Aviation decision making: Project on limousine selection in Phuket airport Member Dandao sukantanak 5731210093 Manlika Udta 5731210180 Wisaruta Chaowlaka 5731210201 Wissuta Teerageerayut 5731210202 Mae Fah Luang University Academics year 2016 ©copyright by Mae Fah Luang University

2. Decision making in Aviation Business
Present
Aj. Apichat Sopadang
BACHELOR IN
AVIATION LOGISTICS BUSINESS MANAGEMENT
SCHOOL OF MANAGEMENT
MAE FAH LUANG UNIVERSITY
ACADEMICS YEAR 2016
©COPYRIGHT BY MAE FAH LUANG UNIVERSITY
Dandao sukantanak 5731210093
Manlika Udta 5731210180
Wisaruta Chaowlaka 5731210201
Wissuta Teerageerayut 5731210202
Limousine selection in Phuket airport

3. Table of contents
Introduction ................................................................................................................ 1
Criteria for purchasing new limousine .................................................................... 1
Weighting of criteria.................................................................................................. 3
Scoring using Quantitative method
Simple Additive Weighting (SAW) Method ................................................ 7
Weight Product Method (WPM) .................................................................. 9
TOPSIS method ........................................................................................... 10
ELECTRE method ........................................................................................ 13
Scoring using Qualitative method
Analytic Hierarchical Process (AHP) method ......................................... 15
MADM Using Fuzzy TOPSIS .................................................................................. 18
Conclusion of Methods .......................................................................................... 22
Conclusion on MADM Analysis.............................................................................. 22

4. Limousine Selection in Phuket airport
1. Introduction
For our project, as we are limousine agent operated at Phuket airport, we would
like to purchase new limousine that are SUV type, size L, family car and having 5
doors to serve the customers who travel as family group in Phuket. There are 4
alternative cars which are;
1. Ford Everest 3.0L 4x4 LTD Navi AT
2. Toyota Fortuner 3.0V AT 4WD Navi
3. Mitsubishi Pajero Sport 4WD 2.5 VGT GT / 5AT
4. Chevrolet Captiva 2.0 Diesel AT LTZ
2. Criteria for purchasing new limousine
We define related criteria that important to making decision of purchasing
new limousine as shown below.
Criteria for selecting SUV limousine to operate at Phuket airport.
1. Fuel tank (𝑥𝑥11)
2. Fuel eco (𝑥𝑥12)
3. Max power (𝑥𝑥13)
4. Navigation system (𝑥𝑥21)
5. Service center (𝑥𝑥22)
6. Weight (𝑥𝑥3)
7. Price (𝑥𝑥41)
8. Maintenance (𝑥𝑥42)
1

5. For our 4 main areas including mechanical performance, convenience,
weight, and purchasing cost, it can be divided into 8 criteria that we choose
to consider for purchasing new limousine with the most value of usability.
In terms of mechanical performance, we consider that we need the car
with large fuel tank to contain the oil for more duration of work (the more is
better), but it has to be less consuming or fuel economy for more cost saving
from this the more distance (km.) that it can drive is much better for us (the
more is better) to save our cost. Moreover, the max power is also our
additional factor to make decision. It means the more max power is more
preferable for us in terms of efficiency performance of engine for driving (the
more is better).
In terms of convenience, we consider that higher technology of
navigation system is useful for more convenient and better service for
customers (the more is better). In addition, we also consider that the more
service centers is also the important factor for our decision (the more is
better), because, it saves the cost and time as well as more convenient when
the car has problems that need to be repaired or checking and maintenance
period.
For the weight of the car, it also link to the fuel cost that we have to
pay, the more weight of the car is the more fuel consumption (the less is
better). So, we also consider this factor to make the decision.
For purchasing cost area of decision, which is the last part but the
major factor of our decision, we consider the price of the car that it is worth
for the investment or not, although the cheap price is more preferable for us
(the less is better), but it has to consider the quality and efficiency of
usability of the car. Apart from this, the maintenance cost is also the main
factor that impacts the decision. The more duration or period of usability is
more good, as well as spare part cost, maintenance and checking service
cost, etc. are also relate to the maintenance cost that we have to consider.
From this, the cheaper maintenance cost is better and more needed (the less
is better).
2

