!!!!Kuwait

The Airport Gate Assignment Problem: Scheduling

Algorithms and Simulation Approach

MARCH, 2012

Ahmed Thanyan AL-Sultan

Graduate School of environmental science

(Doctor Course)

OKAYAMA UNIVERSITY

1

Contents

1 introduction 4

1.1 General preview ………………………………………………… 5

1.2 previous works ………………………………………………….. 7

2 Kuwait International Airport (KIA) 11

2.1 KIA gates terminal ……………………………………………… 12

2.2 Data collection ………………………………………………….. 14

3 Problem description and model formulation 17

3.1 Problem description …………………………………………… 18

3.2 Identify decision variables ………………………………………. 20

3.3 Constraints and objective function ……………………………… 22

4 Algorithms and data generation 24

4.1 Greedy algorithm ……………………………………………….. 25

4.2 Other scheduling algorithms ...……………………………….. 27

4.3 Tabu search heuristic ………………………………………….. 30

4.3.1 New neighborhood search methods …………………….. 30

4.3.2 The interval exchange move ……………………………. 31

4.3.3 The apron exchange move ……………………………… 33

4.3.4 Tabu short-term memory ………………………………. 34

4.4 Data generation ………………………………………………. 35

2

5 Results and analysis 37

5.1 Results ………………………………………………………… 38

5.1.1 Result of objective 1 …………………………………. …. 38

5.1.2 Result of objective 2 …………………………………….. 47

6 Simulation approach 53

6.1 Objective of the simulation approach …………………………. 54

6.2 Arrival rate estimation and simulation steps ………………… 55

6.3 Simulation results ………………………………………………. 57

7 Conclusion 65

Bibliography 68

Acknowledgements 72

Appendix 73

3

Chapter 1 Introduction

4

1.1 General preview

The rapid development of airlines has made airports busier and more

complicated. The assignment of schedule to available gates is a major issue

for daily airline operations. We consider the over-constrained airport gate

assignment problem (AGAP) where the number of flights exceeds the

number of available gates, and where the objectives are to minimize the

number of ungated flights and the total walking distance or connection

times. The procedures used in this project are to create a mathematical

model formulation to identify decision variables to identify, constraints and

objective functions. In addition, we will consider in the AGAP the size of

each gate in the terminal and also the towing process for the aircraft. We

will use a greedy algorithm and a Tabu search meta-heuristic to solve the

problem and compare it with other scheduling methods. Actual and

forecasted data will be simulated in the experiment. The greedy algorithm

minimizes ungated flights while providing initial feasible solutions that

allow flexibility in seeking good solutions, especially in case when flight

schedules are dense in time. Experiments conducts give good results. The

distance a passenger has to walk in any airport to reach various key areas,

including departure gates, baggage belts and connecting flights provide for

an important performance measure for the quality of any airport. While

5

certain walking distances are fixed, others are dynamic. In particular, the

distances traversed by passengers from check-in counters to gates and from

gate to gate, in the case of transfer or connecting passengers, change

according to how scheduled flights are assigned to gates. This allows for

the ground handling agents and airlines, together with airport authorities, to

dynamically assign airport gates to scheduled flights so as to minimize

walking distances while, consequently, minimizing connection times.

Which flight to gate assignment policy to be used so as to achieve such

minimum times can be derived at the start of such planning day based on

published flights schedules and booked passenger loads. The airport gate

assignment problem (AGAP) seeks to find feasible flight to gate

assignments so that total passenger connection times and walking distances

is minimized. Distances that are taken into account are those from check-in

to gates in the case of embarking or originating passengers, from gates to

baggage claim areas (check-out) in the case of disembarking or destination

passengers and from gate to gate in the case of transfer or connecting

passengers. In the over-constrained case, where the number of aircraft

exceeds the number of available gates, we include the distance from the

apron or tarmac area to the terminal for aircraft assigned to these areas.

6

The problem of assigning gates to flight arrivals and departures is an

important decision problem in daily operations at major airports all over the

world. Strong competition between airlines and the increasing demands of

passengers for increased comfort has made the measures of quality in their

decisions at an airport as important performance indices of airport

management. This is why mathematical modeling of this problem and the

application of Operations Research (OR) methods to solve those models

have been studied widely in OR literature. The common characteristics of

busy international airports usually involve serving a large number of

different airlines, a large number of flights over day, and accommodating

various types of aircrafts.

1.2 previous works

Much work has been centered on the gate assigning problem with the

objective of minimizing distance cost (or variants of this). One of the first

attempts to use quantitative means to minimize intra-terminal travel into a

design process was given by Braaksma and Shortreed (1971). The

assignment of aircraft to gates that minimize travel distances is an easily

motivated and understood problem but a difficult one to solve. The total

passenger walking distance is based on passenger embarkation and

7

disembarkation volumes, transfer passenger volumes, gate to gate distances,

check in to gate distances and aircraft to gate assignments. In the gate

assignment problem, the cost associated with the placing of an aircraft at a

gate depends on the distances from key facilities as well as the relations

between these facilities. The basic gate assignment problem is quadratic

assignment problem as shown to be NP-hard in Obata (1979). Babic et al.

(1984) formulated the gate assignment problem as linear 0-1 IP. A branch

and bound algorithm is used to find the optimal solution where transfer

passengers are not considered. Haghani and Chen (1998) proposed an

integer programming formulation of the gate assignment problem and

heuristic solution procedure for solving the problem. The multiple

objective model for the gate assignments were proposed in Yan and Huo

(2001) where the model is formulated as a multiple objective 0-1 integer

programming. Network model (Yan and Chang, 1998) and simulation

models (Cheng, 1998a, b) were also proposed to formulate the problem.

Since the gate assignment problem is NP-hard, various heuristic

approaches have been suggested by researches, e.g. Haghani and Chen

(1998) proposed a heuristic that assigns successive flights parking at the

same gate when there is no overlapping. Flights are assigned based on the

shortest walking distance coefficients. Xu and Bailey (2001) provide a

8

Tabu search meta – heuristic to solve the problem. The algorithm exploits

the special properties of different types of neighborhood moves, and creates

highly effective candidate list strategies. The work of Yan et al. (2008)

considered stochastic disturbances in daily passenger demand that occur in

actual operations. They established a stochastic – demand flight scheduling

model, SDFSM. Two heuristic algorithms, based on arc-based and route

based strategies, were developed to solve the SDFSM. In addition, previous

work (Ding et el., 2004) has considered the over constrained gate

assignment problem which addressed both the objectives of minimizing the

number of ungated aircraft while minimizing total walking distance. In the

work of AL-Sultan, A.T. (2011) considered in the airport gate assignment

problem the size of each gate in the terminal and also the towing process

for the aircraft. In addition, analyses were added for the buffer time that is

the time that locks the aircraft gate after departure. In the current project,

Actual and forecasted data will be simulated in the experiment to estimate

the percentage of the ungated flights and walking distance cost.

