Choice of Airport in Extinguishing Wildfires: Model and Cases
Louise Ekström and Christine Große
a
Department of Information Systems and Technology, Mid Sweden University, Holmgatan 10, Sundsvall, Sweden
Keywords: Balanced Transportation Problem, Wildfire, Regional Airport, Flying Forces, Real-life Application.
Abstract: This paper develops a model to support the optimal choice of an airport as a base for the flying vehicles that
are operated to extinguish wildfires and forest fires. Based on experiences from the two largest wildfires in
Swedish history, this study models the optimisation as a balanced transportation problem. In both cases, the
model selected the airport that is closest to the fire area. If the capacity of the chosen airport was insufficient
to host all of the flying vehicles, then the model added a second airport which is also nearby the wildfire area.
The cases demonstrate that the total cost of the operation is lower when the extinguishing work is concentrated
in an area that has a short distance between the airport and the fire, the fuel depots and the pilots’
accommodation. Improved access to relevant data in the context of crisis management by air could allow for
the inclusion of additional parameters and correct data in the optimisation model, which could in turn provide
more comprehensive decision-making support.
1 INTRODUCTION
Sweden consists of large forest areas, and woodland
comprises about 65% of the land area in the country.
A considerable number of wildfires occur in Sweden
each year, while severe forest fires emerge only once
or twice per decade (Hansen, 2003). However, in a
recent five-year period, two years witnessed heavy
wildfire seasons. In the summer of 2014, a large
wildfire occurred in Surahammar in the county of
Västmanland in Sweden. This fire destroyed about
14,000 hectares of forest (Länsstyrelsen i
Västmanlands län, 2014). Four years later, in 2018,
several large forest fires emerged throughout the
Swedish countryside. Many of them were difficult to
address because of the extent, location or scarcity of
resources. The four largest areas on fire were in the
Swedish counties of Gävleborg, Dalarna, Jämtland
and Västernorrland. These fires covered a total area
of more than 18,000 hectares (MSB, 2018).
To extinguish such large wildfires, firefighting
operations require many resources, such as trained
personnel, proper equipment and water. One of the
most effective resources for extinguishing forest fires
are the flying forces (Coen, 2008). These forces
include airplanes for extinguishing fires as well as
helicopters from civil organisations, public sources
and civil protection organisations of other countries.
a
https://orcid.org/0000-0003-4869-5094
Because of the vast distances and extent of roadless
areas in Sweden, the distances between airports and
wildfire sites significantly affect the economic losses
that accompany wildfires. Therefore, the selection of
an airport as a base for co-ordinating firefighting
activities impacts not only societal security but also
the cost of the fire extinguishing work and the
economic loss due to burned woodland.
In view of this, the aim of the present study is to
develop a model that supports the selection of the
optimal airport to act as base for flying resources
during fire extinguishing operations. To select the
ideal airport or combination of airports, this study
examines a balanced transportation problem that
minimises the cost of fire extinguishing.
The remainder of the article is structured as
follows. Following this introduction, Section 2 briefly
outlines air-based firefighting practices as well as
previous research on the topic. Then, Section 3
describes the construction of the model, including the
assumptions and delimitations. In addition to
presenting the applied data from two wildfires in
Sweden, Section 4 provides the results and discusses
certain implications. The conclusion summarises the
article and indicates areas for improvement and
further research.
364
Ekström, L. and Große, C.
Choice of Airport in Extinguishing Wildfires: Model and Cases.
DOI: 10.5220/0009166703640371
In Proceedings of the 9th International Conference on Operations Research and Enterprise Systems (ICORES 2020), pages 364-371
ISBN: 978-989-758-396-4; ISSN: 2184-4372
Copyright
c
2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
2 BACKGROUND
2.1 Air-based Firefighting
Flying forces are effective and efficient because they
perform aerial firefighting that can approach remote
areas that are difficult to access with land-based
resources. In addition, airplanes and helicopters can
quickly transport a large amount of water to the site
of the fire and unload it from an overhead position.
Such vehicles can collect water from a lake or other
watercourse in the surrounding area without landing.
Sweden currently maintains no flying resources that
are entirely dedicated to firefighting. Instead, such
resources are borrowed from several partners in the
public and private sectors at both the national and
international levels, including from other countries in
the European Union, such as France, Italy, Spain and
Germany. Helicopters are also loaned by civil and
private organisations, the military or even other
countries (Hansen, 2003).
