springs and Oneonta 31

32 Damaged City City Node Airport Node Scenario 2c Delays Roads-Jasper, Blount Springs and Oneonta 32

33 Damaged City City Node Airport Node Scenario 2c Delays Roads-Jasper, Blount Springs and Oneonta 33

34 Damaged City City Node Airport Node Scenario 2c Delays Roads-Jasper, Blount Springs and Oneonta 34

35 Damaged City City Node Airport Node Scenario 2c Delays Roads-Jasper, Blount Springs and Oneonta 35

36 Damaged City City Node Airport Node Scenario 2c Delays Roads-Jasper, Blount Springs and Oneonta 36

37 Damaged City City Node Airport Node Scenario 2c Delays Roads-Jasper, Blount Springs and Oneonta 37

38 Damaged City City Node Airport Node Scenario 2c Delays Roads-Jasper, Blount Springs and Oneonta 38

39 Damaged City City Node Airport Node Scenario 2c Delays Roads-Jasper, Blount Springs and Oneonta 39

40 Damaged City Prepositioned Stocks City Node Airport Node Scenario 3 Prepositioned Aid 40

41 Damaged City Prepositioned Stocks City Node Airport Node Scenario 3 Prepositioned Aid 41

42 Damaged City Prepositioned Stocks City Node Airport Node Scenario 3 Prepositioned Aid 42

43 Damaged City Prepositioned Stocks City Node Airport Node Scenario 3 Prepositioned Aid 43

44 Damaged City Prepositioned Stocks City Node Airport Node Scenario 3 Prepositioned Aid 44

45 Damaged City Prepositioned Stocks City Node Airport Node Scenario 3 Prepositioned Aid 45

46 Damaged City Prepositioned Stocks City Node Airport Node Scenario 3 Prepositioned Aid 46

47 Damaged City Prepositioned Stocks City Node Airport Node Scenario 3 Prepositioned Aid 47

48 Damaged City Prepositioned Stocks City Node Airport Node Scenario 3 Prepositioned Aid 48

49 Damaged City Prepositioned Stocks City Node Airport Node Scenario 3 Prepositioned Aid 49

50 Damaged City Prepositioned Stocks City Node Airport Node Scenario 3 Prepositioned Aid 50

51 Scenario 1: Operator Resilience Curve 51

52 52 Scenario 1 Results  With roads completely destroyed, tornado quickly cuts off access to affected area. 5 Roads knocked out cuts off Jasper from relief supplies Must then use Chinook helicopters to deliver supplies to the city, and vehicle delivery for surrounding areas affected less  Most damaging path with fewer destroyed roads is south of the city, taking out the roads from 2 of the 3 airports Supplies then flow through Huntsville Airport Main storm actually followed this path

53 Scenario 2: Operator Resilience Curve 53

54 54 Scenario 2 Results  With delayed roads, ramp-up in time is more gradual Spikes when moving across multiple delayed roads  Most damaging tornado path remains the same  No longer possible to cut off supplies to ground shipment

55 Scenario 3: Operator Resilience Curve 55

56 56 Scenario 3 Results  With supplies pre-positioned instead of flown-in, travel time is decreased, but not significantly Original flown-in supply model does not include flight time to airport Change in travel time due to proximity of prepositioned supplies to area

57 Prepositioned Supplies Comparison 57

58 Model Useability  Model is easily customizable to a given scenario Can be used to show movement of supplies to any affected city/area Scalable for multiple damaged cities via adding demands to those nodes Can use flown-in supplies or prepositioned supplies  Useful to quickly formulate delivery plan for FEMA/military responders

59 Conclusions  Depending on the city, tornado damage can quickly cut off area from relief supplies if roads are rendered unusable Helicopter delivery via US Army National Guard would then be necessary  Best option for high network resiliency is to keep road network in good repair and clear of neighboring trees  Prepositioned stocks of relief supplies would not make a large difference Must still get vehicles and personnel to distribute Not much closer than airports

60 Potential Future Work  Model entire state or other areas prone to natural disasters  Adjust model to depict hurricane or earthquake damage instead  Analyze changes in results with a more micro- resolution network (more roads, towns)

61 References  Map images and road distance maps.google.com  Past tornado path and strength data:  City statistics/demographic info:  Consolidated list of information and articles: 2011_tornado_outbreak

62 62 Questions?


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