Multi-Hazard Supply Chain Network Management: Assessment of Modeling Elements and Data Requirements for Pre- and Post- Disaster Restoration

June 26, 2013

Dr. Suzanna Long, Missouri S&TDr. Tom Shoberg, U.S. Geological Survey

Dr. Steven Corns, Missouri S&TDr. Héctor Carlo, University of Puerto Rico at

Mayaguez

Project #G13AC00028

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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

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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

Lines

10 days 10 days Same as Joplin

Electricity 15.5 days 14 days +1.5 days

Local Transportation

Network

10 days total major

roads 5-6 days

90% major roads

cleared.

+1 day

Water Pipelines 8 days 10 days -2 days

Resiliency 22.5 +/- 2.5 days 24.5 +/- 3.5 days +/- 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.

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Tools to Approach the Project

• Model Based Systems Engineering (MBSE)

• Complex Adaptive Systems (CAS)

• Agent Based Modeling

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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Vision: Integrated systems-oriented decision support…

Minimum Turn Radius: 24 ft.Dry Pavement Braking Distance at 60 MPH : 110 ft.

Automatic Cruise Control <FAULT>

Thermal/Heat

Dissipation: 780°Ergonomic/

Pedal Feedback: 34 ERGSHydraulic Pressure: 350 PSISensor

MTBF:3000 hrs

Power Rating:18 Amps

Hydraulic Fluid: SAE 1340 not-compliant

Minimum Turn Radius: 24 ft.Dry Pavement Braking Distance at 60 MPH : 110 ft. 90 ft

Source: INCOSE MBSE Initiative – Mark Sampson

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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.

Complex Adaptive Systems

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Agents

EmergentBehavior

Feedback Feedback

SystemChangingexternal environment

Changingexternal environment

Changingexternal environment

Changingexternal environment

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Combining Information from Multiple Models

Disaster type: TornadoRestore supply chain to 80% pre-event capacity.

Disaster type: EarthquakeRestore supply chain to 80% pre-event capacity.

Geospatial Data

Freight capacity Data Restoration

Data

Infrastructure Data

Location Data

Transportation Data

MBSE Model

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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

• 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

A system is comprised of:

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 elevation data

Transportation data (road, air, rail, 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

Data Acquisition - TransportationCategory Data Description Data

TypeOwnership Difficulties with data

Freight DataCommodity Freight Food, Agriculture, Paper etc. Tons Public 1)Static data;

2)Generalized data; 3)

Proprietary data

Manufactured Goods

Electronics, Machinery, Vehicles etc. Tons Private/Public

Raw Materials Coal, Fuel, Chemicals etc. Tons Private/Public

Freight Flow DataRoad

TransportationGoods Transported by Road Tons Private/

Public1)Inconsistency;

2)Estimation maybe required;

3)Private-Public ownership

Rail Transportation Goods Transported by Rail Tons PrivateAir Transportation Goods Transported by Air Tons Private

Water Transportation

Goods Transported by Water Tons Private/Public

Pipeline Transportation

Goods Transported by Pipeline Tons Private/Public

Infrastructure Capacity DataRoad- Hub Bulk, General Cargo, Containers Tons Private 1) Varied amount of data

required; 2) Different capabilities of

hubs; 3) Interdependency of data

Rail-Hub Bulk, Break Bulk, Intermodal, Shunting, etc. Tons PrivateWater-Hub Rail Car Storage, Dry Storage, Liquid Storage

etc.Tons/

BushelsPrivate

Data Acquisition - TransportationCategory Data Description Data

Type Ownership Difficulties with data

Infrastructure Location Data/Geospatial DataHub Location Number of hubs in the area Number Private

1)Ever increasing data set;

2) Use of software; 3) Static

data

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

River Location Location of infrastructure that aids in river transportation Number Private

Rail Location Location of infrastructure that aids in rail transportation Number Private

Restoration Data# of People Number of people available and required to work on

restoration Number Private/Public 1)Different time dependence factors;

2)Vast amount of data; 3) Scalability; 4)Ownership of

data

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

Data Size EstimationData Set Location Size (MB)Elevation U.S. Geological Survey (USGS) – The National Map (TNM) 2000

Hydrography USGS – National Hydrography Data (NHD) from TNM 93.6Orthoimagery USGS – TNM 674,000

Roads USGS – TNM 354Rail Missouri Department of Transportation (MoDoT) - Illinois Department of Transportation (IDoT) 85.2

Airports USGS – Center for Excellence in Geospatial Information Science (CEGIS) 0.310

Electric Grid USGS – CEGIS 0.980Bridges and Overpasses USGS – CEGIS 0.436

Tunnels and Culverts USGS – CEGIS 0.223Water Plants and Stations USGS – CEGIS 0.063

Dams and Locks USGS – CEGIS 0.053Power plants USGS – CEGIS 0.007

Railroad yards and stations USGS – CEGIS 0.031River Docks/Ports USGS – CEGIS 0.053

Communication Towers U. S. Federal Communications Commission (FCC) 0.020Restoration Rates Missouri University of Science and Technology (S&T) – Department of Engineering

Management and Systems Engineering (DEMSE) (Compiled from literature and interviews)0.250

Supply Chain Flow Rates U.S. Department of Commerce (Commerce), U.S. Department of Transportation (USDoT) and Private business (Compiled and housed at S&T – DEMSE)

50.56

Disaster Damage/Hazard Scenario

Simulated data from disaster scenario model, compiled and executed by USGS and S&T, housed at S&T

100.6

Total 676,686

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Model work flow

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

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Algorithm for Interdependency1: Load data for each element.2: while data exists do 3: for i = 1 to number_of_infrastructure_elements4: Select one element5: Make directed graph with nodes and edges6: while intersection do

7: Planarize edge and use weight as distance between the line segment

8: end while9: Use Dkijstras algorithm find nearest edge10: while not reached end point of agents destination do11: Calculate list of edges that make the route12: for j = 1 to number_of_coordinates_to_pass13: Map according to rules14: end for 15: end while 11: Update database10: end for11: end while

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Combining Sub-SystemsGeospatial

DataFreight

capacity DataRestoration

DataInfrastructure

DataTransportation

Data

GUI

CAS

Restoration Optimization

Decision Framework

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.

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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 under-designed

• 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.

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master’s thesis, Naval Postgraduate School.

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scheduling of post-earthquake electric power restoration tasks. Earthquake Engineering

and Structural Dynamics 36 (2), 265–284.

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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

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