6. Criteria for good SUV limousine
3. Weighting of criteria
We use ratio weighting method (AHP weighting) as a based calculation
to weight each criteria.
Pair wise comparison of weight
P F.Eco S Main Max F.Tank W N
⎣
⎢
⎢
⎢
⎢
⎢
⎢
⎡
1 2 3 3 4 4 5 6
1/2 1 2 2 3 3 4 5
1/3 1/2 1 1 2 2 3 4
1/3 1/2 1 1 2 2 3 4
1/4 1/3 1/2 1/2 1 1 2 3
1/4 1/3 1/2 1/2 1 1 2 3
1/5 1/4 1/3 1/3 1/2 1/2 1 2
1/6 1/5 1/4 1/4 1/3 1/3 1/2 1⎦
⎥
⎥
⎥
⎥
⎥
⎥
⎤
Price
Fuel Eco
Service Center
Maintenance
Max Power
Fuel Tank
Weight
Navigation
3

7. Contingency check for ratio weighting
Consistency = 0.01400
Sum = 1
⎣
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎡ (1 × 2 × 3 × 3 × 4 × 4 × 5 × 6)
1
8 = 3.10
(1/2 × 1 × 2 × 2 × 3 × 3 × 4 × 5)
1
8 = 2.90
(1/3 × 1/2 × 1 × 1 × 2 × 2 × 3 × 4)
1
8 = 1.30
(1/3 × 1/2 × 1 × 1 × 2 × 2 × 3 × 4)
1
8 = 1.30
(1/4 × 1/3 × 1/2 × 1/2 × 1 × 1 × 2 × 3)
1
8 = 0.77
(1/4 × 1/3 × 1/2 × 1/2 × 1 × 1 × 2 × 3)
1
8 = 0.77
(1/5 × 1/4 × 1/3 × 1/3 × 1/2 × 1/2 × 1 × 2)
1
8 = 0.48
(1/6 × 1/5 × 1/4 × 1/4 × 1/3 × 1/3 × 1/2 × 1)
1
8 = 0.32⎦
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎤
=
⎣
⎢
⎢
⎢
⎢
⎢
⎢
⎡
0.31
0.21
0.13
0.13
0.08
0.08
0.05
0.03⎦
⎥
⎥
⎥
⎥
⎥
⎥
⎤
Geometric Mean
Weight
Price
Fuel Eco
Service Center
Maintenance
Max Power
Fuel Tank
Weight
Navigation
4

8. Data for evaluation of SUV car
Weight
(Ratio)
Ford
Everest
3.0L 4x4 LTD Navi AT
Toyota
Fortuner
3.0V AT 4WD
Navi
Mitsubishi
Pajero Sport
4WD 2.5 VGT GT
/ 5AT
Chevrolet
Captiva
2.0 Diesel AT
LTZ
1. Mechanical performance
1.1 Fuel tank (Liters)
1.2 Fuel eco.( km./Liter )
1.3 Max power (KW(PS)/rpm)
0.08
0.21
0.08
71
10.82
3,200
65
8
3,600
70
11
4,000
65
9
3,800
2. Convenience
2.1 Navigation system(Yes: 5,No: 2)
2.2 Service center (Place)
0.03
0.13
5
2
5
6
2
1
5
2
3. Weight (Kg.)
0.05
2,026 1,960 2,070
1,986
4. Purchasing cost
4.1 Price (million baht)
4.2 Maintenance cost (x<100,000 km.)
0.31
0.13
1.26
29,915
1.44
16,729
1.37
22,185
1.7
29,041
5

9. Data for evaluation of SUV car (Normalized)
Weight
(Ratio)
Ford
Everest
3.0L 4x4 LTD Navi AT
Toyota
Fortuner
3.0V AT 4WD
Navi
Mitsubishi
Pajero Sport
4WD 2.5 VGT GT
/ 5AT
Chevrolet
Captiva
2.0 Diesel AT LTZ
1. Mechanical performance
1.1 Fuel tank (Liters)
1.2 Fuel eco.( km./Liter )
1.3 Max power (KW(PS)/rpm)
0.08
0.21
0.08
0.52
0.55
0.44
0.48
0.41
0.49
0.52
0.56
0.55
0.48
0.46
0.52
2. Convenience
2.1 Navigation system(Yes: 5,No: 2)
2.2 Service center (Place)
0.03
0.13
0.56
0.30
0.56
0.89
0.23
0.15
0.56
0.30
3. Weight (Kg.) 0.05 0.50 0.51 0.49 0.51
4. Purchasing cost
1.1 Price (million baht)
1.2 Maintenance cost (x<100,000 km.)
0.31
0.13
0.56
0.38
0.49
0.67
0.52
0.51
0.42
0.39
6