In the practice of fire extinguishing by flying
resources, regional airports provide bases at which
the flying forces can, for example, receive service,
refuel and access parking. To fulfil this role, airports
must meet several requirements, such as an adequate
capacity for the airplanes and helicopters which will
be used for firefighting. In addition, they must
maintain access to fuel, support lodging for pilots and
staff, and provide the necessary service and logistics
for the flying resources.
Another factor that influences the selection of an
airport as a base for fire extinguishing is its location
in relation to a particular wildfire area. From a
strategic point of view, it appears relevant to use an
airport that is centrally located in relation to
interdependent infrastructures and services as well as
proximal to site of the fire, as such characteristics can
reduce irrelevant flight time. Since airplanes and
helicopters for firefighting are rather slow-flying
vehicles, the distance between the airport and the
wildfire significantly affects the flight time. These
conditions encourage the selection of an airport that
is located as close as possible to the wildfire area.
2.2 Previous Research
There are many studies in the field of wildfires and
fire extinguishing. For example, previous research
has examined effective and efficient strategies for
firefighting operations (Mendes, 2010). Other
research has investigated the performance of such
operations and identified areas for improvement in
these approaches (Coen, 2008). Moreover, some
studies have developed models for risk analysis to
analyse the extent of the risk of a wildfire in certain
areas and, on this basis, create prognoses that can
support decisionmakers in planning risk reduction
strategies and crisis management (Ekström, 2003;
Pandey and Ghosh, 2018). In the context of
firefighting operations in forest areas, another study
has explored how to optimise the combination of
different resource types (Donovan and Rideout,
2003).
Previous research has also targeted other societal
sectors to examine optimised resource allocations.
For example, in the context of emergency medical
care, studies have applied decision models to
investigate ideal locations for rescue services (Yang
et al., 2007) or appropriate allocation of resources to
various stations to reduce response time while
achieving maximum area coverage (Liu et al., 2017;
McCormack and Coates, 2015). In this context, Liu et
al. (2017) have considered which rescue service
stations to utilise as well as how many vehicles to
place at the stations to reduce stand-by costs and
increase the efficiency of emergency response.
Several studies on wildfires have applied
simulation and optimisation approaches to determine
how to allocate resources of various types. Their aim
has been to support preparatory emergency response
planning to facilitate firefighting operations that are
as effective as possible in the event of a wildfire (Lan
et al., 2011; Rodríguez-Veiga et al., 2018). Many
studies have focused on preparatory emergency
response in terms of, for instance, where to place
airplanes for fire extinguishing in advance to reduce
stand-by costs. Such studies have assessed at which
airport to station the airplanes to decrease the
response time and the distance to areas that have a
high risk of wildfires (Bilbao Marón, 2013; Fiorucci
et al., 2005). Bilbao Maróns (2013) has conducted
forecasts that identify areas with a higher risk of
forest fires and account for the airport capacity and
distance to a potential fire to determine the optimal
solution.
However, few studies have examined the ideal
placement of flying resources to minimise costs
during a wildfire which must be extinguished by
flying resources. This study addresses this gap by
considering examples from the major wildfires in
Sweden in 2014 and 2018 and examining the issue as
a balanced transport problem. It ultimately proposes
a model for the selection of airports as bases for
firefighting by flying forces.
Choice of Airport in Extinguishing Wildfires: Model and Cases
365
3 MODELLING OF THE
TRANSPORTATION PROBLEM
3.1 Foundations
A transportation problem is a specific minimum-cost
flow problem (MCFP) for determining the optimal
solution to a problem. The method is popular because
it can be applied in several ways and to many domains
(Sonia, 2012).
A MCFP sends a flow from a number of supply
nodes to one or several demand nodes through the
arcs in a network at the lowest possible total cost
(Sonia, 2012). The subject of a transportation
problem is often cargo that moves alongside the arcs.
Each supply node provides a restriction to the amount
of goods that it can supply, while the demand nodes
have a limit to the minimum amount that they must
receive. If the total demand and total supply are equal,
then the problem is called a balanced transportation
problem.
The arcs between the nodes specify how the goods
can be transported. In addition, each arc in the
network reports the cost of shipping the goods
between the connected supply and demand node. At
times, arcs can demonstrate a constraint to the number
of units that can be transported via the specific arc.
A solution for this problem indicates the number
of units that can be transported on each arc. A general
formulation can be created to minimise the total cost
for the particular transportation problem. In addition
to such general formulation, sets of constraints aim to
ensure that the provided solution does not exceed the
maximal possible supply and simultaneously fulfils
the demand as fully as possible (Winston, 2004).
