evaluating the consequences of an inland waterway port closure with a dynamic ... 101201... ·...
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Evaluating the Consequences of an Inland Waterway Port Closure with a
Dynamic Multiregional Interdependency Model
Cameron MacKenzie and Kash Barker
School of Industrial Engineering
University of Oklahoma
Society for Risk Analysis Annual Meeting
December 6, 2010
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Motivation
• 2.5 billion tons of commerce via water annually
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What’s new
• Focusing on inland waterway ports
• Combining simulation with multiregional input-output model
• Incorporating companies’ decision-making process into simulation
• Integrating publicly available databases for a case study examining effects of closing an Oklahoma port
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Outline
1. Simulation
2. Multiregional Dynamic Inoperability Input-Output Model (DIIM)
3. Port of Catoosa case study
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Simulation + model
Port officials announce or
revise expected opening
Each company updates
probability of expected
opening of port
Ship via alternate
route?
No adverse economic effects
Port opens?
Commodities not yet
shipped flow through port
No
Yes
Yes
No
Port suddenly
closes
Multiregional DIIM
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Ship now or wait for port to open?
Cost of shipping via alternate route
Cost of shipping via port
Expected penalty cost from waiting
Premium company willing to pay to ensure on-time delivery
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Ship now or wait for port to open?
Cost of shipping via alternate route
Cost of shipping via port
Expected penalty cost from waiting
Premium company willing to pay to ensure on-time delivery
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• Companies needing those commodities suffer supply shortages
If company chooses to wait
Commodities not exported Commodities not imported
Multiregional DIIM
Loss of production
• Effect is equivalent to reducing demand for those commodities
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Multiregional Dynamic Inoperability Input-Output Model (DIIM)
)()()()1( *** tttt cTqATKqKIq *
np x np matrix describing interdependencies among industries
np x 1 vector describing production loss of each industry
np x 1 vector describing reduction in customer demand for each industry at time t
n industries per region, p regions
np x np diagonal matrix describing how quickly perturbations reverberate through economy
np x np matrix describing interdependencies among regions
Ref: Lian and Haimes 2006 Crowther and Haimes 2010
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Port of Catoosa case study
• McClellan-Kerr Arkansas River
• 2 million tons of cargo
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Transportation hub
• 3 different rail lines
• 500 – 1000 trucks per day
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Catoosa daily schedule
• Create daily schedule of shipments through Catoosa
• Combine publicly available databases
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Food and
Beverage
and
Tobacco
Products
Petro-
leum and
Coal
Products
Chemical
Products
Non-
metallic
Mineral
Products
Primary
Metals
Fabri-
cated
Metal
Products
Machi-
nery
Misc.
Manu-
facturing Total
Ala. 9 9
Ill. 3 3
Kent. 18 18
Louis. 131 49 30 210
Miss. 71 71
Tex. 8 78 6 92
Ala. 165 38 203
Ark. 1 1
Ill. 1 2 4
Iowa 2 2
Louis. 3 9 131 93 21 257
Miss. 2 2
Ohio 55 12 67
146 66 223 4 313 71 108 6 937
From
Okla.
To
Okla.
