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Systems Engineering for the Transportation Critical Infrastructure The Development of a Methodology and Mathematical Model for Assessing the Impacts of K Links Disconnects have on Defined Links of the Network. Terms and Definitions. Critical Infrastructure (CI) System Transportation CI - PowerPoint PPT Presentation
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Department of EMISSMU School of Engineering
Leadership in Engineering
Systems Engineering for the Transportation Critical
InfrastructureThe Development of a Methodology and
Mathematical Model for Assessing the Impacts of K Links Disconnects have on Defined Links of the
Network
4Department of EMIS
SMU School of Engineering
Leadership in Engineering
• Critical Infrastructure (CI)
• System
• Transportation CI
• System of Systems (SoS)
• Major Cities
• City Boundary
• Network
Terms and Definitions
5Department of EMIS
SMU School of Engineering
Leadership in Engineering
• Movement of Goods
• Trucks
• Peak Traffic
• Normal Traffic
• Other Traffic
• Days of Operation
Terms and Definitions
6Department of EMIS
SMU School of Engineering
Leadership in Engineering
• Node• Arc Link• Disconnect• Steady State• Highway • Defined Links• Worst Link• Best Link
Terms and Definitions
7Department of EMIS
SMU School of Engineering
Leadership in Engineering
Objective
• The objective of this dissertation is to develop a methodology, using a SE approach, and apply the methodology to develop a mathematical model, using performance metrics such as travel time and flow, to simulate the impacts K Links disconnects have on highway networks of major metropolitan cities
8Department of EMIS
SMU School of Engineering
Leadership in Engineering
Objective
– Two Objective Steps
1. Systems Engineering Approach
2. K Links with Highest Affect on Network
9Department of EMIS
SMU School of Engineering
Leadership in Engineering
Research Significance
• Contribution: This dissertation provides officials a decision-making methodology and tool for resource allocation and risk mitigation– Metrics that measure the performance of the
network given disconnects occurring– Ranking of K Links affecting the network the most
10Department of EMIS
SMU School of Engineering
Leadership in Engineering
Research Significance
• Decision Making Methodology and Tool
i, j
11Department of EMIS
SMU School of Engineering
Leadership in Engineering
Research Significance
• Algorithm for finding efficiently the K Links with the greatest impact on the network
Minutes
Acc
urac
y
Accuracy Vs. Time
12Department of EMIS
SMU School of Engineering
Leadership in Engineering
Brief Literature Review
• SE– Osmundson et al, The Journal of The International Council on Systems
Engineering (INCOSE), 2004
– Tahan et al, The Journal of The INCOSE, 2005
– Bahill et al, The Journal of The INCOSE, 2005
– Blanchard et al, “Stems Engineering and Analysis”, 1990
– INCOSE, “Systems Engineering Handbook”, 2004
– Hazelrigg, “Sys. Eng.: An Approach to Information-Based Design” 1996
– Miller et al, “Systems Engineering Management”, 2002
– Stock et al, “Strategic Logistics Management”, 1993
– Ibarra et al, Conference for Systems Engineering, 2005
– Blanchard, “Logistics Engineering and Management”, 2004
– US Department of Homeland Security, “Budget in Brief, Fiscal Year 2005”
13Department of EMIS
SMU School of Engineering
Leadership in Engineering
Brief Literature Review
• Modeling– Osmundson et al, The Journal of The International Council on Systems
Engineering (INCOSE), 2004
– Bahill et al, The Journal of The INCOSE, 2005
– Sathe et al, Transportation Research Board, 2005
– Jain et al, Transportation Science, 1997
– Arroyo et al, Transportation Research Board, 2005
– Rardin, “Optimizations in Operations Research”, 1998
– Rinaldi et al, IEEE Control System Magazine. 2001
– Murray-Tuite, Dissertation, 2003
14Department of EMIS
SMU School of Engineering
Leadership in Engineering
The Systems Engineering Process• Defining the System – System of Systems
AgricultureWater
Public Health
EmergencyServices
DefenseIndustrial
Base
Telecom.