This paper applies an optimisation model in the
form of a transport problem to examine the optimal
airport or combination of airports to act as a base for
fire extinguishing by flying forces. The following
section describes the model and its particular
delimitations and assumptions.
3.2 Model and Conditions
3.2.1 Mathematical Model
This section details the mathematical model for the
optimisation. Equation 1 presents the objective
function that minimises the total cost of the
transportation problem, while Equations 2 and 3
specify the constraints. Table 1 outlines the sets,
variables and parameters that appear in the model.
Table 1: List of sets, variables and parameters.
Ter
m
Descri
p
tion
f
F
Set of flight vehicle types
a
A
Set of airports
z
Total cost that is minimised
x
fa
N
umber of flying vehicles of type f at airport a
p
fa
Cost for parking flying vehicles of type f at airport a
k
a
Capacity of airport a
d
a
Distance from airport a to the forest fire
e
a
Distance from airport a to the nearest city
s
a
Distance from airport a to the stock of aviation fuel
t
Total number of flying vehicles in use
b
Total number of transports to fulfil demand of
aviation fuel during the extinguishing operation
c
f
Fuel consumption of flying vehicles of type f
l
Fuel consumption of  trucks transporting aviation fuel
m
Fuel consumption of vehicle transporting pilots
n
Price of aviation fuel
o
Price of diesel
First, the following model accounts for several
aspects, such as the cost of storing the vehicles at an
airport, the cost of flying to approach the wildfire and
to return to the airport, the cost of transporting pilots
between the airport and their accommodation, and the
cost of transporting aviation fuel to the airport.
min𝑧 = 𝑥

∈∈
(1)
∙𝑝

+2𝑛𝑐
𝑑
+4𝑚𝑜𝑒
+𝑏𝑙𝑠
The model applies the constant 2, which conveys
that the cost of flying appears twice: first when
airplanes or helicopters fly to the fire and again when
they return to the airport. The constant 4 indicates that
there are two routes for land-based transport of pilots
and staff—the route from the airport to the city in
which their accommodation is located and the return
route—and that there at least two pilots for each
flying vehicle.
Second, two constraints are formulated to frame
the transportation problem.
∀𝑎
𝐴
;0𝑥

≤𝑘
(2)
𝑥

∈∈
=𝑡
(3)
As Equation 2 demonstrates, the first constraint
indicates that the number of flying vehicles that are
placed at an airport cannot be less than zero or greater
than the maximal capacity of the airport. Equation 3
states that the sum of the flying vehicles that are
stationed at all airports must be equal to the total
ICORES 2020 - 9th International Conference on Operations Research and Enterprise Systems
366
number of vehicles that are in use during firefighting
operations.
3.2.2 Delimitations and Assumptions
The model development focused solely on cost-
effective selection of airports in the context of
wildfire extinguishing by flying forces. It considers
flying resources, including airplanes and helicopters,
which can collect firefighting water from a
watercourse. Accordingly, it excludes other
resources, such as ground-based firefighting
resources. In the Swedish context, such delimitation
is appropriate since the large forest areas contain few
roads, which significantly hampers time-efficient
emergency response by ground-based vehicles.
The model has been delimited to determine the
optimal airport or combination of airports. In Section
4, two Swedish cases are used to evaluate the
optimisation model. This study selected these two
areas, namely Surahammar and Ljusdal, because they
contended with major forest fires in 2014 and 2018,
respectively. For each area, three airports are
preselected that meet several requirements. First, they
have the capacity to act as a base during firefighting
efforts. Second, they are located near the forest fire
area. In fact, two of the selected airports were utilised
during fire extinguishing operations in the individual
cases. Third, the airports, which are national, regional
or military, are be regularly operated or classified as
emergency response airports. This selection is based
on the assumption that such airports provide adequate
facilities and services to act as a base for crisis
management in the event of a wildfire by, for
example, providing infrastructure for operations
management, ground service for flying vehicles and
around-the-clock manning. This third requirement
implies that closed or privately owned airports are not
considered. The preselected airports are Stockholm-
Västerås Airport, Sundsvall-Timrå Airport, Uppsala-
Ärna Airport, Åre-Östersund Airport and Örebro
Airport.
