Optimum Locations of National
Resilience Assets
Isaac Newton Institute for Mathematical Sciences
Cambridge
26 March 2013
Suk Athwal
Gian Amat
Russell Hocken
Fire, Emergencies and Resilience Directorate (FRED)
Department for Communities and Local Government
DCLG National Resilience Policy
DCLG has the policy responsibility for the funding of the Fire and Rescue Service (FRS)
national resilience capabilities in England for dealing with major emergencies including
terrorist incidents involving a deliberate release of chemical, biological and radiological
(CBR) substances.
Fire Resilience and Emergencies Directorate (FRED) is responsible for policy and advice
on national fire and rescue resilience. It’s priorities include:
•
to work closely with Fire and Rescue Authorities to maintain existing national resilience
capability ensuring it is fit for purpose and ready to be deployed when needed;
•
to improve national resilience by ensuring capability gaps are identified and filled where
practical; and
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to achieve value for money and efficiencies in current and future provision.
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The response to an incident is inter-agency, with each agency having clearly defined
roles and responsibility. Therefore the FRS response is critically dependent upon the
response of other agencies (and hence the resilience policies of other Central
Government Departments such as the HO and DH).
FRS National Resilience Capabilities
FRS National Resilience capabilities include the following assets:
•
•
•
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Urban search and rescue (USAR)
High volume pumps
Mass decontamination, including DIM vehicles (detection,
identification and monitoring)
Command and control - enhanced logistics support and Fire and
Rescue Service National Co-ordination Centre
FRS National Resilience Assets
Urban Search &
Rescue
Water
20 x complete USAR units
comprising:
5 Modules
3 Prime Movers
Canine support
46 x High Volume Pumps (HVPs)
comprising:
HVP Module
Hose Box Module
A Prime Mover
73 x Incident Response Units
(IRUs)
33 x Dis-robe Module (MDD) and
a Prime Mover
Mass
Decontamination
13 x Re-robe Module (MDR) and
a Prime Mover
18 x Detection, Identification &
Monitoring (DIM)
Strategic Locations of FRS NR Assets
FRS national resilience assets are strategically located in England (similarly in
Wales, Scotland and Northern Ireland)
The strategic positioning of the assets takes into account the Cabinet Office
National Risk Assessment (NRA) and the National Resilience Planning
Assumptions (NRPAs).
•
NRA provides information on the types of malicious and non-malicious
disruptive events that would cause significant harm and disruption to the UK.
•
NRPAs provide information on, inter alia, the number of concurrent incidents
and their potential impacts (e.g. numbers of people affected). NRPAs are used
to inform and underpin resilience planning at the national and local tiers of
government.
•
For incidents involving a release of chemical, biological or radiological
substances, the response of the emergency services and other responding
agencies is assessed against certain predefined locations.
GIS map examples
Key Performance Indicators
Key performance indicators (KPIs) are used in the mobilisation of the FRS
national resilience assets.
For example, to deal with 900 casualties:
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2 IRUs are required for mass decontamination, first IRU to arrive
within 30 minutes and second to arrive in 60 minutes. Third and fourth
IRUs arrive within 60 minutes for logistical support.
•
1 MDD and 1MDR are required to arrive in 60 minutes.
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Need 3 DIMs. First DIM to arrive in 30 minutes and the second DIM to
arrive in 60 minutes. The third DIM to arrive in 90 minutes for
logistical support.
Examples of Policy Questions
How is the response of the FRS assets affected if we:
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Remove one or more vehicles;
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Change the locations of interest to where the response is
required;
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Look at different combination of simultaneous incidents;
If we need to deploy one or more new vehicle(s), where should we
locate it/them?
What are the optimal number of vehicles required to provide a
given response to locations of interest?
Analysis Methods to date
A number of different analytical models and software have been used by
DCLG Analysts in support of policy teams:
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The FSEC (Fire Service Emergency cover) model was used to
generate a drive-time matrix between selected origins and
destinations;
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Bespoke analysis and presentation of results using spreadsheet
models;
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GIS based models;
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Optimisation techniques, e.g. Frontline Solver add-in for Excel.
Analytical Tools currently under development
• A Geographic Information System (GIS) National Resilience
Tool (NRT) is now being developed that will consolidate and
enhance the relevant functionalities and features of our
previous analysis models.
• The NRT will enhance our overall analytical capabilities and
ensure the tools are more flexible and user friendly.
GIS datasets
Some of key datasets that will be used in conjunction with the GIS tools under
development are:
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Ordnance Survey Integrated Transport Network (ITN) network dataset, that
maps all types of roads in Great Britain, from motorways to pedestrianised
streets using Department of Transport (DfT) road speed data derived from their
National Transport Model. The DfT road speed data is broken down by vehicle
type (i.e. cars/light vans and HGVs), geographic region, urban/rural split and
time/day.