EnergyTransportation
Government
Chemical andHazMat
Postal andShipping
Banking andFinance
FoodAgriculture
Water
Public Health
EmergencyServices
DefenseIndustrial
Base
Telecom.
EnergyTransportation
Government
Chemical andHazMat
Postal andShipping
Banking andFinance
Food
15Department of EMIS
SMU School of Engineering
Leadership in Engineering
The Systems Engineering Process
• Need Analysis
• Stakeholders• City• State and Federal• Business• Society (Indirectly)
16Department of EMIS
SMU School of Engineering
Leadership in Engineering
The Systems Engineering Process
• Requirements– Mission Definition– Performance and Physical Parameters– Use Requirements
17Department of EMIS
SMU School of Engineering
Leadership in Engineering
The Systems Engineering ProcessC
ompo
nent
s
• Transportation CI SoS
INPUT•Disconnects•Hrs of Op.
PROCESS•Mathematical model
Att
ribu
tes
•Flow•Distance
•Links •Nodes•Efficiency of model
RelationshipsMovement of Goods
Efficiently Finding K Links
Perf. of Defined
Links
OUTPUT•Performance
•Disconnects•Hours of operation
18Department of EMIS
SMU School of Engineering
Leadership in Engineering
The Systems Engineering Process
• Ground Rules and Assumptions – Highway– Major Cities– Steady State
• Non-Event Days
• Construction established and on-going
• Mon – Fri
– Disconnect
19Department of EMIS
SMU School of Engineering
Leadership in Engineering
The Systems Engineering Process
• Metrics– Performance of Network
• Travel Time
• Throughput
– Solution – Processing Time of Model (as a function of OD table and network topology)
(OD)
Links
Model /Algorithm
Time
Accuracy
20Department of EMIS
SMU School of Engineering
Leadership in Engineering
The Systems Engineering ProcessSystem
Requirements
SystemSolution
Validate &Verify
Actual Model
System Objective
City Boundary
Section of City
Small Network Enumeration
EnumerationProcessing Time
Functional Analysis
EnumerationProcessing Time
21Department of EMIS
SMU School of Engineering
Leadership in Engineering
Model
• Most naive process– Disconnect Link (Li,j) subject to Time (tn)
– Simulate Network Performance
– Connect Link (Li,j)
– Repeat until all links tested
22Department of EMIS
SMU School of Engineering
Leadership in Engineering
Model
• Objective– Performance of Network based on Defined Links
• Constraints– Mathematical model of how the system responds
to changes in variables
• Variables– Time of Day– Disconnected Links
23Department of EMIS
SMU School of Engineering
Leadership in Engineering
Example of Model
Time
1
2
a
i
b c
3
4
6
5
3
4
8
6
4
3
O D Matrix3 4
1 200 1002 200 200
Number of Vehicles traveling from Origin to Destination during Off-Peak Period
24Department of EMIS
SMU School of Engineering
Leadership in Engineering
1
2
a
i
b c
3
4
6, 300
5, 4004, 250
8, 450
6, 700
4, 400
3, 300
3, 450
Example of Model: Routing Assignment
Time, Flow
a i b c 3 4 a i b c 3 41 300 1 62 400 2 5a 450 250 a 3 4i 450 i 8b 700 b 6c 400 300 c 4 3
Flow = Veh / Hr Travel Time = Minutesq t
25Department of EMIS
SMU School of Engineering
Leadership in Engineering
1
2
a
i
b c
3
4
6, 300
5, 400
8, 700
6, 700
4, 400
3, 300
3, 700