4 CHOICE OF AIRPORTS: TWO
SWEDISH CASES
4.1 Context and Data from Wildfires in
Surahammar and Ljusdal
This study applies the optimisation model to two
Swedish cases, namely Surahammar and Ljusdal.
Major wildfires occurred in both areas in recent years.
In 2014, a major wildfire raged in the area of
Surahammar and developed into the largest forest fire
in Swedish history at the time (Länsstyrelsen i
Västmanlands län, 2014).
In 2018, a combination of heat and drought caused
even more extensive wildfires. These fires burned
more than 25,000 hectares of forest, which
corresponds to almost twice the area that was on fire
in 2014. Compared to the 110 wildfires that occur in
an average season, more than 500 fires were detected
by the rescue service in the summer of 2018 (Sjökvist
et al., 2019). The most severe fires were concentrated
in the middle of Sweden, and the area of Ljusdal was
heavily affected (Ljusdals kommun, 2018).
Aviation—and, thus, the regional airports—had a
crucial role in combatting the forest fires.
Surahammar is located near several airports and
cities, including the Swedish capital, whereas Ljusdal
is situated further north at a greater distance from
cities and airports.
Apart from airports that are located nearby
Surahammar and Ljusdal, the pre-selection of airports
for the optimisation included those that were used
during the extinguishing operations in 2014 and 2018,
respectively. During the wildfire of Surahammar,
Stockholm-Västerås Airport (MSB, 2015) was the
base for the flying forces. In addition, this study pre-
selected Örebro Airport and the military airport Ärna
in Uppsala, Sweden, which fulfil the requirements in
Section 3.2.2. During the wildfire in Ljusdal, Örebro
Airport was the base for the flying resources. This
study also added the airports of Åre-Östersund and
Sundsvall-Tim to the optimisation because they
meet the aforementioned criteria and are classified as
airports for emergency response.
The calculations apply data from public reports
regarding the extinguishing work, public statistics in
the context of aviation and a well-founded estimate of
completing aspects.
In the most intensive phase of the firefighting, 14
helicopters and four airplanes were used (Ljusdals
kommun, 2018; nsstyrelsen i Västmanlands län,
2014). In the first case, airplanes were operational on
1,533 occasions, and the number of helicopter
operations was estimated three times as high as the
airplane operations. Airport capacity estimates were
based on the number of operations per flying vehicle
and the possible number of flights from the airport in
accordance with public statistics from the Swedish
Transport Agency (Transportstyrelsen, 2019). The
airport capacity was determined by dividing the
number of operated flights per day by the number of
operations per flying vehicle.
The calculation of staff transport between the
accommodations and airports included the fuel
Choice of Airport in Extinguishing Wildfires: Model and Cases
367
consumption of a minivan of the type Renault Traffic.
The transport of aviation fuel was based on fuel
consumption data for a truck of the type Scania S 500.
Since the public reports do not include any details
regarding the type of helicopter, this study assumes
that they were Blackhawk UH-60 aircrafts, which the
Swedish military operates. Aviation fuel
consumption was based on estimates (e.g. Military
Advantage, 2019). The airplanes for fire
extinguishing were Canadair CL-415 airplanes
(MSB, 2018). The municipality of Ljusdal has stated
that the wildfire was under control after a period of
21 days. This most intensive phase in the second case
was of approximately the same duration as the fire in
2014.
Other operational costs, such as those to land and
park at the airports, were collected from the pricelists
of the respective airports (Sundsvall-Timrå Airport,
2015; Swedavia Airports, 2019; Örebro Airport,
2014). Stockholm-Västerås Airport applies
Swedavia’s pricelist, whereas the state-owned
military airport of Uppsala-Ärna has not provided any
public prices. For the latter airport, this study
estimated the cost to be half of the lowest fees that
were specified by Stockholm-Västerås Airport. To
calculate the cost, the landing fees were multiplied by
the number of days of firefighting and the weight of
the flying vehicle. Parking fees reflect 24 hours.
The distance between the airport and a nearby city
for the accommodation of pilots was based on data
from Google Maps, which offered the fastest
connection. It remains uncertain which fuel depots
the airports use to refill their aviation fuel storage, but
this study selected Bromma Airport as the starting
point for the aviation fuel supply. The distances
between the airports and the two fire areas were set to
the linear distance between the respective airport and
the centrum of the fire-affected area according to
reports. The cost of aviation fuel was collected from
Hjelmco Oil (2017).