Furthermore, we will compare between four aircraft scheduling algorithms

which are greedy algorithm and other three scheduling methods. We will

use actual aircrafts arrival and departure schedules. The number of

9

passengers for each aircraft will be generated randomly using the Monte

Carlo method.

The Airport gate assignment problem (AGAP) seeks to find feasible

flight to gate assignments so that the number of the flights that need be

assigned to the apron and total passenger connection times, as can be

proxies by walking distances, are minimized.

This project is organized as follows. Since we will apply our

mathematical model on a real data, in chapter 2 we explain Kuwait

international airport gates terminal and the actual data collection. Next, we

will describe the problem and explain the objective functions on chapter 3.

In chapter 4, the aircrafts scheduling Algorithms that will be used in this

project will be explained. The analysis of the results will be showed in

chapter 5. The simulation approach will be explained in chapter 6.

10

Chapter 2 Kuwait International Airport (KIA)

11

2.1 KIA gates terminal

We will apply our model at Kuwait international airport (KIA) that has

becomes busier after applying the “Open Skies” policy that applies to both

passenger and cargo operations, forms an essential part of the Kuwait

government’s latest initiative to promote the state as a major centre for

financial, commercial and economic activities in the Gulf Region. KIA is

already serves more than 50 airlines currently connect Kuwait directly with

over 100 international destinations; In addition, there is considerable scope

for expansion. The airport underwent a massive renovation and expansion

project from 1999–2001, in which the former parking lot was cleared and a

terminal expansion was built. This incorporated new check-in areas, a new

entrance to the airport, the construction of a multi-storey parking structure,

and an airport mall. Kuwait International Airport can currently handle more

than seven million passengers a year. A new general aviation terminal was

completed in 2008 under a BOT scheme and is operated by Royal Aviation.

Kuwait international airport has one terminal that has ten gates for the

aircrafts. The gate numbers are 1,2,3,4,5,21,22,24,25 and 26. Figure 2.1

describes the shape of the terminal.

12

Figure 2.1 KIA’s gates terminal

Next to the terminal there are stands (aprons) for the ungated flights,

there are several locations for aircraft stands such as cargo flights stand

area, VIP or privet flight stand area, and the regular stand area which is

used mostly for the ungated commercial aircrafts. In this project, we will

use actual aircraft arrival and departure schedules. The number of

passengers for each aircraft will be generated randomly using the Monte

Carlo method.

13

2.2 Data collection

Table 2.1 presents a sample from the actual schedule (daily movement)

for the arrival and departure flights for a specific week. The rest of the data

is presented in the appendix.

The schedule contents:

1. A/L: Airline.

2. A/C: Type of aircraft.

3. Time: arrival / Departure time.

4. FLT: Flight number.

5. Arrival from / Departure to

6. Gate: gate number.

Table 2.1 Sample of the daily movement

A/L A/C Arrival Departure

FLT From Time Gate FLT To Time Gate JAI 737 574 COK 0040 25 573 COK 0140 25 RJA 319 5256 AMM 0045 3 5257 AMM 0130 3 MEA 320 408 BEY 0110 21 409 BEY 0200 21 THY 737 1172 IST 0115 26 1173 IST 0215 26 MSR 737 614 CAI 0145 22 615 CAI 0245 22

Note that the time which presented in the daily movement is the actual

time for the arrival or departure time. For example, the arrival time for the

first flight in table 2.1 is 0040 which means the aircraft arrived at 00:40.

Furthermore, the rest of the data which is presented in the appendix shows

14

the daily movement for whole week. For example, if a flight arrived at time

14700 mean that the flight arrived at time 21:00 in the 7th day (21*7 = 147).

From the collected data:

- We have 756 arrival flights and 754 departure flights.

- From the arrival flights, we have 212 flights ungated (which means that

28.04% from the arrival flights are ungated).

- From the departure flights, we have 193 flights ungated (which means

that 25.60% from the departure flights are ungated).

Table 2.2 presents the types of aircrafts that could be assigned to each

gate in the terminal. The sign “O” means that the type of aircraft can be

assigned to the gate. We can notice that Gate 1 is the smallest gate in the

terminal since it has the smallest number of types of aircraft which can be

assigned to it. On the other hand, Gate 2, Gate 4, Gate 5, Gate 21 and Gate

22 are considered the largest gates in the terminal since they could be used

to any type of aircraft.

15

Table 2.2 types of aircrafts that could be assigned to each gate in the terminal

A/C Gate1 Gate2 Gate3 Gate4 Gate5 Gate21 Gate22 Gate24 Gate25 Gate26

310 ○ ○ ○ ○ ○ ○ ○ ○ ○ ○

319 ○ ○ ○ ○ ○ ○ ○ ○ ○ ○

320 ○ ○ ○ ○ ○ ○ ○ ○ ○ ○

321 ○ ○ ○ ○ ○ ○ ○ ○ ○ ○

300 ○ ○ ○ ○ ○ ○ ○ ○ ○

330 ○ ○ ○ ○ ○ ○ ○

340 ○ ○ ○ ○ ○ ○ ○

727 ○ ○ ○ ○ ○ ○ ○ ○ ○ ○

737 ○ ○ ○ ○ ○ ○ ○ ○ ○ ○

747 ○ ○ ○ ○ ○ ○

767 ○ ○ ○ ○ ○ ○ ○ ○

777 ○ ○ ○ ○ ○ ○

DC10 ○ ○ ○ ○ ○ ○ ○

E95 ○ ○ ○ ○ ○ ○ ○ ○ ○ ○

MD90 ○ ○ ○ ○ ○ ○ ○ ○

16

Chapter 3 Problem description and model formulation

17

3.1 Problem description

In this project, we consider the airport gate assignment problem

(AGAP), where the number of flights exceeds the number of gates

available. Our main objective is to minimize the number of ungated flights

or minimize the number of flights assigned to the apron and the total

walking distance or connection times. We will consider the size of each

gate in the terminal. We represent iω set of gates that can be assigned to

flight. To show how iω will be used, table 3.1 shows the values of iω for

each type of aircraft that been used in KIA.