INDUSTRY
Total
Value of products through Catoosa
(in millions of dollars)
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Key assumptions
• Each shipment < 9000 tons (six barges)
• Railroad is alternate route ▫ A little less than 3 times
more expensive than barge ▫ No capacity constraints
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State Mean
Standard
Deviation
Alabama 68 31
Arkansas 61 28
Illinois 116 70
Iowa 32 14
Kentucky 60 32
Louisiana 798 391
Mississippi 277 135
Ohio 132 60
Oklahoma 2,993 1,449
Texas 525 277
Total 5,061 2,206
Industry MeanStandard
deviation
Food and beverage and
tobacco products 17 7
Petroleum and coal
products 8 4
Chemical products 26 10
Nonmetallic mineral
products 0.5 0.4
Primary metals 37 14
Fabricated metal
products 8 4
Machinery 13 14Misc. manufacturing 1 2
Total 110 36
Results: no penalty In millions of dollars
Value of product not transported while port is closed
Production loss per state due to interdependencies
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Distribution of production losses
0 5 10 150
50
100
150
200
Billions of dollars
Fre
qu
en
cy
0 5 10 150
50
100
150
200
Billions of dollars
Fre
qu
en
cy
Oklahoma’s lost production Region’s lost production
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State MeanStandard
deviation
Alabama 7 6
Arkansas 5 4
Illinois 27 36
Iowa 3 2
Kentucky 6 6
Louisiana 137 131
Mississippi 23 34
Ohio 10 8
Oklahoma 218 234
Texas 29 25
Total 465 361
Industry MeanStandard
deviation
Food and beverage and
tobacco products 3.9 2.8
Petroleum and coal
products 1 1
Chemical products 4 3
Nonmetallic mineral
products 0.2 0.3
Primary metals 2 3
Fabricated metal
products 0.2 0.9
Machinery 0 0
Misc. manufacturing 0 0
Total 12 6
Results: 0.2% penalty In millions of dollars
Value of product not transported while port is closed
Production loss per state due to interdependencies
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Distribution of production losses
0 1 2 30
100
200
300
400
500
600
700
800
Billions of dollars
Fre
qu
en
cy
0 1 2 30
100
200
300
400
500
600
700
800
Billions of dollars
Fre
qu
en
cy
Oklahoma’s lost production Region’s lost production
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Impact of penalty
0% 0.1% 0.2% 0.3% 0.4% 0.5% 0.6% 0.7% 0.8% 0.9% 1.0%10
-1
100
101
102
103
104
Penalty
Mill
ion
s o
f d
olla
rs
Oklahoma's lost production
Region's lost production
Extra transportation cost paid by companies
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0 1 20
200Port closed Jan 1
0 1 20
200
Port closed Feb 1
0 1 20
200
Port closed Mar 1
0 1 20
200Port closed Apr 1
0 1 20
200
Port closed May 1
0 1 20
200Port closed Jun 1
0 1 20
200
Port closed Jul 1
0 1 20
200Port closed Aug 1
0 1 20
200
Port closed Sep 1
0 1 20
200Port closed Oct 1
0 1 20
200Port closed Nov 1
0 1 20
200Port closed Dec 1
Temporal impact (with 0.2% penalty)
Fre
qu
en
cy
Region’s production loss (billions of dollars)
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Conclusions
• Model – Integrating simulation with Multiregional DIIM
provides powerful analytical tool
– Incorporating companies’ reactive strategies to port closures delivers a more complete picture of consequences
• Catoosa case study – If commodities sit at port, losses around $5 billion
– If 90% of commodities move before port reopens, losses around $460 million
– Policymakers may want to incentivize companies to move commodities before port reopens
21
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This work was supported by
• The U.S. Federal Highway Administration under awards SAFTEA-LU 1934 and SAFTEA-LU 1702
• The National Science Foundation, Division of Civil, Mechanical, and Manufacturing Innovation, under award 0927299
Email: [email protected]
Backup
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Daily inoperability for Oklahoma and Louisiana with no penalty
-0.1
0
0.1
0.2
0.3
Ino
pe
rab
ility
Oklahoma sectors
0 10 20 30 40 50 60-.1
0
.1
.2
.3
Days Catoosa is closed
Louisiana sectors
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Daily inoperability for Oklahoma and Louisiana with 0.2% penalty
-.02
0
0.02
Ino
pe
rab
ility
Oklahoma sectors
0 10 20 30 40 50 60.02
0
0.02
Days Catoosa is closed
Louisiana sectors
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References
• C. Lian and Y.Y. Haimes. 2006. “Managing the Risk of Terrorism to Interdependent Infrastructure Systems through the Dynamic Inoperability Input-Output Model.” Systems Engineering 9 (3): 241-258.
• K.G. Crowther and Y.Y. Haimes. 2010. “Development of the Multiregional Inoperability Input-Output Model (MRIIM) for Spatial Explicitness in Preparedness of Interdependent Regions.” Systems Engineering 13 (1): 28-46.