Research on Group Decision Model of Civil Aviation Emergency
Transport Based on Foreground Regret Theory
Yunxue Song and Qingmin Meng
Civil Aviation University of China, Tianjin, China
Keywords: Civil Aviation Emergency Transport, Group Decision Making, Cumulative Prospect Theory, Regret Theory
and Comprehensive Utility Value.
Abstract: For the decision problem of civil aviation emergency transport task, a solution of civil aviation emergency
transport group decision model based on prospect-regret theory is proposed. First of all, on the basis of
considering civil aviation transport mission uniqueness superposition decision makers using airport
emergency support ability, flight time, assembly time, flight cost as attribute value to evaluate the aviation
emergency transport scheme, by calculating the cumulative prospect value and regret theory of different
scheme regret joy value, combined with the expert weight to regret joy value weighted calculate
comprehensive utility value, and the comprehensive utility value is the best solution. Using this approach can
provide a more comprehensive reference for air transport in states of emergency.
1 INTRODUCTION
In the field of emergency transport, effective
decision-making models are important for improving
transport efficiency, reducing risk and achieving
sustainable development. Traditional decision-
making model often only consider decision makers in
absolute rational decision, ignoring the decision
maker preferences for risk and benefit and may
produce regret after making a choice, and these close
to the actual situation of limited rational factors in the
actual decision-making may have a significant impact
on the results.
The cumulative prospect theory is a theory of
decision behavior describing people in the context of
uncertainty, proposed by Kahneman and 1979 by
Tversky (Kahneman and Tversky, 2014) Tang
Qionghua (Tang et al., 2022) Based on the cumulative
prospect theory, the traditional impedance function is
modified to study the railway passenger flow
distribution method. Meng (Meng and Wang, 2022).
They applied it to the distribution path optimization
of electric vehicles, and built a decision model based
on quantitative attributes. The regret theory was
proposed by economists Bell and Loomes Sugden in
1982, comparing decision options with their expected
reference point, and making decisions based on the
attribute utility value and regret pleasure value of the
options. Scholar Hao Minxi
(Hao, 2022) Apply regret
theory to the study of subway emergency evacuation
by considering physiological and psychological
factors such as pedestrian homogeneity and
heterogeneity. Prospect-regret theory is a mixed
theory of risk decision making that takes into account
multiple emotional and cognitive factors. It can help
decision makers to more comprehensively consider
the risks and benefits of decision making, and reduce
decision errors. Zhang Peng (Zhang et al., 2021) In
view of the existing risk assessment method of oil and
gas pipeline, they proposed a risk mode analysis
method of oil and gas pipeline without considering
the psychological behavior characteristics of limited
rationality and regret avoidance. In the aspect of civil
aviation emergency transportation, some scholars
have completed the construction of optimal path
model and analysis method of such factors as driving
time, path distance, path complexity and so on (Shi
and Chen, 2019; Song and Li, 2022.
At present, there is no decision model research in
the field of civil aviation emergency transport
decision-making. To this end, this study aims to more
fully consider the psychological factors and
emotional responses of decision makers, and explore
the prospect-regret theory based on the prospect-
regret decision.
In the field of civil aviation, there are often some
urgent air transport tasks. Different from the ordinary
Song, Y. and Meng, Q.
Research on Group Decision Model of Civil Aviation Emergency Transport Based on Foreground Regret Theory.
DOI: 10.5220/0012880600004536
Paper published under CC license (CC BY-NC-ND 4.0)
In Proceedings of the 1st International Conference on Data Mining, E-Learning, and Information Systems (DMEIS 2024), pages 75-81
ISBN: 978-989-758-715-3
Proceedings Copyright © 2024 by SCITEPRESS – Science and Technology Publications, Lda.
75
decision-making scenario, the civil aviation
emergency transport group requires the most
reasonable and lowest risk decision quickly.
When these emergencies occur, the decision-
making of air transport needs to be timely and
scientific. The timely performance of decision
ensures the timeliness of the task. Scientific decision-
making can ensure the accuracy of the decision as
much as possible.