Table 3.1 values of iω for each type of aircraft

Type of aircrafts Values of iω

310,319,320,321,727,737,E95 { }26,25,24,22,21,5,4,3,2,1

300,767,MD90 { }26,25,24,22,21,5,4,3,2

767,MD90 { }26,25,22,21,5,4,3,2

330,340,DC10 { }26,22,21,5,4,3,2

747 { }26,22,21,5,4,2

777 { }22,21,5,4,3,2

According to the KIA officials, the towing process for the aircrafts will

be applied if this aircraft is scheduled to a specific gate for more than 6

hours. The towing process means pulling off an aircraft from the terminal

gate to the aircraft stand area. The same process will be used to pull off an

aircraft from the stand area to the terminal gate for departure. According to

the KIA officials, the towing process takes approximately one hour. One of

18

the reasons for using the towing process is if a flight is scheduled to use a

gate for more than 6 hours, we pull off this aircraft from the assigned gate

after one hour from its arrival to give an opportunity to other aircrafts to

use this gate. Table 3.2 shows an example for using the towing process if a

flight is scheduled to use a gate for more than 6 hours.

Table 3.2 example for using the towing process Before applying the towing process


FLT From Time FLT To Time

KAC 320 672 DXB 1400 551 DAM 2100

After applying the towing process


FLT From Time FLT To Time

KAC 320 672 DXB 1400 - - 1500

KAC 320 - - 2000 551 DAM 2100

In addition, we will add a buffer time which is added between two

continuous flights that assigned to the same gate. The time interval locked

for particular aircraft equals to ],[ βα +− ii da . Where and are the

arrival and the departure time of flight i.

ia id

α and β represent the earliest and

the latest time that the flight can be assigned to a gate. In this project, we

will let the sum of α and β represents the buffer timeλ . The goal of buffer

time is to enlarge the interval between any two adjacent aircrafts assigned

to the same gate, which naturally decrease the probability of conflict

between these two aircrafts. It is recommended to let the latest time higher

19

or equal to the earliest time since in KIA (in most of the cases) the delay

departures are more than the early arrivals.

3.2 Identify decision variables.

Notations:

N: Represents set of flights arriving to/departing from the airport.

M: Represents set of gates available at the airport.

n: Total number of flights.

m: Total number of gates.

ia : Arrival time of flight i.

id : Departure time of flight i.

jif , : Number of passengers transferring from flight i to flight j.

lkw , : Walking distance for passengers from gate k to gate l.

α : The earliest time that the flight can be assigned to a gate.

β : The latest time that the flight can be assigned to a gate.

λ : The buffer time which is equal to βα + .

iω : Represents set of gates that can be assigned to flight i.

DF: the difference between the departure and the arrival time (6 hours).

TP: The towing process (1 hour)

20

Additionally, we will make use of two dummy gates. Gate 0 represents

the entrance or exit of the airport, and gate 1+m represents the apron

where flights arrive at when no gates are available.

Hence, represents the walking distance between gate k and the

airport entrance or exit, and represents the number of originating

departure passengers of flight i; represents number of the disembarking

arrival passengers of flight i. So represent the walking distance

between the apron and gate k (usually significantly larger than the distance

among different gates).

okw ,

if ,0

0,if

kmw ,1+

The binary variables

⎩⎨⎧ +≤<

=otherwise 0

. 1) (0 gate toassigned is flight if 1 mkkiyik

The following constraint must be satisfied:

ji kkji ωω ∈∈∀ &),,(

)1(1 +≠== mkyy jkik Implies or ji da > ij da >

This condition disallows any two flights to be scheduled to the same gate

simultaneously (except if they are scheduled to the apron).

21

3.3 Constraints and objective function

Our objective is to minimize the number of flights assigned to the apron

and the total walking distance. The mathematical formulation can be

expressed as follow:

Minimize (3.1) ∑=

+

n

imiy

11,

Minimize (3.2) ∑∑∑∑ ∑ ∑= =

+

=

+

= = =

++n

i

n

j

m

k

m

l

n

i

n

iiiiiljkilkji wfwfyywf

1 1

1

1

1

1 10,0,,0,0,,,,

1

Equation (3.1) refers to the first objective which minimizes the number

of flights assigned to the apron. And equation (3.2) refers to the second

objective which is minimizes the total walking distance. We will call the

value of equation (3.2) the walking distance cost.

The constraints:

1. Ensures that every flight must be assigned to one and only one gate or

assigned to the apron.

.)1,(,1,∑∈

≤≤∀=ik

ki niiyω

Where iω represent set of gates that can be assigned to flight i.

2. Each flight's departure time is later than its arrival time

22

).1,(, niida ii ≤≤∀<

3. Two flights schedule cannot overlap if they are assigned to the same gate

,0))((,, ≤−− jiijkjki adadyy

,1,&,,(

),1, +≠≤≤∈∈∀ jikkji ji mknωω { }.1,0, ∈kiy

23

Chapter 4 Algorithms and data generation

24

4.1 Greedy algorithm

To solve the AGAP, we will use Greedy algorithm which uses a heuristic

methods for minimizing the number of flights assigned to the apron. And

then compare it with other scheduling method. First we will explain the

basic details of greedy algorithm. The steps are as follow:

1. Sort the flights according to the departure time Let

represents the earliest available time (actually the departure

time of last flight) of Gate k Set

).1( nidi ≤≤

)1( mkgk ≤≤

1−=kg for all k.

2. For each flight i.

- Find gate k such that and is maximized; and ik ag < kg ik ω∈ ;

- If such k exists, assign flight i to Gate k, update ik dg = .

- If k does not exist, assign flight i to the apron.

3. Output the result.

Note that in step 2 before assigning flight i to gate k, we will check if

hours. If the answer is yes, we will divide this flight into

two flights and apply the towing process which will take TP=1 hour. The

first flight’s time interval becomes

6=>− DFad ii

]1,[ +ii aa and the second flight’s time

interval become . This means if a flight is scheduled to use a gate ],1[ ii dd −

25

for more than 6 hours, we pull off this aircraft from the assigned gate after

one hour from its arrival to give an opportunity to other aircraft to use this

gate. Then for this flight’s departure, if we find a gate, we will assign this

flight to this gate one hour before its departure.

Proof of the correctness of the greedy algorithm:

By induction, assume we have found the optimum solution after

scheduling flight i by the greedy algorithm. Now by this, we will assign

flight to gate . But the optimal solution is to drop flight and

assign to gate . Hence we can always replace by to make

our greedy solution no worse than the optimal solution. There are two cases

we should consider:

f

'f

k f

)'( ff > 'k 'f f

1. If , since we sort the flight by departure time, we have

as we considered the earliest available time of the gates, we find

the greedy solution is better or at least equal to the optimal solution.

'kk =

kg'

'ff dd ≤

kg ≤

2. If , we find that 'kk ≠ ''kk gg ≤ , and kk gg '' ≤ since we choose the

maximum in the greedy solution. The figure 4.1 illustrates this. kg

26

Figure 4.1 the correctness of the greedy algorithm

4.2 Other scheduling algorithms

In this section, we will present other three scheduling algorithms that

will be compared with greedy algorithm. We call those three scheduling

algorithms Method 1, Method 2 and Method 3. The only difference

between those algorithms and greedy algorithm is the condition of flights

sorting (according to the arrival or departure time) and also the earliest

available time (maximized or minimized). kg

We will explain in more detail the difference between those algorithms

by showing the steps for each aircraft scheduling method.

