Dynamic resilience planning for interdependent economic systems

DYNAMIC RESILIENCE PLANNING FOR INTERDEPENDENT ECONOMIC SYSTEMS

Raghav Pant, PhD Environmental Change Institute

University of Oxford

ITRC Conference: The future of national infrastructure systems & economic prosperity Cambridge March 28, 2014

Kash Barker, PhD School of Industrial and Systems Engineering

University of Oklahoma

PANT R & BARKER K. 2014. Dynamic resilience planning for Interdependent Economic systems 2

Why Resilience If we cannot control the volatile tides of change, we can learn to build better boats. We can design—and redesign—organizations, institutions, and systems to better absorb disruption, operate under a wider variety of conditions, and shift more fluidly from one circumstance to the next.

[Zolli and Healy 2012]


Interdependent Infrastructures

Critical Infrastructures

and Key Resources

Banking & Finance

Government Facilities

Defense Industrial Base

Commu-nication

Energy Dams Water Healthcare & Public Health

National Monuments & Icons

Information Technology

Transportation Systems

Emergency Services

Commercial Facilities

Nuclear Reactors, Materials & Waste

Critical Manufacturing

Agriculture & Food Chemical

Postal & Shipping

Rail Transport

Water Transport

Air Transport

Road Transport

Disruptions can propagate, resulting in direct as well as wider-spread indirect impacts.

Resilience is particularly important in interdependent systems

Disruption


Resilience: background

The notion of resilience has a strong history in several fields

- Ecology, social sciences, civil engineering

But lots of conflict in developing an agreed definition and resilience framework

In particular a resilience framework for interdependent systems is missing


Relevance of this work

Build a quantifiable resilience framework

A resilience paradigm from engineering with an economic interdependency model

Provide a means to valuate preparedness strategies


Defining Economic Resilience

Ability exhibited by systems that allows them to recover productivity In a desired time And/or with an acceptable cost

Resilience is planned for in advance of a disruptive event through preparedness policies and investments.

t0 t1

Disruption

Recovery

Stable target

Time

Eco

no

mic

Per

form

ance

As-planned level

Policies/ Investments

[Pant et al. 2012, 2014]


Interdependence and Disruption

3 4

2 1

1 2 3 4

1

2

3

4

Interdependence data (A*)

Disruptive event

Resilience Building

3 4

2 1

Building inventory

Resource Allocation

Controlling Interdependence

Resilience Evaluation

Inoperability Economic loss

Sect

or

Sect

or

Static Analysis

Dynamic Analysis

Time

Time

Op

erab

ility

O

per

abili

ty

Uncertainty

Outcomes

Cost

Eco

no

mic

loss

N

et B

enef

it

Cost

Resource management

Resilience framework


Dynamic inoperability IO model

[Lian and Haimes 2004]

For n interdependent sectors


0 10 20 30 40 50

0

0.2

0.4

0.6

0.8

1

T i me( t )

Op

era

bil

ity

(1−

qi(

t))

T i me aver aged l evel of oper abi l i t y :

F i = 1 −1

T

T

0

qi ( t ) dt

T i me t o r ecover y :τ i

M axi mum l oss of funct i onal i t y : qmi

qei

T =

Resilience definitions: through model Measuring dynamic resilience with inoperability

– Performance metrics that quantify the interdependent resilience from inoperability

Measures average interdependent loss

sustained during recovery

Measures maximum impact of the disruption

Measures the capability of the infrastructure/industry to move towards stability

[Pant et al. 2012, 2014]


Engineering Resilience definitions Dynamic resilience

– Ability to resist the initial impact

– Ability to recover in desired time

[Bruneau et al. 2003]

shaded area


Interdependent resilience planning Ultimately, the 4-tuple of provide a means to describe interdependent economic resilience and generate planning strategies

[Pant et al. 2014]


Different values for the 4-tuple can describe planning options after each shock event at h = 0,1,….

Multiple external shocks

0 10 20 30 40 50

0

0.2

0.4

0.6

0.8

1

T i me( t )

Op

era

bil

ity

(1−

qi(

t))

qi,1m

qi,2m

qi,3m

τi,1

τi,2

τi,3

[Pant et al. 2013]


Model control

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

0

0.2

0.4

0.6

0.8

1.0

Time

Error

Desired behavior or ‘observations’ z(k)

Modeled behavior q(k)

Minimize the error

A dynamic feed-back control problem that sets the model parameters setting resilience targets controlling interdependence planning

Model application


Port of Catoosa: Oklahoma

General'Dry'Cargo' Dry'Bulk' Grains' Liquid'Bulk'


Inland waterway network


Commodity flows and disruptions

Commodity queuing model at port and dynamic commodity flow along network

[Pant et al. 2011, 2014]

Disruption at port


Recovery time (days)

Targeted Recovery


Construction

Textile mills and textile product mills

Primary metals

Fabricated metal products

Machinery

Electrical equipment

Motor vehicles, bodies and trailers

Other transportation equipment

Furniture and related products

Miscellaneous manufacturing

Wholesale trade

Retail trade

Truck transportation

Administrative and support services

Computer systems design and related services

τ = 5 days τ = 10 days τ = 14 days

30.2% 17..1% 9.3%

0.10 0.04 0.02

0.02 0.008 0.0014

Planning for Preferred Recovery

τ = Time to recovery

Primary Metals

Chart showing degree of investment in inventory of

Primary Metals into other sectors for recovery


Oil and gas extraction

Construction

Food and beverage products

Textile mills and textile product mills

Wood products

Petroleum and coal products

Chemical products

Plastics and rubber products

Mineral products

Primary metals


Machinery

Electrical equipment

Motor vehicles, bodies and trailers

Other transportation equipment

Furniture and related products

Miscellaneous manufacturing

Wholesale trade

Retail trade

Truck transportation

Administrative and support services

Computer systems design and related services

Petroleum and coal products

Chemical products

Mineral products

Primary metals


Machinery Miscellaneous manufacturing

69.6%

81.2%

55.9%

71.5% 30.2%

Resilience For Port Sectors Resilience dependence of port sectors on other sector for 5 day stabilization from disruption

27.3% 28.1%


Scope for Future development

Integrating network dynamics with economic dynamics

Long term risk, resilience and adaptation analysis


Thanks for the attention Questions?

contact: [email protected]

[1] Pant, R., K. Barker, F.H. Grant, & T.L. Landers. (2011). Interdependent Impacts of Inoperability at Multi-modal Transportation Container Terminals. Transportation Research - Part E: Logistics and Transportation Review. 47(5), 722-737. [2] Pant, R.,K. Barker, K., & C. W. Zobel. (2013). Adaptive Economic Resilience Planning for Infrastructure and Industry Sectors with Multiple Shocks. ICOSSAR. [3] Pant, R.,K. Barker, K., & C. W. Zobel. (2014). Static and dynamic metrics of economic resilience for interdependent infrastructure and industry sectors. Reliability Engineering & System Safety.

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