This model takes the transportation scheme given
by the airline as the evaluation object, takes the
attribute value of each scheme as the initial data, and
specifically calculates the comprehensive utility
value of each scheme based on the attribute tendency
rate of each scheme. Finally, the scheme with the
highest comprehensive utility value is the best
scheme.
2 THEORETICAL PRINCIPLE
2.1 Cumulative Foreground Theory
The cumulative prospect theory considers people's
risk attitude and value judgment when facing
uncertainty in the decision analysis. The decision
maker not only evaluates the possible outcomes of
various options but also determines the value of each
outcome based on the underlying outcome and
probability weighting function. Policymakers tend to
assess risk using a biased treatment of probability,
describing decision-makers' perception of the value
of different outcomes and their subjective weighting
of probabilities.
𝑉
𝑓
𝜋
𝑣
𝑥
𝜋
𝑣
𝑥
(1)
By introducing affective effects and asymmetry
effects (Camerer and Weber, 1992), The theory
explains some of the behavioral characteristics people
exhibit in decisions, such as risk aversion, loss
aversion and preference reversal. It provides a
framework to more accurately describe and predict
human decision-making behavior.
2.2 Regret Theory
Regret theory in decision analysis the regret that
people may feel after making a choice. Policymakers
not only evaluate the outcomes of the various options,
but also consider the degree of regret that may be felt
after choosing each option, compared to other
unselected options. Policymakers will choose the
option to reduce potential regret whenever possible.
The theory deeply explores the psychological
mechanisms behind decision-making behavior by
quantifying the regret feelings that individuals
experience in decision-making (Bell, 1982)
𝑍𝑥𝐺
𝑥𝑅
𝑥
(2)
Regret theory provides an important framework
for explaining emotional and regret influences in
decision behavior. By considering regret pleasure
values and attribute utility values, regret theory is able
to more accurately explain the choices and behaviors
of decision makers.
2.3 Prospect-Regret Theory
Prospect-regret theory considers that a decision
maker not only estimates the expected utility in terms
of the value and probability of the potential outcome,
but also considers the degree of regret he may feel
after making a certain choice compared with other
unselected options. By considering the cumulative
prospect value and regret joy value, the prospect-
regret theory provides a more comprehensive and
scientific decision framework, as shown in Figure 1.
Figure 1: Prospect-Regret Theory Technical framework.
3 CIVIL AVIATION
EMERGENCY TRANSPORT
GROUP DECISION MODEL
3.1 Model Characteristic Indicators
Were Determined
Combined with the particularity of the civil aviation
industry, many reference factors are involved in the
face of emergency transport tasks. For example,
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76
security, resource availability and communication
and coordination skills, as well as time efficiency,
economic cost control and other cost factors. After
study, the following 4 parameters were determined as
characteristic parameters of the evaluation scheme:
Guarantee ability: income-type index. The
support capability mainly includes the task response
speed between the upper and lower levels, the
complete degree of professional equipment in the
airport, the number of dispatching aircraft, etc. Five
indicators were identified as the typical characteristic
indicators of civil aviation support capability
assessment through reference (Song and Li, 2022).
Assembly time: cost-type index. Medical teams in
different cities need time to gather and go to the
airport, flight support time at airports in different
cities, time to board and load cargo.
Flight time: a cost-type index. Pure flight time
from different cities to the destination airport.
Flight cost: a cost-type index. Taking off from
different starting cities to destination cities requires
various cost support, including fuel costs, airport
costs, crew costs and other costs. Other expenses
mainly include aircraft maintenance, air navigation
service fee, etc.
3.2 Decision-Making Model
Obtain task data: Make data statistics combined with
the given transportation scenario. Raw data were
determined for subsequent calculations.
Determine the attribute value: compare the
relative size of the support capability; estimate the
assembly time; obtain the flight time; and estimate the
flight cost.
