Multi-Hazard Supply Chain Network
Management: Assessment of Modeling
Elements and Data Requirements for Preand Post- Disaster Restoration
Dr. Suzanna Long, Missouri S&T
Dr. Tom Shoberg, U.S. Geological Survey
Dr. Steven Corns, Missouri S&T
Dr. Héctor Carlo, University of Puerto Rico at Mayaguez
Project #G13AC00028
June 26, 2013
Students Involved
• Varun Ramachandran, PhD Student, Engineering
Management, Missouri S&T
• Wilson Alvarez, Graduate Student, Industrial
Engineering, University of Puerto Rico at Mayaguez
• Alejandro Vigo, Undergraduate Student, Industrial
Engineering, University of Puerto Rico at Mayaguez
• Victor David, Undergraduate Student, Industrial
Engineering, University of Puerto Rico at Mayaguez
• Lizzette Pérez, PhD Student, Engineering
Management, Missouri S&T
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Aim:
To approach SCSI restoration as a complex adaptive systems problem
and define the necessary model elements, data needs/element,
component interdependencies and metrics for success.
Resiliency and scalability are then incorporated into a multi-hazard
management decision-making tool.
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Emergency Response
• Short Term: Focus on people
– Rescue & Recovery (FEMA)
– Command & Control (DHS)
• Long Term: Focus on infrastructure
– Scalability
– Resiliency
– Sustainability
4
Gap Analysis
• Medium- to long-term Supply Chain Strategic
Infrastructure (SCSI) recovery after extreme event
disruption has yet to be adequately modeled.
• Interdependencies between SCSI elements are not
well mapped.
• Much infrastructure data are proprietary and, as
such are difficult to acquire.
• Decision making and handoffs between private and
public entities lead to restoration bottlenecks.
• No single approach exists to model SCSI recovery
from planning stage to restoration stage.
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Uniqueness of Approach
• Integration of geospatial and supply
chain data.
• Model Based Systems Engineering
(MBSE) and Complex Adaptive
Systems (CAS) to account for the
different elements of the supply chain
network.
• Model considers:
– Scalability
– Resiliency
– Sustainability
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Previous Results
• Used Combinatorial Graph Theory.
• Feasibility analysis of the model against an
actual EF-5 tornado.
• Development of Priority Restoration Matrix.
Infrastructure
Model
Joplin
Difference
Communication
10 days
10 days
Same as Joplin
Electricity
15.5 days
14 days
+1.5 days
Local Transportation
10 days total major
90% major roads +1 day
Network
roads 5-6 days
cleared.
Water Pipelines
8 days
10 days
Resiliency
22.5 +/- 2.5 days
24.5 +/- 3.5 days +/- 2 days
Lines
-2 days
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Project Goals
• Integration of information to make a comprehensive
MBSE model.
• Develop a framework of how sub-system level
resilience metric can be used to calculate resiliency
time and cost to get sub-systems back to pre-event
levels.
• Make scalable models that span multiple elements
of the supply chain network across several regions
in response to a variety of possible extreme events.
• Improve decision-making algorithms to approach
optimal restoration associated with the destruction
caused by an extreme event.
8
Tools to Approach the Project
• Model Based Systems Engineering
(MBSE)
• Complex Adaptive Systems (CAS)
• Agent Based Modeling
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What is Model Based Systems
Engineering?
• Away of doing systems engineering
using models
• The shift from a document centric
systems engineering paradigm to a
model centric (Friedenthal)
• New topic and still evolving
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Source: INCOSE MBSE Initiative – Mark Sampson
Vision:
Integrated systems-oriented decision support…
Power
Rating:
18 Amps
Hydraulic
Fluid: SAE
1340 notcompliant
Sensor
MTBF:
3000 hrs
Minimum Turn Radius: 24 ft.
Dry Pavement Braking Distance at
60
MPH
: 110
ft. ft. 90 ft
at 60
MPH
: 110
Thermal/Heat
Dissipation:
780°
Ergonomic/Pe
dal Feedback:
34 ERGS
Hydraulic
Pressure: 350
PSI
Automatic
Cruise Control
<FAULT>
Complex Adaptive Systems
• Complex Adaptive Systems are dynamic
systems that may represent cells, species,
individuals, nations, constantly acting and
reacting to what the other entities around
them are doing.
• Because extreme event SCN restoration
involves a large number of coupled, dynamic
sub-systems, the reconstruction effort must
be approached as a complex adaptive
system.
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Complex Adaptive Systems
Changing
external
environment
System
Feedback
Changing
external
environment
Changing
external
environment
Feedback
Emergent
Behavior
Changing
external
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environment
Combining Information from Multiple
Models
Freight
capacity Data
Infrastructure
Data
Geospatial
Data
Disaster
Disaster type:
type: Tornado
Earthquake
Restore
supply
Restore supply chain
chain to
to 80%
80% prepreevent
capacity.
event capacity.