10. = 0.08(0.52) + 0.21(0.55) + 0.08(0.44) + 0.03(0.56) + 0.13(0.30) + 0.05(0.05) +
0.31(0.56) + 0.13(0.38)
= 0.4961
4. Scoring using Quantitative method
4.1. Simple Additive Weighting (SAW) Method
Vector normalization
Table show relationship between alternative and criteria with weighting
From SAW formula
V (A1) = ∑ 𝑊𝑊(𝑗𝑗) 𝑟𝑟(𝑖𝑖𝑖𝑖)8
𝑗𝑗=1
�
0.52 0.55 0.44 0.56 0.30 0.50 0.56 0.38
0.48 0.41 0.49 0.56 0.89 0.51 0.49 0.67
0.52 0.56 0.55 0.23 0.15 0.49 0.52 0.51
0.48 0.46 0.52 0.56 0.30 0.51 0.42 0.39
�
X11 X12 X13 X21 X22 X3 X41 X42
A1
A2
A3
A4
7

11. V (A2) = ∑ 𝑊𝑊(𝑗𝑗) 𝑟𝑟(𝑖𝑖𝑖𝑖)8
𝑗𝑗=1
V(A3) = ∑ 𝑊𝑊(𝑗𝑗) 𝑟𝑟(𝑖𝑖𝑖𝑖)8
𝑗𝑗=1
V(A3) = ∑ 𝑊𝑊(𝑗𝑗) 𝑟𝑟(𝑖𝑖𝑖𝑖)8
𝑗𝑗=1
In summary for SAW method
The other alternatives have values of V (A1) = 0.4961, V (A2) = 0.5607, V (A3)
= 0.4816 and V(A4) = 0.4388. The preference order is [A2, A1, A3,A4], where the A2
is the first rank and A4 is the last.
= 0.08(0.48) + 0.21(0.41) + 0.08(0.49) + 0.03(0.56) + 0.13(0.89) + 0.05(0.51) +
0.31(0.49) + 0.13(0.67)
= 0.5607
= 0.08(0.52) + 0.21(0.56) + 0.08(0.55) + 0.03(0.23) + 0.13(0.15) +
0.05(0.49) + 0.31(0.52) + 0.13(0.51)
= 0.4816
= 0.08(0.48) + 0.21(0.46) + 0.08(0.52) + 0.03(0.56) + 0.13(0.30) +
0.05(0.51) + 0.31(0.42) + 0.13(0.39)
= 0.4388
8

12. 4.2. Weight Product Method (WPM Weight Product Method)
From weight product formula
V (A1)
V (A2)
V (A3)
V (A4)
= (71)0.08
+ (10.82)0.21
+ (3200)0.08
+ (5)0.03
+ (2)0.13
+ (2026)−0.05
+
(1.26)−0.31
+ (29915)−0.13
= 8.982429
= (65)0.08
+ (8)0.21
+ (3600)0.08
+ (5)0.03
+ (6)0.13
+ (1960)−0.05
+
(1.44)−0.31
+ (16729)−0.13
= 9.041217
= (70)0.08
+ (11)0.21
+ (4000)0.08
+ (2)0.03
+ (1)0.13
+ (2070)−0.05
+
(1.37)−0.31
+ (22185)−0.13
= 8.883946
= (65)0.08
+ (9)0.21
+ (3800)0.08
+ (5)0.03
+ (2)0.13
+ (1986)−0.05
+ (1.7)−0.31
+
(29041)−0.13
= 8.855532
Values of alternatives
Ideal limousine (A*)
The other alternatives have values of
V (A1) = 8.982429, V(A2) = 9.041217,
V(A3) = 8.883964, and V (A4) = 8.855532.
The prefer order is [A2, A1, A3, A4], which is
identical with the order obtained by the SAW
method for this case. The ratios with the
ideal alternative are obtained as
(R1,R2,R3,R4) = 0.975059, 0.98144,
0.96437, 0.961284
∴ R2 is the first rank when compare
with ideal limousine and R4 is the last
Summary of WPM
9

13. 4.3 Technique for order preference by similarity to ideal solution
(TOPSIS) method
Vector normalize rating
Weighted normalize rating with identify positive and negative ideal
Separation measures
From the formula of Separation measures
Separation measures of S* or positive ideal
�
0.52 0.55 0.44 0.56 0.30 0.50 0.56 0.38
0.48 0.41 0.49 0.56 0.89 0.51 0.49 0.67
0.52 0.56 0.55 0.23 0.15 0.49 0.52 0.51
0.48 0.46 0.52 0.56 0.30 0.51 0.42 0.39
�
X11 X12 X13 X21 X22 X3 X41 X42
A1
A2
A3
A4
�
0.0419∗
0.1161 0.0350−
0.0169 0.0388 0.0248 0.1744∗
0.0489−
0.0383 0.0858−
0.0393 0.0169∗
0.1163∗
0.0256∗
0.1526 0.0874∗
0.0413 0.1180∗
0.0437∗
0.0068−
0.0194−
0.0243−
0.1604 0.0659
0.0383−
0.0966 0.0415 0.0169 0.0388 0.0253 0.1292−
0.0503
�
X11 X12 X13 X21 X22 X3 X41 X42
A1
A2
A3
A4
10