Identify each indicator reference point: determine
the reference point for the decision maker to evaluate
the results, usually the current state or expected
reference point. The main methods of finding
reference points are normal distribution method,
expected value method, zero point method and so on
(Jiang, 2015). The method of giving reference point
makes the subjective factors of decision makers have
a great influence on the decision result; normal
distribution method is applicable for large data
volume, leading to inaccurate estimation of density
function. The data volume of this study is relatively
small and the data used are determined, so it is
appropriate to use the average of the data as the
reference point.
Calculate the value function: Use the value
function to quantify the value of the result. The value
function calculates the value function of the four
attribute values respectively. The value function is
usually nonlinear, with different sensitivities to losses
and gains. The common value function is the type S
curve, with increasing marginal utility for obtained
values and diminishing marginal utility for loss
values.
𝑣
𝑥
=
𝑥
, 𝑥
≥0
−𝜇
−𝑥
, 𝑥
<0
(3)
𝑥
Is the benefit value calculated by the decision
maker according to the reference value; 𝛼, 𝛽 indicates
yes and against coefficient, where 0 <𝛼 and 𝛽 <1. If
𝛼 = 𝛽, it means that the risk assessment of the decision
maker for the benefit value is biased, the size of 𝛼 and
𝛽 determines the sensitivity of the decision maker; 𝜇
means the avoidance of benefit loss, used to measure
the aversion of the decision maker to the scheme
when the benefit loss, and means that the decision
maker is more sensitive to the equivalent loss. Take 𝛼
=1.21, 𝛽 =1.02, and 𝜇 =2.25.
Normalization of value function: the value
function value of different groups is calculated
according to the value function formula and then the
dimensional standardization integration.
Cos
t
-based indicators:
𝑎
=
|𝑣|
−𝑣
|𝑣|
−|𝑣|
(4)
I
ncome indicators:
𝑎
=
𝑣
−|𝑣|
|𝑣|
−|𝑣|
(5)
𝑣
合
= 𝑎
𝑣
(6)
𝑎
+ 𝑎+··· +𝑎
=1
(7)
𝑎
is the proportion of the different value function
values to the value function values with the largest
absolute value.
The value function values calculated from the four
attribute values in this model are normalized, and the
final value function values are added according to
different attribute value values.
Calculate the decision weight function: Use the
decision weight function to determine the weights of
the different results. The decision weight function
measures the relative importance of the decision
maker for different outcomes. It is usually non-linear,
and the data mainly represents the bias of the decision
makers for different attribute values in different
schemes.
Research on Group Decision Model of Civil Aviation Emergency Transport Based on Foreground Regret Theory
77
𝜋
= 𝜔
𝑝
−𝜔
𝑝
(8)
𝜋
= 𝜔
𝑝
−𝜔
𝑝
(9)
𝑚𝑎𝑥𝑄
= 𝑣
𝜋
(𝜔
)
−𝑣
𝜋
(𝜔
)
(10)
𝑠. 𝑡.
(𝜔
)
=1
𝑒𝑙𝑠𝑒 𝜔
≥0
(11)
among:
𝜔
(
𝑝
)
=
𝑝
𝑝
+
(
1 −𝑝
)
(12)
𝜔
(
𝑝
)
=
𝑝
𝑝
+
(
1 −𝑝
)
(13)
𝜋
(
)
and 𝜋
(
)
represent the decision weight
function of gain and loss respectively, 𝑝 represents
the probability of each scenario value given by the
expert, 𝜔
(
∗
)
and 𝜔
(
∗
)
represent the weight
function of gain and loss respectively, showing a
monotonically increasing S curve, x and 𝛿 represent
different coefficients of the decision maker with
different attitudes to gain and loss. Take x=0.61 and
𝛿 =0.69.
Calculate the cumulative foreground value:
combine the value function and the weight function
to calculate the cumulative foreground value for each
result. The cumulative foreground value is the
product of the value function and the weight function,
representing the comprehensive evaluation of each
outcome. The cumulative foreground values of all the
results were summed to obtain the cumulative
foreground values for the scheme.