27

Method 1

1. Sort the flights according to the arrival time Let



).1( nidi ≤≤

)1( mkgk ≤≤

1−=kg for all k.


- Find gate k such that and is minimized; andik ag < kg ik ω∈ ;




Method 2

1. Sort the flights according to the arrival time Let



).1( nidi ≤≤

)1( mkgk ≤≤

1−=kg for all k.


- Find gate k such that and is maximized; and ik ag < kg ik ω∈ ;




28

Method 3

1. Sort the flights according to the departure time Let



).1( nidi ≤≤

)1( mkgk ≤≤

1−=kg for all k.


- Find gate k such that and is minimized; andik ag < kg ik ω∈ ;




Note that we will apply in step 2 for each of the three scheduling

algorithms as we did for greedy algorithm. In step 2, we will apply the

towing process. The conditions are the same which as follow: before

assigning flight i to gate k, we will check if 6=>− DFad ii hours. If the

answer is yes, we will divide this flight into two flights and apply the

towing process which will take TP=1 hour. The first flight’s time interval

becomes and the second flight’s time interval become . ]1,[ +ii aa ],1[ ii dd −

Table 4.1 summarizes the difference between greedy algorithm and other

scheduling algorithms.

29

Table 4.1 difference between greedy algorithm and other scheduling algorithms Method Flight sorting kg greedy departure maximized

Method 1 arrival minimized Method 2 arrival maximized Method 3 departure minimized

4.3 Tabu search heuristic

Tabu search (TS) algorithm is specially designed for the AGAP. TS is

meta-heuristic approach that is recognized as a very effective tool for many

combinatorial optimization problems. The basic TS approach is to search

for the optimum solution with the assistance of an adaptive memory

procedure that proceeds as follows. At each iteration, candidate

neighborhood moves are evaluated, which then lead from the current

solution to a new solution.

4.3.1 New neighborhood search methods

- The insert move: move a single flight to a gate other that the one it

currently assigns. This move is the same as the original insert move.

- The interval exchange move: exchange two flight intervals in the current

assignment. A flight interval consists of one or more consecutive flights in

one gate.

30

- The apron exchange move: exchange one flight which has been assigned

to the apron with a flight that is assigned to a gate currently.

We now discuss the interval exchange move and apron exchange move in

greater detail.

4.3.2 The interval exchange move

The essential reason for the interval exchange move is to find two

compatible intervals, which will allow us to get a feasible solution. In order

to get this, interval data should contain four time points: the earliest

available time (t1), the start time (t2), the end time (t3) and latest available

time (t4). Figure 4.2 illustrates the meaning of these four time points.

Figure 4.2 the four time points of an interval

Further to this, we define two functions on intervals. Extend Left ()

extends the current interval by adding the flight which is just left to it, and

Extend Right () extends the current interval by adding the flight which is

just right to it.

31

Additionally, previous (i) returns the flight just arranged before flight i in

the same gate, next (i) returns the flight just arranged after flight i.

With these, we can now stat an algorithm for finding compatible intervals

in Algorithm 1.

Algorithm 1 finding compatible intervals

1. Select two flights a, b in different gate, where a, b have overlap time.

2. Initialize interval A← a ,interval B← b;

3. A.t1← previous (a). Departure;

4. A.t2 ← a. arrival;

5. A.t3← a. departure;

6. A.t4 ← next (a). Arrival;

7. B.t1← previous (b). Departure;

8. B.t2 ← b.arrival;

9. B.t3 ← b.departure;

10. B.t4 ← next (b). Arrival;

11. Success ← true;

12. While A and B are incompatible and success is true do

13. If (A.t2 < B.t1 and extend left (B))

14. Success ← false;

32

15. If (B.t2 < A.t1 and extend left (A))

16. Success← false;

17. If (A.t3 > B.t4 and extend right (B))


19. If (B.t3 > A.t4 and extend right (A))


21. End while

22. If success

23. Exchange interval A and B;

24. Else output “exchange Failed”;

4.3.3 The apron exchange move

The apron exchange move is used to deal with the flights that are

assigned to the apron. In each move, we exchange one flight that is

assigned to the apron currently with a flight that has been assigned to a gate.

As the minimal number of flights out of the gates has been determined by

the greedy algorithm, we cannot perform a many- many exchange, so we

can only effect a single flight exchange.

33

4.3.4 Tabu short-term memory

Tabu search memory plays an important role in the search process. It

forbids the solution attribute changes recorded in the short-term memory to

be reused. How long a restriction is in effect depends on the tabu tenure

parameter, which identifies the number of iterations a particular restriction

remains in force (F. Glover and M. Laguna, 1997)

In our AGAP problem, since there are three types of neighborhood search

moves, the tabu short-term memory can be implemented as follows (where

iter denotes the current iteration number):

1. Insert Move: denoted as ),( , ),( liki →

tenuretabuiterlikitabu _)),(),(( +=→

to prevent the move ; ),(),( liki →

2. Interval Exchange move: denoted as

tenuretabuiterldckbatabuldckba _)),,(),,((),,,(),,( +=↔↔

to prevent the move ),,(),,( kdclba ↔ ;

3. Apron Exchange Move: denoted as

tenuretabuiterOUTbkatabuOUTbka _),(),((),.(),( +=↔↔

to prevent the move ),(),( kbOUTa ↔ .

34

4.4 Data generation

The arrival time and the departure time for flight i are actual data from

the schedule department at Kuwait international airport. But other data

should be generated or assumed to apply our model. Those data are

concerning the walking distance and number of passengers.

First for the walking distance, we assume that the distance measure

between two gates which are next to each other is 1 unite. For example, if

one passenger arrived at gate 25 his walking distance to the passport

control is 5 units (The distance measure is known as Manhatten Distance).

Table 4.2 represents a summary for the assumed walking distance from a

specific gate to the passport control.

Table 4.2 walking distance

Gate Distance (in units) 1,21 2 2,22 1 3,24 4 4,25 5 5,26 6

Apron 10

We can use table 4.2 to assume the walking distance from gate k to the

airport entrance or exit ( ) or the walking distance from the airport

entrance or exit to gate k ( ). For the transferring passengers, the

walking distance from gate k to gate l ( ) is randomly generated in the

interval [1, 10].