14. Separation measures of S- or negative ideal
Table show result from separation measures of S- and s*
11

15. Calculate similarities to positive-ideal solution
From the formula
Table show three sets of preference ranking
12

16. 4.4 Elimination et choice translating reality (ELECTRE) method
Vector normalize rating
Weighted normalize rating with identify positive and negative ideal
Determine concordance and discordance
Concordance Discordance
𝐶𝐶12 = {1,2,4,7}
𝐶𝐶13 = {1,4,5,6,7}
𝐶𝐶14 = {1,2,4,5,7}
𝐶𝐶21 = {3,4,5,6,8}
𝐶𝐶23 = {4,5,6,8}
𝐶𝐶24 = {1,4,5,6,7,8}
𝐶𝐶31 = {2,3,8}
𝐶𝐶32 = {1,2,3,7}
𝐶𝐶34 = {1,2,3,7,8}
𝐶𝐶41 = {3,4,5,6,8}
𝐶𝐶42 = {1,2,3,4}
𝐶𝐶43 = {4,5,6}
𝐷𝐷12 = {3,5,6,8}
𝐷𝐷13 = {2,3,8}
𝐷𝐷14 = {3,6,8}
𝐷𝐷21 = {1,2,7}
𝐷𝐷23 = {1,2,3,7}
𝐷𝐷24 = {2,3}
𝐷𝐷31 = {1,4,5,6,7}
𝐷𝐷32 = {4,5,6,8}
𝐷𝐷34 = {4,5,6}
𝐷𝐷41 = {1,2,7}
𝐷𝐷42 = {5,6,7,8}
𝐷𝐷43 = {1,2,3,7,8}
�
0.52 0.55 0.44 0.56 0.30 0.50 0.56 0.38
0.48 0.41 0.49 0.56 0.89 0.51 0.49 0.67
0.52 0.56 0.55 0.23 0.15 0.49 0.52 0.51
0.48 0.46 0.52 0.56 0.30 0.51 0.42 0.39
�
X11 X12 X13 X21 X22 X3 X41 X42
A1
A2
A3
A4
�
0.0419∗
0.1161 0.0350−
0.0169 0.0388 0.0248 0.1744∗
0.0489−
0.0383 0.0858−
0.0393 0.0169∗
0.1163∗
0.0256∗
0.1526 0.0874∗
0.0413 0.1180∗
0.0437∗
0.0068−
0.0194−
0.0243−
0.1604 0.0659
0.0383−
0.0966 0.0415 0.0169 0.0388 0.0253 0.1292−
0.0503
�
D i d d di d
Concordance Discordance
𝐶𝐶12 = {1,2,4,7}
𝐶𝐶13 = {1,4,5,6,7}
𝐶𝐶14 = {1,2,4,5,7}
𝐶𝐶21 = {3,4,5,6,8}
𝐶𝐶23 = {4,5,6,8}
𝐶𝐶24 = {1,4,5,6,7,8}
𝐶𝐶31 = {2,3,8}
𝐶𝐶32 = {1,2,3,7}
𝐶𝐶34 = {1,2,3,7,8}
𝐶𝐶41 = {3,4,5,6,8}
𝐶𝐶42 = {1,2,3,4}
𝐶𝐶43 = {4,5,6}
𝐷𝐷12 = {3,5,6,8}
𝐷𝐷13 = {2,3,8}
𝐷𝐷14 = {3,6,8}
𝐷𝐷21 = {1,2,7}
𝐷𝐷23 = {1,2,3,7}
𝐷𝐷24 = {2,3}
𝐷𝐷31 = {1,4,5,6,7}
𝐷𝐷32 = {4,5,6,8}
𝐷𝐷34 = {4,5,6}
𝐷𝐷41 = {1,2,7}
𝐷𝐷42 = {5,6,7,8}
𝐷𝐷43 = {1,2,3,7,8}
X11 X12 X13 X21 X22 X3 X41 X42
A1
A2
A3
A4
R =
V =
13