Restoration
Data
MBSE Model
Location
Data
Transportation
Data
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Agent-based Modeling
ABM is defined as decentralized,
non- system level approach to
model design. The agents or active
components need to identify
component behavior and an
environment must be defined
which establishes connections for
the simulation.
Characteristics of Agents:
• Identifiable and discrete.
• Rules for interactions with
environment
• Goal-directed.
• Flexible.
• Learns from surroundings.
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SCSI Modeling
A system is comprised of:
• Nodes (with a variety of “types”)
• Links or “connections” to other nodes (with a variety
of “modes”)
• Local rules for Nodal and Link behavior
• Local Adaptation of Behavioral Rules
• “Global” forcing from Policy
Connect nodes appropriately to form a system
(network)
Critical Infrastructures are to be modeled as Complex systems
because they are composed of many parts whose interaction
yields emergent structure (networks) and behavior (cascades),
they grow and adapt in response to policy and contain people
which makes the behavior unpredictable
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Model Framework
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Model Layout
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Sub-System Model
• A critical infrastructure model is constructed
from three key data sources.
– The National Map (TNM) of the U.S.
Geological Survey – Geospatial data
– Infrastructure elements derived from these
data.
– Missouri and Illinois departments of
transportation.
• These are integrated with transportation
capacity data from the U. S. Department of
Commerce to model the flow of goods and
services through the urban center
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Data Requirements
• Lack of centralized data repository
for comprehensive analysis of
critical infrastructures.
• Data needed for this research:
Geospatial Data with geographic and Transportation data (road, air, rail,
elevation data
and sea)
Hydrographic models of water
basins
Infrastructure interdependency data
Regional nodes of critical
infrastructure
Real-time data and not static data
Hazard Data e.g. geo-seismic data
Social, administrative, economical
data of a the region under
consideration
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Data Acquisition - Transportation
Category
Commodity Freight
Manufactured
Goods
Raw Materials
Data Ownership Difficulties with data
Type
Freight Data
Data Description
Food, Agriculture, Paper etc.
Electronics, Machinery, Vehicles etc.
Tons
Tons
Coal, Fuel, Chemicals etc.
Tons
Public
Private/
Public
Private/
Public
1)Static data;
2)Generalized data;
3) Proprietary data
Private/
Public
Private
Private
Private/
Public
Private/
Public
1)Inconsistency;
2)Estimation maybe
required;
3)Private-Public
ownership
Freight Flow Data
Road
Transportation
Rail Transportation
Air Transportation
Water
Transportation
Pipeline
Transportation
Goods Transported by Road
Tons
Goods Transported by Rail
Goods Transported by Air
Goods Transported by Water
Tons
Tons
Tons
Goods Transported by Pipeline
Tons
Infrastructure Capacity Data
Road- Hub
Rail-Hub
Water-Hub
Bulk, General Cargo, Containers
Bulk, Break Bulk, Intermodal, Shunting, etc.
Rail Car Storage, Dry Storage, Liquid Storage
etc.
Tons
Tons
Tons/
Bushels
Private
Private
Private
1) Varied amount of data
required;
2) Different capabilities of
hubs;
3) Interdependency of data
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Data Acquisition - Transportation
Category
Data
Difficulties with
Ownership
Type
data
Infrastructure Location Data/Geospatial Data
Data Description
Hub Location
Number of hubs in the area
Number
Private
Utility Location
Location of all utilities that aid in freight flow
Number
Private/Public
Road & Bridge
Location
Location of infrastructure that aids in road
transportation
Number
Public
Airport Location
Location of infrastructure that aids in air transportation
Number
Private
Pipeline Location
Location of infrastructure that aids in pipeline
transportation
Number
Private
Number
Private
Number
Private
River Location
Rail Location
Location of infrastructure that aids in river
transportation
Location of infrastructure that aids in rail transportation
1)Ever increasing
data set;
2) Use of software;
3) Static data
Restoration Data
# of People
Number of people available and required to work on
restoration
Number
Private/Public
Travel Time
Time required for different teams to arrive at the
damage area
Hours/Days
Private/Public
Skill Set
Skilled people required to work on different aspects
-
Private/Public
Mode Substitution
If possible, substitution of mode to allow freight flow
-
Private/Public
Task Management
Assignment and management of different tasks
-
Private/Public
Equipment Required
Goods required for restoration to take place
Tons/Pieces
Private/Public
1)Different time
dependence factors;
2)Vast amount of
data;
3) Scalability;
4)Ownership of data
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Data Size Estimation
Data Set
Elevation
Hydrography
Orthoimagery
Roads
Rail
Location
U.S. Geological Survey (USGS) – The National Map (TNM)
USGS – National Hydrography Data (NHD) from TNM
USGS – TNM
USGS – TNM
Missouri Department of Transportation (MoDoT) - Illinois Department of Transportation (IDoT)
Size (MB)
2000
93.6