Example of Model: Effects of Disconnect on Link (a,b)
Time, Flow
qa i b c 3 4
1 3002 400a 700 0i 700b 700c 400 300
Flow = Veh / Hr 1,3 = {1,a a,i i,b b,c c,3} = 271,3 = {1,a a,b b,c c,3} = 201,4 = {1,a a,i i,b b,c c,4} = 261,4 = {1,a a,b b,c c,4} = 192,4 = {1,a a,i i,b b,c c,3} = 262,3 = {1,a a,b b,c c,3} = 192,4 = {1,a a,i i,b b,c c,4} = 252,4 = {1,a a,b b,c c,4} = 18
Avg. T = 2.5Min/Veh
26Department of EMIS
SMU School of Engineering
Leadership in Engineering
Example of Model
1
2
a
i
b c
3
4
6, 300
5, 4006, 700
4, 400
3, 300
1
2
a
i
b c
3
4
6, 300
5, 400
8, 4503, 450
4, 700
1
2
a
i
b c
3
4
6, 300
5, 4006, 700
4, 400
3, 3004, 700
4, 250
27Department of EMIS
SMU School of Engineering
Leadership in Engineering
0.0
100.0
200.0
300.0
400.0
500.0
System
System 412.2 268.0 479.6 383.8 402.5
Link a Link b Link c Link d Link e
DefinedLinks Link a Link b Link c Link d Link eLink 1 17.2 25.1 35.0 72.0 19.1Link 2 74.0 36.3 93.4 19.8 15.6Link 3 22.2 17.4 28.8 0.5 97.4Link 4 37.1 74.2 32.0 29.7 28.0Link 5 90.7 9.3 95.5 98.1 60.7Link 6 28.9 32.9 82.7 61.7 54.8Link 7 75.1 23.1 1.2 14.9 13.2Link 8 43.1 33.8 64.5 18.4 60.3Link 9 23.9 16.0 46.4 68.9 53.4System 412.2 268.0 479.6 383.8 402.5
Links in Network
Example of Model: Performance for a General Metric
OUTPUTS
Sum of Performance
, …,
28Department of EMIS
SMU School of Engineering
Leadership in Engineering
0.0
50.0
100.0
150.0
200.0
250.0
300.0
350.0
400.0
450.0
500.0
(2, 11) (1, 11) (2, 12) (3, 14) (1, 12) (4, 7) (5, 6) (3, 8) (4, 8) (2, 5) (3, 8) (1, 2) (3, 5) (2, 4) (4, 5) (5, 8)
Example of Model
Links
Perf
orm
ance
Worst
Best
OUTPUTS
0 is threshold
K Links = {2,11}, …, {1,12}affecting the TransportationCI the most
29Department of EMIS
SMU School of Engineering
Leadership in Engineering
OutputPerformance:•Travel Time/Throughput
I35W I35E I45
I35W I35E Hwy 75
I20
I30
I20
InputSingle Disconnect; 1/0
Variables•Temporal Time of Day: I =1, 2, 3 (peak, norm, other)•Links: l =(i,j), [(i+1), (j+1)],…, (i+n, j+n)
L1 L2 L3
L8 L7 L6
L5
L4
L9
Information Flow
I=1
I=1
Network
30Department of EMIS
SMU School of Engineering
Leadership in Engineering
• Restricting the Search Space– Find least reliable links
– Find largest/lightest flow
• Approximation Methods– “Quickly” find “Good” solution
Ideas for Improving Algorithmic Model Efficiencies
1
2
a
i
b c
3
4
6, 300
5, 4004, 250
8, 450
6, 700
4, 400
3, 300
3, 450
31Department of EMIS
SMU School of Engineering
Leadership in Engineering
Validation and Verification
• SE Approach– Integrations Process– V-Chart
• Model– Small Network– Enumeration– Efficiency of Model
32Department of EMIS
SMU School of Engineering
Leadership in Engineering
Conclusion
• Transportation CI is important– To individuals’ way of life – To companies’ way of doing business
• Proposed a Methodology and Mathematical Model to Determine Impact of K Links Disconnects have on the Defined Links of a Network
33Department of EMIS
SMU School of Engineering
Leadership in Engineering
Conclusion
• Research Significance– Society: A Methodology and Tool for Officials to
use in the Decision Making Process– Engineering: A New Algorithm for Solving
Complex Systems Efficiently