17. Calculate the concordance matrix
Calculate the discordance matrix
Determine the concordance dominance matrix
Determine the discordance dominance matrix
Determine the aggregate dominance matrix
Eliminate the less favorable alternatives
∴ A 4 can be eliminated
�
− 0.63 0.60 0.76
0.42 − 0.34 0.73
0.42 0.68 − 0.81
0.42 0.40 0.21 −
�
�
− 1 0.8791 0.0145
0.3903 − 0.3322 0.1384
1 1 − 0.6223
1 1 1 −
�
�
− 0 0 1
1 − 1 1
0 0 − 1
0 0 0 −
�
�
− 0 0 1
0 − 0 1
0 0 − 1
0 0 0 −
�
A1 A2
A3 A4
F = �
− 1 1 1
0 − 0 1
0 1 − 1
0 0 0 −
�
G =
E =
𝐷𝐷𝑥𝑥 =
C = Total 𝐶𝐶𝑘𝑘𝑘𝑘 = 6.42
Total 𝐷𝐷𝑥𝑥 = 8.38
𝑐𝑐̅ =
∑4
𝑘𝑘=1 ∑ 𝑐𝑐𝑘𝑘2
4
𝑙𝑙=1
4 ×3
= 0.535
𝑑𝑑̅ =
∑ ∑ 𝑑𝑑𝑘𝑘2
4
𝑙𝑙=1
4
𝑘𝑘=1
4 ×3
= 0.698333
14

18. 5. Scoring using Qualitative method
5.1 Analytic Hierarchical Process (AHP) method
Pair wise comparison of alternative on factors
15

19. Final weight factor
Final weight of alternative across risk factor
Final weight of alternatives
16

20. A1 (Ford) = 0.3093
A2 (Toyota) = 0.2592
A3 (Mitsubishi) = 0.2795
A4 (Chevrolet) = 0.1719
17

21. 6. MADM Using Fuzzy TOPSIS
MADM Using Fuzzy Set Theory
Assume that a company is looking to vehicles for taxi use at the airport. After
preliminary screening, A1, A2, A3, and A4 are chosen as an alternative for future evaluation.
A committee of three decision makers, D1, D2 and D3, have been given the
importance weight and rate for each as show in the Table 1 and Table 2 below. Criteria
for choosing the car are:
C1: Fuel tank, C2: Fuel eco C3: Max Power C4: Navigation system
C5: Service center C6: Weight C7: Price C8: Maintenance
Linguistics Variable Transformation
Table1 .Linguistic variables for the importance weight of each criterion
Table 2. Linguistic variable for the rating
Table 3. Graded mean integration representation for the important weight of each
criteria
18

22. Table 4. Graded mean integration representation for the rating
Weight of criteria from 3 DMs
Table 5. The importance weight of the criteria
Criteria Information
Table 6. Decision makers’ rating of the four candidates under all criteria
19

23. Decision Matrix
For the eight criteria, ratings based on the graded mean integration
representation can also be calculated, and finals result can be show as follows:
Rating of four candidate under all critical
Weight alternative of each criteria
The decision matrix can be calculated as follows:
The normalized decision matric can be obtained as follows:
Determine Ideal Solution, determine the “Positive-Ideal solution” and “Negative-Ideal
solution”, the result are shown below.
20

24. Solution: A2-A3-A1-A4
A+
= (0.5696, 0.6494, 0.6474, 0.6315, 0.9019, 0.6051, 0.6712, 0.7743)
A- = (0.4190, 0.2505, 0.3312, 0.2301, 0.2227, 0.3659, 0.1657, 0.2410)
d+
=�∑ (𝑣𝑣�𝑛𝑛
𝑗𝑗=1 R
ij−𝑣𝑣�R
j
+
)2
The distance of each alternative
d-
=�∑ (𝑣𝑣�𝑛𝑛
𝑗𝑗=1 R
ij−𝑣𝑣�R
j
-
)2
Distance Measure and Closeness Coefficient
Table 7. The distance measurement
According to the closeness coefficient, the ranking order of all alternative are
determined
CC1 = 0.4563
CC2 = 0.6602
CC3 = 0.4599
CC4 = 0.2434
21

25. Conclusion of methods
Method
Ranking
A1
Ford
A2
Toyota
A3
Mitsubishi
A4
Chevrolet
Simple Additive Weighting (SAW) 2 1 3 4
Weight Product Method (WPM) 2 1 3 4
TOPSIS method 2 1 3 4
ELECTRE method - - - eliminate
Analytic Hierarchical Process (AHP) 1 3 2 4
MADM Using Fuzzy TOPSIS 3 1 2 4
Conclusion on MADM Analysis
A Numerical
A Numerical: Majority voted
22

26. Average preference ranking from three technique
23