674,000
354
85.2
Airports
USGS – Center for Excellence in Geospatial Information Science (CEGIS)
0.310
Electric Grid
Bridges and Overpasses
Tunnels and Culverts
Water Plants and Stations
Dams and Locks
Power plants
Railroad yards and stations
River Docks/Ports
Communication Towers
Restoration Rates
USGS – CEGIS
USGS – CEGIS
USGS – CEGIS
USGS – CEGIS
USGS – CEGIS
USGS – CEGIS
USGS – CEGIS
USGS – CEGIS
U. S. Federal Communications Commission (FCC)
Missouri University of Science and Technology (S&T) – Department of Engineering
Management and Systems Engineering (DEMSE) (Compiled from literature and interviews)
U.S. Department of Commerce (Commerce), U.S. Department of Transportation (USDoT) and
Private business (Compiled and housed at S&T – DEMSE)
0.980
0.436
0.223
0.063
0.053
0.007
0.031
0.053
0.020
0.250
Simulated data from disaster scenario model, compiled and executed by USGS and S&T,
housed at S&T
100.6
Supply Chain Flow Rates
Disaster Damage/Hazard
Scenario
Total
50.56
676,686
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Model work flow
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Types of Interdependencies
• Physical – one system depends on another
for operation (ex, wastewater depends on
power)
• Geographic – co-located systems
• Cyber – linked electronically or through
information-sharing
• Logical – other, such as shared financial
market
Types of Interdependent
Failures
• Cascading – direct disruption
• Escalating – exacerbates already-existing
disruption, increasing severity or prolonging
• Restoration – impacts the restoration of
another system
• Compound damage propagation – leads
to disruption that causes serious damage
• Substitutive – disruption due to excessive
demands placed on a system to substitute
for failed system
Algorithm for Interdependency
1: Load data for each element.
2: while data exists do
3:
for i = 1 to number_of_infrastructure_elements
4:
Select one element
5:
Make directed graph with nodes and edges
6:
while intersection do
7:
Planarize edge and use weight as distance between the line segment
8:
end while
9:
Use Dkijstras algorithm find nearest edge
10:
while not reached end point of agents destination do
11:
Calculate list of edges that make the route
12:
for j = 1 to number_of_coordinates_to_pass
13:
Map according to rules
14:
end for
15:
end while
11:
Update database
10:
end for
11: end while
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Combining Sub-Systems
Infrastructure
Data
Freight
capacity Data
Restoration
Data
Geospatial
Data
Transportation
Data
CAS
Restoration
Optimization
GUI
Decision
Framework
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Summary
• An inventory of the necessary data is
presented along with information on how
well these data can be estimated and
integrated from public access sources.
• Creating a SCSI model using public data is
a daunting task, but is possible.
• ABM can be used for mapping
interdependencies and creating
simulations.
Summary
• Use SCN availability function to
estimate the expected economic
impact of a given extreme event.
• Restoration management routing
decisions during extreme events
• Facility location in terms SCN
resiliency
• Using resilience as a metric of SCN
reliability during and after extreme
events.
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Summary
• Economic Recovery following extreme
events dependent on robustness of
supply chain networks.
• SCN highly dependent on geospatial
data for facility location but this
element is significantly underdesigned
• Improving the SCN resilience and
scalability after Extreme Events
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Thank you
• Questions?
• For more information contact:
Dr. Suzanna Long, EMSE
Telephone: 1-573-341-7621
Email: longsuz@mst.edu
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References
•
Guha, S., Moss, A., Naor, J., Schieber, B., 1999. Efficient recovery from power outage. In:
Vitter, J., Larmore, L., Leighton, F. (Eds.), Proceedings of the Symposium on Theory of
Computing (STOC). Atlanta, GA, USA
•
Ang, C., 2006. Optimized recovery of damaged electrical power grids. Unpublished
master’s thesis, Naval Postgraduate School.
•
Xu, N., Guikema, S., Davidson, R., Nozick, L., Cagnan, Z., Vaziri, K., 2007. Optimizing
scheduling of post-earthquake electric power restoration tasks. Earthquake Engineering
and Structural Dynamics 36 (2), 265–284.
•
E.E. Lee, J.E. Mitchell, and W.A. Wallace. Restoration of services in interdependent
infrastructure systems: A network flows approach. IEEE Transactions on Systems, Man,
and Cybernetics, Part C: Applications and Reviews, 37(6):1303{1317, 2007.
•
J. Gong, E.E. Lee, J.E. Mitchell, and W.A. Wallace. Logic-based multi-objective
optimization for restoration planning. In W. Chaovalitwongse, K.C. Furman, and P.M.
Pardalos, editors, Optimization and Logistics Challenges in the Enterprise, chapter 11.
Springer, 2009.
•
Cavdaroglu, B., Hammel, E., Mitchell, J., Sharkey, T., Wallace, W.: Integrating restoration
and scheduling decisions for disrupted interdependent infrastructure systems. Annals OR
203(1 ): 279-294 (2013)
•
Nurre, S., Cavdaroglu, B., Mitchell, J., Sharkey, T., Wallace, W.: Restoring infrastructure
systems: An integrated network design and scheduling (INDS) problem. European Journal
of Operational Research 223(3): 794-806 (2012)
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