𝑉(
𝑓
)=𝜋
𝑣
(
𝑥
)
+ 𝜋
𝑣
(
𝑥
)
(14)
Calculate the regret-pleasure value: substitute the
calculated cumulative foreground value into the
regret-joy value formula. For each decision option,
the corresponding regret pleasure value was
calculated. The regret pleasure value indicates the
regret or satisfaction that a decision maker may feel
after making a choice. The common formula is to use
the difference between the maximum attribute utility
value and the other attributes.
𝑍(𝑥)=(𝐺
(𝑥)+𝑅
(𝑥))
(15)
𝑅
(𝑥)=1−𝑒𝑥𝑝[𝜀|
𝑉
(𝑥) −𝑉
(𝑥)
𝑉
(𝑥) −𝑉
(𝑥)
|]
(16)
𝐺
(𝑥)=1−𝑒𝑥𝑝[−𝜀|
𝑉
(𝑥) −𝑉
(𝑥)
𝑉
(𝑥) −𝑉
(𝑥)
|]
(17)
R
(
x
)
is the regret function, G
(
x
)
is the joy
function, ε is the regret avoidance coefficient,
and the general value is 0.3.
Therefore, the theory of cumulative prospect and
the theory of regret are integrated, making the
multiple emotions of decision makers considered in
the decision calculation.
Finally, the final comprehensive utility value is
calculated according to the different weights of
different experts. The larger the comprehensive utility
value is, the better the scheme is.
3.3 A. Key Parameter Adaptation Value
The favor-opposition coefficient in the value function
is usually 0.88. This data is obtained by the author of
the cumulative prospect theory in the United States.
This index is usually less than 1, which means that the
decision maker has a weakening sensitivity to the
benefit. Through industry research and data
collection, combined with the unique decision
environment of civil aviation transportation, the
parameters are adjusted as follows:
It is understood that for the civil aviation industry,
the greater the benefit deviation of the results, the
greater the hidden danger of the actual situation, that
is, the sensitivity of the decision makers to the
benefits should gradually increase. Therefore,
according to the research object of this paper, it is
appropriate to choose the yes and opposition
coefficient greater than 1. Avavailable from reference
(Zheng, 2007) the approval coefficient is 1.21 and the
opposition coefficient 1.02.
In the civil aviation emergency transport industry,
it attaches more importance to the airport support
capacity, followed by the value of time. In contrast, it
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78
has less attention to the value of cost. Then, the
weight ratio of the four key factors of the guarantee
evaluation system is determined through data
collection as 0.5:0.2:0.2:0.1.
4 ANALYSIS OF THE CASES
4.1 Case Description
Taking an air transport task of the epidemic in Hubei
in 2020 as an example for calculation and analysis,
according to the specific situation of the task: the
COVID-19 in 2020,123 medical personnel and
medical supplies are needed to the Three Gorges
Airport in Yichang, Hubei. Combined with the
objective conditions at that time, there were four
staging areas for Hangzhou, Haikou, Dalian and
Kunming to choose from. The five characteristic
values of the guarantee capability data will be
comprehensively scored, with a score range of 1 to 5
points. The following Table 1 and Table 2 are the
example scoring criteria for two of the aspects.
1. Support ability of emergency plan (weight: 25%)
2. Emergency response speed (weight: 20%)
3. Support capacity of emergency equipment
(weight: 25%)
4. Emergency communication support capability
(weight: 20%)
5. Support ability of emergency drill (weight: 10%)
Table 1: Scoring table of emergency equipment support
capacity.
grade appraise code of points
1
To be
improved
There are certain emergency
equipment but the quantity is
insufficient and uneven
distribution.
2 same as
Emergency equipment and
resources can cover basic
emergency situations.
3 preferably
More complete emergency
equipment and resources are
provided with appropriate
maintenance.
4 good
Emergency equipment and
resources are complete, including
high-performance special
equipment, to adapt to all kinds of
emer
g
enc
y
situations.
5
outstanding
Emergency equipment and
resources are very advanced and
sufficient to respond to the most
extreme situations in time.