•
National resilience assets datasets that contain the geo-reference (i.e. location)
of each of the asset in England and Wales.
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Datasets of various locations of interest/destinations (i.e. locations at risk),
where an incident could occur to which a response of the national resilience
assets is required.
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Datasets for different critical national infrastructure (e.g. locations of hospitals,
police stations, nuclear and conventional power stations, COMAH sites, utilities
(gas, water and electricity), transport hubs, etc.)
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2011 Census data for Great Britain at the following geographies – output area,
lower super output area, Local Authority Upper Tier, Local Authority Lower Tier.
GIS Tools A, B and C
Isochrones/drive-time model (Model A):
• Will create a set of isochrones around a given number of asset
origin locations to show the distance travelled in a given
time(s), e.g. 30 mins, 60 mins.
Population count model (Model B):
• Will calculate the total population (based on Census 2011 data)
and the total geographical area within each of the isochrones.
Drive-time matrix model (Model C):
• Will calculate the drive-time from the location of each asset to
the location of each destination considered (i.e. an origindestination matrix containing the arrival times of the assets at
each destination)
GIS Tool D Overview
Sequential selection-location model (Model D):
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Will calculate the weighted response of the assets to a number of
concurrent incidents, selected from pre-defined destinations using a
set of logic rules (e.g. 3 concurrent incidents, one in London and 2
outside London).
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The weighted response is calculated based on whether or not the
arrival times of the assets at the selected destinations are within the
key performance indicator times for that asset (e.g. first asset must
arrive within 30 minutes, second asset within 60 minutes, etc.).
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This model will consider all possible combinations of the concurrent
events.
GIS Tool D Response Matrix
• For each vehicle type, from an initial response matrix and a
deployment matrix, a response matrix is generated which puts
the vehicles in response order for each site (fastest first)
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GIS Tool D Incident Locations
• Previous work has looked at the reasonable “worst case” which
was multiple incidents co-located at a single location (i.e. all in
London) We are now using combination theory to consider all
possible combinations of incidents.
• Using
• 1 in London and 1 from a national list of 17 gives us 17
combinations to evaluate
• 2 in London, plus 2 from a national list of 17 gives us 136
combinations to evaluate
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GIS Tool D Operation
• Tool D assesses the response matrix to evaluate response to
the first combination of sites based on a set of vehicle
deployment rules.
• Once a vehicle is deployed to a simulated incident, it is
removed as an option from all sites.
• All incident locations are assessed in order, from the fastest to
the slowest KPI.
• The matrix is then reset so all the vehicles are made available
again and another combination of sites can be evaluated.
• The output of Tool D is a Percentage score of how many of the
weighted KPIs have been met.
• The model currently takes about 1 second to evaluate each
combination of incident sites.
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GIS Tool D Output
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GIS Tool E - Optimisation
• Tool D tells us what happens for a specific
deployment of vehicles
• A more useful question for us is, based on a set of
constraints where should the vehicles be deployed?
• The intention of Tool E is to use Tool D as the
objective function of an optimisation problem.
• This is an even bigger problem, where 100 fire
stations, with 17 vehicles to deploy gives us: 7x1018
possible solutions.
Tool E – Genetic Algorithm
We have developed a concept tool in Excel that uses simple genetic algorithms.
•The tool works by generating an initial pool of solutions (vehicle deployment
strategies)
•These are individually assessed and ranked using Tool D
•The lowest scoring solutions are removed and replaced by newly generated
solutions.
•The best scoring solutions are combined together based on random chance to
produce new hybrid (hopefully better) solutions
•There is a small chance of a random mutation occurring to add variety to the
solution pool.
•The highest scoring solutions of all time are recorded as the best ever and are
proposed as near-optimal solutions.
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Current Position
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The proof of concept model has been tested successfully with simple small scale problems.
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The concept model solves small scale problems in minutes.
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Improvements in the proposed solutions can be seen from the model outputs:
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We are in discussion with suppliers about developing a customised GIS tool based on
commercial networking evaluation algorithms
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We are considering the development of a more developed version of Tool E that is easier
to use, and has been developed to handle larger problems without involving extensive
runtimes.
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Questions
We are interested in any thoughts on our work, but our initial questions are:
General
• We currently measure success against meeting KPI criteria. Would you see benefit in
moving to a different metric such as lives saved?
• We currently consider each element of our response individually. Have you got any
suggestions on how we could do a more complete incident model?
Tool D:
• We are concerned about any ordering effects of the locations, but considering every
permutation is too slow.
• We plan to randomise the location ordering to minimise this effect, is this reasonable?
Tool E:
• Is optimisation the right technique to use?
• Are there other optimisation methods that would be better for this problem?
• Our model is slow because we are looping Tool D within loops for Tool E. Is there a way
around this approach?
• Our concept model is in Excel, what would be best to produce a faster model?
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How can we process solutions faster?
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Are there other technologies we can use to process more efficiently?
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