0,kw

kw ,0

lkw ,

35

Now for the arriving passengers and the departing passengers are

randomly generated from different interval sizes depending on the type of

the aircraft. Table 4.3 represents the scenarios to generate the arriving and

the departing passengers.

0,if if ,0

Table 4.3 data generation for the arriving and departing passengers

Type Data Generation

310 [180,280] 319 [80,126] 320 [80,180] 321 [86,186] 300 [235,335] 330 [235,335] 340 [195,295] 727 [87,187] 737 [89,189] 747 [324,424] 767 [88,188] 777 [244,344]

DC10 [270,370] E95 [80,110]

MD90 [87,187]

There are rarely small numbers of passengers transferring from one

flight to another flight. The number of transfer passengers will increase if

flight schedules are close, but not too close (At least 1 hour different). The

number of transferring passengers from flight i to flight j ( ) is usually

within a certain interval, say [1, 50].

jif ,

36

Chapter 5 Results and analysis

37

5.1 Results

We implement R (statistical software) to solve the problem. We have two

objective functions. The first objective function minimizes the number of

flights assigned to the apron. And the second objective function minimizes

the total walking distance. In this chapter, we will present the detailed

results and analysis for both objective functions separately.

5.1.1 Result of objective 1

We will represent the results for each scheduling algorithm for the first

objective function which is minimizing the number of ungated flights. First,

we will use greedy algorithm and then Method 1 then Method 2 and finally

Method 3.

First, we will apply greedy algorithm to obtain initial feasible solutions

for the first objective function. Table 5.1 represents a sample for the output

for the aircraft scheduling. We can notice from the presented table that the

first five flights have no arrival information since those flights arrived in

the previous day.

38

Table 5.1 the output for the aircraft scheduling using greedy algorithm

A/L A/C Arrival Gate Departure From time To time

AXB 737 - - 2 TRV/CCJ 0010 SYZ 320 - - 1 DAM 0020 MSR 320 - - 4 LXR 0030 LZB 320 - - 5 BOJ 0040 IAC 320 - - 21 BOM/MAA 0050 … … … … … … …

RJA 310 AMM 2310 1 AMM 2405 KLM 330 AMS 2315 Apron AMS 2435 JZR 320 DXB 2350 3 - 2450 PIA 310 LHE 2355 22 PEW/LHE 2510

We have used the buffer time (λ which is equal to βα + ) that will be

added between two continuous flights that assigned to the same gate. The

time interval locked for particular aircraft equals to ],[ βα +− idia . By

using greedy algorithm, we tried λ equal to 0, 10, 20 and 30 minutes. In

addition, we have tried 0 4=λ minutes but output gives negative result.

The next results represent the output results for using greedy algorithm for

the incoming and outgoing flights for different values of λ . Table 5.2

represents the output analysis for the ungated flights for different values

ofλ .

39

Table 5.2 output analysis for the ungated flights using greedy algorithm

0=λ 10=λ

Incoming Outgoing Incoming Outgoing

Greedy algorithm 85 82 112 113

Difference from the actual data 127 111 100 80

Saving percentage 59.91% 57.51% 47.17% 41.45%

30=20=λ λ


Greedy algorithm 135 136 168 168



From the output, after applying Greedy algorithm, the total number of

ungated flights for 0=λ is 85 for the incoming flights and 82 for the

outgoing flights. The difference between the actual data and the output is

127 for the incoming flights and 111 for the outgoing flights. Note that the

number of the ungated flights for the actual data is 212 in the incoming

flights and 193 in the outgoing flights. So we have minimized the number

of ungated flights by 59.91% in the incoming and 57.51% for the outgoing

flights.

Choosing the values of α or β depends on the airports conditions. In

KIA the delay departure flights are more than the early arrivals. As a result,

KIA could consider giving a buffer time for the departure flights more than

the arrival flights (i.e. letting βα < ). For example, if KIA agree to let

30=λ minutes then the possible time intervals for flight are i

40

]30,0[ +− ii da and ]20,10[ +− ii da . Both intervals give same result

regarding the number of ungated flights and saving percentage.

We will summary the output for the ungated flights by using greedy

algorithm in table 5.3 by showing the positive and negative results for the

different values of α βand . A positive result means that the saving

percentage for the ungated flights is positive. However, a negative result

means that the saving percentage for the ungated flights is negative. The

(O) mark shows a positive result and (-) mark shows a negative result.

Table 5.3 positive and negative results using different values of α andβ . α β

0 10 20 30 40

0 O O O O - 10 O O O - - 20 O O - - - 30 O - - - - 40 - - - - -

Next, we will apply Method 1 to obtain initial feasible solutions for the

first objective function (minimize the number of flights assigned to the

apron). Table 5.4 represents a sample for the output of the aircraft

scheduling.

41

Table 5.4 the output for the aircraft scheduling using Method 1

A/L A/C Arrival

Gate Departure

From time To time AXB 737 - - 2 TRV/CCJ 0010 SYZ 320 - - 1 DAM 0020 MSR 320 - - 4 LXR 0030 LZB 320 - - 5 BOJ 0040 IAC 320 - - 21 BOM/MAA 0050 … … … … … … …

RJA 310 AMM 2310 25 AMM 2405 UAL 777 - 2315 3 IAD 2345 KLM 330 AMS/BAH 2315 22 AMS 2435 KAC 340 - 2320 21 KUL 2350

The next results represent the output results for using Method 1 for the

incoming and outgoing flights for different values of λ . Table 5.5


ofλ .

Table 5.5 output analysis for the ungated flights using Method 1

0=λ 10=λ


Method 1 93 102 115 122



30=20=λ λ


Method 1 145 147 177 184



From the output, after using Method 1, the total number of ungated

flights for 0=λ is 93 for the incoming flights and 102 for the outgoing

42

flights. The difference between the actual data and the output is 119 for the

incoming flights and 91 for the outgoing flights. We have minimized the

number of ungated flights by 56.13% for the incoming and 47.15% for the

outgoing flights.

Then will apply Method 2 to obtain initial feasible solutions for the first

objective function (minimize the number of flights assigned to the apron).

Table 5.6 represents a sample for the output of the aircraft scheduling.


A/L A/C Arrival

Gate Departure


RJA 310 AMM 2310 4 AMM 2405 UAL 777 - 2315 5 IAD 2345 KLM 330 AMS/BAH 2315 Apron AMS 2435 KAC 340 - 2320 Apron KUL 2350




ofλ .

43


0=λ 10=λ Incoming Outgoing Incoming Outgoing

Method 2 94 99 121 129 Difference from the actual data 118 94 91 64


30=20=λ λ

Incoming Outgoing Incoming Outgoing Method 2 147 154 178 182

Difference from the actual data 65 39 34 11 Saving percentage 30.66% 20.21% 16.04% 5.70%






outgoing flights.