4.2 Results and Analysis
4.2.1 Case One: Surahammar (2014)
The optimisation model suggests that all airplanes
and helicopters that perform fire extinguishing work
should be placed at Stockholm-Västerås Airport to
realise the lowest possible cost. The model calculates
the total cost to be slightly more than 1 million
Swedish kroner (SEK). During the forest fire in 2014,
Stockholm-Västerås Airport served as the base for the
firefighting activities by air. The results of this
optimisation model confirm that the selection of
Stockholm-Västerås Airport was the optimal decision
with regard to the cost of the operations for three
reasons: it is closest to the area, it is the shortest
distance from the nearest city, and it has the capacity
for all flight vehicles.
4.2.2 Case Two: Ljusdal (2018)
The application of the optimisation model to the case
of Ljusdal returned a total cost of slightly more than
2.5 million SEK. For this case, the model identified a
combination of two airports as the optimal solution.
Specifically, all airplanes should be placed at
Sundsvall-Timrå Airport, while the helicopters
should be stationed at two airports, namely
Sundsvall-Timrå and Åre-Östersund. Since none of
the airports has the capacity to host all of the
firefighting helicopters, these aircrafts must be placed
at two airports. Sundsvall-Timrå Airport should be
filled with as many helicopters as possible, and the
remaining helicopters should be stationed at Åre-
Östersund Airport. In this case, the model still
recommends the airport that is closest to the wildfire
as the host for the airplanes, as it is the most expensive
factor in fire extinguishing operations.
The model did not suggest Örebro Airport as the
optimal choice for the flying resources in the second
case, which implies that the decision to use this
airport as the base for the extinguishing work in 2018
was not optimal. This result validates the criticism
that officials from the rescue service expressed
following the event. They noted, for instance, that the
choice was ineffective because of the significantly
greater distance of Örebro Airport from the fire area
compared with Sundsvall-TimAirport. In departing
from the choice in 2018 to locate all airplanes at
Örebro Airport, the optimisation model returned an
increase in the total cost to slightly over 3.5 million
SEK. This result demonstrates that the total cost of
the fire extinguishing work could have been
substantially lower if the airplanes had been moved to
Sundsvall-Timrå Airport instead. Discussions of this
issue occurred as the firefighting was ongoing, but
they produced no consequences for the operation.
One reason for the utilisation of Örebro Airport as the
operational base could be that the airplanes were
already stationed there and ready to participate in
firefighting operations. At the beginning of the
summer, the Swedish Civil Contingencies Agency
(MSB) sent a request to the European Union for the
placement of fire airplanes in Sweden with the
preventive purpose of enabling rapid emergency
operations (MSB, 2018). When the international
flying forces arrived, they were placed at Örebro
ICORES 2020 - 9th International Conference on Operations Research and Enterprise Systems
368
Airport, which was probably because it is centrally
located in the country. Even if aircrafts were located at
Örebro Airport before they started the extinguishing
operation in Ljusdal, they should have been moved to
a closer airport during the operation. In view of these
results, the idea to move the base for the flying
resources further north should have been considered at
an earlier stage once it became clear that the forest fire
was difficult to control and extinguish. Such movement
could have reduced the cost of the extinguishing work.
5 IMPLICATIONS FOR
AIR-BASED FIRE
EXTINGUISHING
In general, an appropriate preparedness is necessary to
host airplanes and helicopters at several airports in all
regions of Sweden and especially during the summer
period, when the risk of wildfires is high.
As the 2018 case of Ljusdal reveals, the aircrafts
that carried out the fire extinguishing work should have
been hosted at an airport that is closer to the fire area
as opposed to Örebro Airport. Although such
preparedness may generate costs, the results of the
optimisation model indicate that preparedness can
provide a benefit to society by saving time and, in turn,
decreasing the loss of economic value. Such
operational preparedness further includes proper co-
operation and co-ordination between airports, pilots
and the MSB, which can be difficult to maintain.
The proposed model of the transportation problem
is rather simple, and it poses some advantages and
disadvantages in comparison to more complex models.
First, the model deliberately neglects many real-life
constraints, which is mainly due to the scarcity of
publicly available data about the Swedish cases.
Despite such limitations, the model not only
emphasises the importance of airports as critical
infrastructure but also exemplifies the benefit of formal
methods for decision-making in the context of crisis
management to, for example, select the optimal airport
for extinguishing operations. The simplicity of the
proposed model therefore facilitates its application to a
particular case. Nevertheless, the presented model
could be improved. For instance, developments could
consider fees for landing and air traffic control at the
included airports, the stand-by costs of airports for
facilitating a swift establishment of operation
management, labour costs for all personnel who relate
to a certain operation and other costs that are relevant
to the logistics and administration of the operation.