Combined with the above situation, an airline
company gives four schemes as shown in Table 3:
Table 2: Scortable of emergency communication support
capability.
grade appraise code of points
1 To be improved
The plan is outdated and rarely
updated; emergency
communication equipment
often fails.
2 same as
Plan meet basic needs and are
updated regularly, but problems
may arise in complex
situations.
3 preferably
Emergency communication
plan is detailed, and can
basically meet all kinds of
emergency situations.
4 good
Emergency communication
plan is complete, regular drill,
constantly updated and
optimized.
5 outstanding
The emergency communication
plan includes all kinds of
extreme situation plans, which
are updated regularly and
familiar to all staff.
Table 3: Airline plan sheet.
Plan name
s1 s2 s3 s4
place of
de
p
arture
Hangzhou seaport Dalian Kunming
air-range /km 940 1333 1405 1193
Scheme aircraft A330-300
737-
800
737-
900
A320ceo
Guarantee
abilit
y
4.65 4.45 4.05 4.25
car detention
time under
accumulation/h
3.5 2.5 3.75 4.08
flight time /h
2.25 2.5 3.25 2
Flight cost
/ten thousand
yuan
15.9 10.4 12.8 11.5
In this case, there are 4 decision makers,
composed of two director decision makers and two
deputy director decision makers. The weight ratio of
the 4 decision makers is 0.1:0.4:0.1:0.4. The different
schemes have different preferences, and the
probability given by the four decision makers is as
follows:
Research on Group Decision Model of Civil Aviation Emergency Transport Based on Foreground Regret Theory
79
Q1 =
0.24 0.22
0.23 0.42
0.15 0.08
0.10 0.25
0.33 0.30
0.36 0.14
0.18 0.16
0.20 0.09
Q2 =
0.36 0.22
0.38 0.21
0.23 0.15
0.20 0.20
0.29 0.21
0.27 0.25
0.23 0.09
0.15 0.08
Q3 =
0.29 0.23
0.32 0.22
0.21 0.09
0.18 0.22
0.33 0.24
0.10 0.15
0.20 0.11
0.23 0.14
Q4 =
0.20 0.18
0.22 0.21
0.25 0.12
0.37 0.14
0.15 0.16
0.19 0.24
0.19 0.15
0.21 0.17
4.2 Case Analysis
Reference point: According to the characteristics of
the data in this case, the average value is used as the
reference point. According to support capability,
assembly time, S flight time and flight cost array:
{4.65，4.45，4.05，4.25}；
{3.5，2.5，3.75，4.08}；
{2.25，2.5，3.25，2}；
{15.9，10.4，12.8，11.5}。
Each takes its average: 4.35, 3.4575, 2.5, 12.65.
In this case, the decision maker can directly obtain
the weight ratio of the four indicators of
0.5:0.2:0.2:0.1.
The array of profit and loss values for calculated
guarantee capability, assembly time, flight time and
flight cost are:
{0.3，0.1，-0.3，-0.1}；
{0.425，-0.9575，0.625，0.933}；
{-0.25，0，0.75，-0.5}；
{3.25，-2.25，0.15，-1.15}。
Value function: The value function of support
capability, assembly time, flight time and flight cost
are:
{0.23298，0.06166，-0.65894，-0.21487}；
{0.0219，-2.1525，0.22595，
0.56352}；
{-0.54712，0，0.70603，-1.10951}；
{4.16273，-5.14528，0.10071，-2.59474}。
dimensional integration: it can be calculated from
the dimensional standardization formula, the final
array of value functions is:
{0.69247，0.84679，-0.15022，0.53503}
Decision weight function: The decision weight
function of the four decision makers is respectively:
π
=
0.14869 0.13058
0.29352 0.06656
0.11531 0.28497
0.22357 0.2791
0.22519 0.12848
0.15968 0.16123
0.15669 0.33422
0.07093 0.34971
π
=
0.21673 0.16237
0.25703 0.13463
0.11312 0.34971
0.10841 0.3599
0.15968 0.18104
0.14869 0.13058
0.10766 0.31298
0.12894 0.30201
π
=
0.15968 0.16789
0.27199 0.11967
0.11798 0.31298
0.11243 0.32898
0.18016 0.15402
0.20803 0.16476
0.12318 0.33422
0.10444 0.1863
π
=
0.18978 0.18301
0.20803 0.25223
0.09914 0.26076
0.11203 0.27312
0.21673 0.13694
0.23342 0.14567
0.09679 0.22691
0.13078 0.25437
Cumulative foreground value: The four calculated
cumulative foreground values are:
{0.3811，0.53819，0.49269，0.40646}
{0.34021，0.36866，0.28425，0.2943}
{0.40693，0.48397，0.44156，0.31663}