Finally, we will apply Method 3 to obtain initial feasible solutions for the

first objective function (minimize the number of flights assigned to the

apron) Table 5.8 represents a sample for the output of the aircraft

scheduling.

44


A/L A/C Arrival

Gate Departure


RJA 310 AMM 2310 1 AMM 2405 AXB 737 CCJ/IXE 2305 24 IXE/CCJ 2410 KLM 330 AMS/BAH 2315 Apron AMS 2435 JZR 320 DXB 2350 4 - 2450




ofλ .


0=λ 10=λ


Method 3 141 141 156 153



30=20=λ λ


Method 3 178 181 198 200

Difference from the actual data 34 12 14 -7

Saving percentage 16.04% 6.22% 6.60% -3.63%



45




outgoing flights. Note that when we let 30=λ minutes, the output gives

negative result in the outgoing flights since the saving percentage is not

positive.

In table 5.10, we will compare greedy algorithm and the other aircraft

scheduling algorithms (Method 1, Method 2 and Method 3) by taking the

average between the incoming and outgoing flights for the saving

percentage for the ungated flights for different values ofλ .

Table 5.10 Saving percentage averages comparison for the ungated flights

Algorithm 0=λ 10=λ 20=λ 30=λ

Greedy 58.71% 44.31% 32.93% 16.85%

Method 1 51.64% 41.27% 27.72% 10.59%

Method 2 52.18% 38.04% 25.44% 10.87%

Method 3 30.22% 23.58% 11.13% 1.49%

From table 5.10, we conclude that greedy algorithm gives the best result

among all the other scheduling methods regarding the saving percentage

for the ungated flights. Method 1 and Method 2 give almost same

percentage. However, Method 3 gives the worst result among the other

scheduling methods.

46

5.1.2 Result of objective 2

Before applying the scheduling algorithms to get an initial feasible

solution for the first objective function, we have estimated the walking

distance cost for the actual data by generating random data for the number

of passengers and walking distance. The estimated cost was 1,509,752.

In this section, we will use the previous output for the ungated flights to

find the solution for the second objective function (minimize the total

walking distance). To do this, we must generate random data for the

number of passengers and walking distance as explained in the data

generation section in chapter 4. The method that will be used to solve the

second objective function is Tabu search heuristic which was also

explained in chapter 4. First we will present the results by using the results

of greedy algorithm and then Method 1 then Method 2 and finally Method

3.

First, we will use the result of greedy algorithm. Table 5.11 represents

the sample for the generated data for the number of passengers and the

walking distance for each flight.

47

Table 5.11 generated data for the number of passengers and the walking distance (greedy algorithm)

A/L A/C Arrival jf ,0 Gate jiW , jif , 0,if Departure

From time To time AXB 737 - - 0 2 2 0 178 TRV/CCJ 0010 SYZ 320 - - 0 1 1 0 137 DAM 0020 MSR 320 - - 0 4 4 0 127 LXR 0030 LZB 320 - - 0 5 5 0 99 BOJ 0040 IAC 320 - - 0 21 6 0 140 BOM/MAA 0050 DLH 330 - - 0 2 2 0 322 FRA 0055 AFG 310 - - 0 1 1 0 236 KBL 0100 KAC 320 BEY 0005 80 4 4 0 0 - 0105 RJA 319 AMM 0045 107 5 5 44 97 AMM 0130 … … … … … … … … … … …

RJA 310 RJA 2310 212 1 2 0 0 AMM 2405 KLM 330 KLM 2315 327 Apron 8 0 0 AMS 2435 JZR 320 JZR 2350 154 3 4 0 0 - 2450 PIA 310 PIA 2355 246 22 1 0 0 - 2510

Table 5.12 presents the estimated walking distance cost with different

values of λ . The saving percentages are calculated by comparing the

estimated walking distance cost after using the Tabu search heuristic with

the estimated walking distance cost for the actual data which is 1,509,752.

Table 5.12 Estimated walking distance cost and actual data comparison by using greedy algorithm

λ 0 10 20 30

walking distance cost 1242082 1351051 1413588 1497324 Saving percentage 17.73% 10.51% 6.37% 0.82%

With a buffer time 0=λ the walking distance costs was 1,242,082. This

means that the walking distance saving percentage is 17.73%.

Next, we will use the result of Method 1. Table 5.13 represents the

sample for the generated data for the number of passengers and the walking

distance for each flight.

48

Table 5.13 generated data for the number of passengers and the walking distance (Method 1)

A/L A/C Arrival

jf ,0 jiW ,Gate jif , 0,if Departure From time To time

AXB 737 - - 0 1 0 0 134 TRV/CCJ 0010 SYZ 320 - - 0 2 0 0 84 DAM 0020 MSR 320 - - 0 3 0 0 123 LXR 0030 LZB 320 - - 0 4 0 0 135 BOJ 0040 IAC 320 - - 0 5 0 0 123 BOM/MAA 0050 DLH 330 - - 0 21 0 0 239 FRA 0055 AFG 310 - - 0 22 0 0 276 KBL 0100 KAC 320 BEY 0005 158 24 0 0 0 - 0105 JAI 737 COK 0040 96 25 7 39 189 COK 0140 … … … … … … … … … … …

RJA 310 AMM 2310 223 25 3 33 180 AMM 2405 UAL 777 - 2315 0 3 1 16 292 IAD 2345 KLM 330 AMS/BAH 2315 238 22 0 0 295 AMS 2435 KAC 340 - 2320 0 21 6 4 199 KUL 2350


values of λ . With a buffer time 0=λ the walking distance costs was

1,309,913. This means that the walking distance saving percentage is

13.24%. However, the saving percentage is negative when we let 30=λ

minutes.

Table 5.14 Estimated walking distance cost and actual data comparison by using Method 1

λ 0 10 20 30

walking distance cost 1309913 1338398 1442087 1561896 Saving percentage 13.24% 11.35% 4.48% -3.45%

Next, we will use the result of Method 2. Table 5.15 represents the



49


A/L A/C Arrival

jf ,0 Gate jiW , jif , 0,if Departure From time To time

AXB 737 - - 0 1 0 0 134 TRV/CCJ 0010SYZ 320 - - 0 2 0 0 84 DAM 0020MSR 320 - - 0 3 0 0 123 LXR 0030LZB 320 - - 0 4 0 0 135 BOJ 0040IAC 320 - - 0 5 0 0 123 BOM/MAA 0050DLH 330 - - 0 21 0 0 239 FRA 0055AFG 310 - - 0 22 0 0 276 KBL 0100KAC 320 BEY 0005 158 24 0 0 0 - 0105JAI 737 COK 0040 96 3 7 39 189 COK 0140… … … … … … … … … … …

RJA 310 AMM 2310 223 4 3 33 180 AMM 2405UAL 777 - 2315 0 21 1 16 292 IAD 2345KLM 330 AMS/BAH 2315 238 Apron 0 0 295 AMS 2435KAC 340 - 2320 0 Apron 6 4 199 KUL 2350




14.28%. However, the saving percentage is also negative when we

let 30=λ minutes.