The proposed optimisation model can support
decisionmakers in their assessment of which airport to
choose for operations. Such assessment would then use
data about the actual area. This study relied on data
about the forest fires in Surahammar and Ljusdal,
Sweden. In the context of these cases, some data were
available, but well-founded estimates were applied for
certain parameters, such as airport capacity. This study
experienced information scarcity in regard to several
important factors. Such data and information must be
available to researchers and decisionmakers to
heighten the quality of decisions about firefighting
operations by air. Access to relevant data is a
precondition to yield more accurate and insightful
results from the suggested optimisation model.
The design of the model considers the distance to
the fire as a key parameter. However, the use of
additional parameters is recommended to gain a more
nuanced understanding of the factors that affect the
choice of the airport. The model could provide a more
detailed and realistic result if it includes more
parameters and weights for the different parameters.
Such weighting can indicate that one parameter is
considered more important than another and is thus
preferred at a certain cost. The experiences from the
wildfires in Sweden reflect that factors beyond the
distance to the fire, the nearest city or the stock of
aviation fuel can affect the decision of which airport to
use for operations during fire extinguishing efforts.
Therefore, further improvements to the optimisation
model can include aspects concerning time or
geographical coverage.
The incorporation of a risk analysis could also
enhance the model application. The choice of airport
assumes that resources should cover large areas to
ensure adequate preparation in the event of a new forest
fire in another part of the area. A developed model
could simultaneously consider several fire areas and
use the enhanced coverage to determine the optimal
base for airplanes and helicopters. The model does not
account for the route of the flying resources between
the wildfire and the watercourses. In the two Swedish
cases, this aspect was not relevant, as the areas
provided abundant resources for refilling the water
tanks. However, this aspect can be significant if the
model is applied to areas with a scarcity of
watercourses. A model that includes this routing could
then identify the optimal watercourse for the
extinguishing work in the area.
Finally, the optimisation model in this study could
be combined with those of previous works which have
focused on the optimal placement of resources for
preventative purposes.
Choice of Airport in Extinguishing Wildfires: Model and Cases
369
6 CONCLUDING REMARKS
This study has developed an optimisation model to
select the most suitable airport to serve as a base for
flying vehicles that carry out extinguishing work in
the case of a wildfire. The model was applied to
evaluate two Swedish cases: Surahammar in 2014 and
Ljusdal in 2018. In both cases, the optimisation model
primarily chose the airport that is most proximal to
the site of the fire. If the capacity of the chosen airport
was not sufficient to host all of the flying vehicles,
then one additional airport was selected from the
remaining airports that are located nearest to the
wildfire. The examples demonstrate that the total cost
of the fire extinguishing operation would be lower if
the work is concentrated such that there is a short
distance from the airport to the fire, the aviation fuel
depot and the pilots’ accommodation. The
optimisation model provided a reliable result because
it identified the same airport for the case of
Surahammar that actually acted as the base for the
extinguishing work in 2014. In addition, it
recommended Sundsvall-Timrå Airport in the second
case of Ljusdal for the lowest possible cost of the
operation in 2018. Thereby, the optimisation model
reveals that the actual choice of Örebro Airport as the
base for the flying resources was an improper
decision from a cost perspective, which confirms the
perceptions of officials regarding that matter.
To enhance its usefulness for relevant
decisionmakers, the proposed optimisation model
should be subject to improvement. For example, the
model could further include the risk of wildfires
occurring simultaneously in different areas or of a
new wildfire arising while the extinguishing work is
still ongoing in some areas. Future analyses could
consider a larger number and variety of airports and
flying vehicles or extend the model to include
ground-based resources. In addition, the surrounding
conditions and their effects on the optimisation
should be a topic of further research. Examples
include the potential rationing of refuel, necessary
availability of both communication services for the
operative crisis management and maintenance
services for flying vehicles, and staffing of the
different functions that relate to the transportation by
air. The latter also encompasses issues such as regular
staff changes and recreation possibilities during long-
lasting operations, such as wildfire extinguishing
efforts.
The optimisation model and result accuracy could
significantly improve if data and information are
publicly available. Heightened access to relevant data
in the context of crisis management by air could allow
for the inclusion of additional parameters and correct
data in the optimisation model, which can in turn
provide more comprehensive decision-making
support.
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