{0.3635，0.38226，0.35455，0.40896}
Count the regret joy value according to the
formula of regret joy value:
{0.05978，0.1491，0.12366，0.07449}
{0.08846，0.1047，0.05609，0.06194}
{0.09154，0.13514，0.11126，0.03913}
{0.08959，0.1003，0.08444，0.11546}
Combined with the decision weight of the four
decision makers (0.1:0.4:0.1:0.4), the comprehensive
utility value of the group decision is calculated as:
{0.086352，0.110424，0.079704，0.082322}
According to the calculation results, Z
2
> Z
1
> Z
4
>
Z
3
, so scheme 2 is better than others.
After the calculation results are verified by means
of expert visit and inquiry, the calculation results of
this paper are consistent with the actual situation.
The initial data of this model are added to the
method of literature (Wan et al., 2022) and literature
(Zhang and Liu, 2022), with literature(Wan et al.,
2022) calculated using only cumulative prospect
theory and literature(Zhang and Liu, 2022) using only
regret theory. After the calculation, the
comprehensive utility value is obtained as follows:
{0.42182，0.46495，0.40512，0.4094}
{0.36784，0.86385，0.51108，0.45095}
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80
Comprehensive utility values for each scheme are
calculated as described in Table 4.
Table 4: Comprehensive utility value of each scheme.
s
chem
e
Comprehensive
utility value
Literature
(Wan et al.,
2022)
Literature
(Zhang and Liu,
2022)
1 0.086352 0.42182 0.36784
2 0.110424 0.46495 0.86385
3 0.079704 0.40512 0.51108
4 0.082322 0.4094 0.45095
sort Z2>Z1>Z4>Z3 Z2>Z1>Z4>Z3 Z2>Z3>Z4>Z1
Can be seen from the table literature(Wan et al.,
2022)
method to calculate the sort of cumulative
prospect value and prospect-regret theory sort,
compared with only using the cumulative prospect
theory, prospect-regret theory considering the
different choice results for decision makers
psychological regret or happy feeling, more
comprehensive consider the civil aviation decision
makers for the influence of decision results. As the
regret avoidance coefficient changes, so does the
ranking of decision results. After literature
[13]
uses the
regret theory, the scheme ranking is different from the
other two methods, because the theory used in this
paper considers the prospect and risk preference,
more comprehensively considers the psychological
activities, and more in line with the realistic decision
scenario. Therefore, the prospect-regret theory of this
paper is more superior and effective in comparison.
5 CONCLUSIONS
This paper combines cumulative prospect theory and
regret theory to construct a risk decision model
suitable for civil aviation emergency transport group
decision. The model not only focuses on the expected
utility in the decision-making process, but also takes
into account the possible regret and joy of decision
makers in the face of different outputs, so as to more
comprehensively and carefully evaluate the
transportation scheme. This study verifies the
practicability and superiority of the model through
real cases. The model calculation results show that the
group decision model is not only more insightful
when comparing different transport schemes, but also
more accurate in predicting the decision maker
behavior than using a single cumulative prospect
theory or regret theory.
In future applications, the model can provide more
scientific and all-round decision support for airlines'
transportation tasks in emergencies, ensuring the best
solution, while reducing the psychological burden of
decision makers in the selection process. With the
expansion of practical application scenarios, the
generality and stability of the model need to be further
verified and improved, so as to provide more
empirical research basis for aviation emergency
transportation.
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