λ 0 10 20 30 walking distance cost 1294204 1382662 1467441 1557137

Saving percentage 14.28% 8.42% 2.80% -3.14%

Finally, we will use the result of Method 3. Table 5.17 represents the



50



From time To time AXB 737 - - 0 1 0 0 184 TRV/CCJ 0010 SYZ 320 - - 0 2 0 0 142 DAM 0020 MSR 320 - - 0 3 0 0 128 LXR 0030 LZB 320 - - 0 4 0 0 123 BOJ 0040 IAC 320 - - 0 5 0 0 129 BOM/MAA 0050 DLH 330 - - 0 21 0 0 241 FRA 0055 AFG 310 - - 0 22 0 0 202 KBL 0100 KAC 320 BEY 0005 110 24 0 0 0 - 0105 RJA 319 AMM 0045 131 25 8 21 107 AMM 0130 … … … … … … … … … … …

RJA 310 AMM 2310 253 1 4 3 253 AMM 2405 AXB 737 CCJ/IXE 2305 119 24 8 37 185 IXE/CCJ 2410 KLM 330 AMS/BAH 2315 305 Apron 2 42 244 AMS 2435 JZR 320 DXB 2350 167 4 0 0 0 - 2450




6.05%. However, the saving percentage is negative when we let 20=λ and

30 minutes.


λ 0 10 20 30 walking distance cost 1418381 1449698 1541133 1579837

Saving percentage 6.05% 3.98% -2.08% -4.64%

In table 5.19, we will give a summary for the comparison between the

four scheduling methods by showing the positive and negative results for

the different values of λ regarding the saving percentage for the walking

51

distance cost. The (O) mark shows a positive result. However, (-) mark

shows a negative result.

Table 5.19 positive and negative results for the walking distance cost using different values of λ Algorithm 0=λ 10=λ 20=λ 30=λ 40=λ

Greedy O O O O -

Method 1 O O O - -

Method 2 O O O - -

Method 3 O O - - -

For the second objective function which is minimizing the passengers

walking distance cost. From table 5.19, we also conclude that greedy

algorithm gives the best result among all the other scheduling methods

regarding the saving percentage for the walking distance cost. By using

greedy algorithm, the saving percentage for the walking distance cost gives

positive result until 30=λ minutes. Method 1 and Method 2 give almost

same result. The saving percentage for the walking distance cost for both

Method 1 and Method 2 gives positive result until 20=λ minutes.

However, Method 3 gives the worst result among the other scheduling

methods. The saving percentage for the walking distance cost gives

positive result until 10=λ minutes.

52

Chapter 6 Simulation approach

53

6.1 Objective of the simulation approach

The rapid development of airlines has made airports became busier. As a

result, the problem of assigning gates to flight arrivals and departures is an

important decision problem in daily operations at major airports all over the

world. With the increasing of flights frequency every year, some airport

officials want to estimate how many gates in the terminal will be needed in

the future so that the airport could handle the increasing number of flights.

The objective of this approach is to use an aircraft scheduling algorithm

to estimate the optimum number of gates in the terminal needed in the

future under a specific percentage of the total number of ungated flights

and buffer time limit. Further more, we will analyze the walking distance

estimation in this approach. In order to estimate the optimum number of

gates, in this simulation approach we suppose that the total number of

ungated flights should be at most 20% from the total number of flights and

the latest time that the flight can be assigned to a gate is at least 30 minutes.

In other word, for the flight time interval we will let 30=β minutes and

α be always equal to 0. So we will let the flight time interval equal to

and run the simulation until we find the optimum number of

gates when the total number of ungated flights should be at most 20% from

the total number of flights.

]30, +id[ ia

54

6.2 Arrival rate estimation and simulation steps

Before we explain the simulation steps, we should find the arrival rate

for the aircrafts. We have collected actual data for the incoming aircrafts

frequency. The collected data is monthly data for 5 years period (60

observations). We will use the Stochastic Models Box & Jenkins

methodology (1976) to do forecasting for the number of incoming aircrafts

and then estimate the arrival rate for the aircrafts.

The general Box & Jenkins model of order (p, P, q, Q) abbreviated by

ARIMA (p, d, q) x (P, D, Q) L

And the ARIMA model will be expressed as

tL

QqtdDLL

pp aBBZBBBB )()()1()1)(()( 0 Θ+=−−Φ θθφ

Regarding to our data, after applying Box & Jenkins methodology, the best

model for the incoming aircrafts frequency that has been found is as

follow:

ARIMA (1, 0, 0) x (1, 1, 1)12

The Estimates of Parameters:

9305.01̂ =φ , , and 9844.0ˆ12,1 −=φ 7898.0ˆ

12,1 −=θ 41.560̂ =θ

Therefore, the fitted ARIMA model can be expressed as follow

tt aBZBBB )ˆ1(ˆ)1)(ˆ1)(ˆ1( 1212,10

121212,11 θθφφ −+=−−−

55

tt aBZBBB )7898.01(41.56)1)(9844.01)(9305.01( 121212 ++=−+−

Or

12

252413121

7898.041.569160.09844.00145.00156.09305.0

−

−−−−−

++=+−+−− ZZZZZZ

tt

tttttt

aa

Figure 6.1 shows aircraft arrival pattern with the forecasted data

including upper and lower bound for monthly data. From the results, after 2

years ahead it has been forecasted that in most crowded case that the

number of arrivals will be 401,760 flights. Then the arrival rate is one flight

every 9 minutes (401760 / 31*24*60 = 9). In addition, it has been estimated

that the departure time for each flight is between 90 and 100 minutes.

Figure 6.1 shows aircraft arrival pattern with the forecasted data (AC is the incoming aircrafts frequency)

Time Series Plot for AC(with forecasts and their 95% confidence limits)

6 12 18 24 30 36 42 48 54 60

1500

2500

3500

4500

5500

AC

Time

56

The simulation steps are as follow:

(1) The arrival time for flight i is iai 9= and the departure time is

. [ ]100,90 ++= iii aad

(2) Let the latest time that the flight can be assigned to a gate equal to 30

minutes (in other word we let 30=β minutes).

(3) Types of aircrafts and passengers are generated randomly.

(4) Apply the scheduling algorithm.

(5) Apply Tabu search (TS) algorithm

(6) Run time is one week period.

(7) Do 30 times replicate.

6.3 Simulation results

We will apply our simulation approach for each aircraft scheduling

method. For each method, we will present flights scheduling output,

average and the percentage of the ungated flights from the total flights with

different number of gates and the walking distance cost change percentage.

The walking distance cost change percentage will be calculated by

comparing the estimated walking distance cost by using the simulation

approach and the estimated value for the walking distance cost for the

actual data which is 1,509,752.

57

First, we will apply our simulation approach to greedy algorithm.

Table 6.1 shows a sample of the output for one replicate with number of

gates equal to ten and 30=β minutes.

Table 6.1 simulation output using greedy algorithm

A/L A/C Arrival

jf ,0 Gate jiW , jif , 0,if Departure From time To time

MSR 320 CAI 0009 153 1 0 0 97 CAI 0210 UAE 330 DXB 0018 305 2 5 13 299 DXB 0221 JZR 320 DOH 0027 153 3 2 4 102 AMM 0234 RJA 320 AMM 0036 163 4 3 5 158 AMM 0243 JZR 320 BOM 0045 84 5 8 6 117 HBE 0252 IRA 727 AWZ 0054 180 21 5 43 102 AWZ 0303 JZR 320 BEY 0103 124 22 2 23 177 DAM 0313 JZR 320 SYZ 0112 156 24 0 0 0 - 0321

MEA 330 BEY 0121 269 26 10 26 274 BEY 0321 … … … … … … … … … … …

ETD 320 AUH 2100 168 26 5 22 127 AUH 2310 JAI 737 BOM 2109 179 22 8 25 167 BOM 2314 JZR 320 BOM 2118 163 Apron 7 36 131 HBE 2319 KAC 300 CAI 2127 324 21 2 39 302 CMB 2336

Next, we do all the 30 times replicates for the simulation. Table 6.2

presents the average and the percentage of the ungated flights from the total

flights with different number of gates. Table 6.3 presents the walking

distance cost change percentage using the simulation approach.

Table 6.2 percentage of ungated flights using simulation approach (greedy algorithm) Number of gates 10 11 12

Ungated flights 367 295 225

Percentage 32.73% 26.29% 20.07%

Table 6.3 percentage of walking distance cost using simulation approach (greedy algorithm) Number of gates 10 11 12

Walking distance cost 2208202.9 2133456.7 2080175.4

Change% 46.26% 41.31% 37.78%

58

We conclude that we will reach to out target which is the total number of

ungated flights should be at most 20% of the total flights and the latest

time that the flight can be assigned to a gate at least 30 minutes latest time

when we let the number of gates equal to 12. Or in other word, the current

number of gates in the terminal is 10 gates. After two years KIA should add

two new gates in the terminal if they want to keep the percentage of the

ungated flights at most 20% of the total fights with buffer time equal to 30

minutes.

By letting the number of gates equal to 12, the estimated number of

ungated flights is 225 and the walking distance cost will be increased

37.8%.

Next, we will apply our simulation approach to Method 1.



59

Table 6.4 simulation output using Method 1


From time To time KAC 320 BAH 0009 178 1 0 0 0 - 0213 GFA 320 BAH 0018 151 2 4 2 172 BAH 0226 JZR 320 - 0027 0 3 4 1 124 AMM 0235

MEA 321 BEY 0036 93 4 0 0 116 BEY 0246 IRA 727 SYZ 0045 159 5 9 23 180 SYZ 0245 JZR 320 DOH 0054 166 21 4 27 117 ATZ 0302 JZR 320 - 0103 0 22 9 39 174 SSH 0306 KAC 340 FRA 0112 245 26 0 0 0 - 0318 JZR 320 LCA 0121 120 24 3 46 152 DOH 0330 … … … … … … … … … … …

QTR 321 DOH 2306 117 5 5 36 165 DOH 2516 GFA 330 BAH 2315 282 21 6 24 304 BAH 2517 KAC 310 DOH 2324 205 22 8 8 234 HYD 2534 KAC 320 - 2333 0 26 9 41 141 DOH 2533

Based on 30 times replicates, table 6.5 presents the average and the

percentage of the ungated flights from the total flights with different

number of gates. Table 6.6 presents the walking distance cost change

percentage using the simulation approach.

Table 6.5 percentage of ungated flights using simulation approach (Method 1) number of gates 10 11 12 Ungated flights 366 294 223

percentage 32.68% 26.25% 19.91%

Table 6.6 percentage of walking distance cost using simulation approach (Method 1) number of gates 10 11 12

walking distance cost 2187555.7 2134526.7 2058271.367

change% 44.90% 41.38% 36.33%

The results are almost same as greedy algorithm. Using Method 1 we

conclude also that we will reach to out target when we let the number of

60

gates equal to 12. The estimated number of ungated flights is 223 and the

walking distance cost will be increased 36.33%.

Also we will apply our simulation approach also to Method 2.





From time To time KAC 340 CGK/KUL 0009 250 2 0 0 198 CAI 0218 JZR 320 DXB 0018 95 1 3 34 163 HRG 0228 JZR 320 SAH/BAH 0027 82 3 2 49 85 BOM 0227 JZR 320 MHD 0036 142 4 0 0 0 - 0236 KAC 320 CAI 0045 137 5 2 18 109 BAH 0246 JZR 320 DXB 0054 99 21 0 0 0 - 0255 KAC 310 HYD 0103 276 22 6 3 267 IKA 0309 KAC 320 DXB 0112 134 24 4 36 152 DXB 0317 KAC 777 - 0121 0 Apron 3 31 299 CAI 0327

… … … … … … … … … … … KAC 340 MNL/BKK 2315 221 Apron 0 0 0 - 2521 KAC 777 LHR 2324 305 4 0 0 0 - 2534 OMA 737 MCT 2333 111 2 3 34 135 MCT 2543 KAC 777 BOM 2342 248 22 9 9 341 LHR 2544

Based on 30 replicates, Table 6.8 presents the average and the




61

Table 6.8 percentage of ungated flights using simulation approach (Method 2) Number of gates 10 11 12 Ungated flights 367 295 225

Percentage 32.77% 26.34% 20.09%

Table 6.9 percentage of walking distance cost using simulation approach (Method 2) Number of gates 10 11 12

Walking distance cost 2196257.4 2142121.5 2071356.1 Change% 45.47% 41.89% 37.20%

The results are almost same as the previous two algorithms. Using

Method 2 we conclude also that we will reach to out target when we let the

number of gates equal to 12. The estimated number of ungated flights is

225 and the walking distance cost will be increased 37.20%.

Finally we will apply our simulation approach also to Method 3.





From time To time JZR 320 DOH 0009 131 1 0 0 129 AMM 0209 KAC 300 BOM 0018 276 2 3 26 290 JED 0219 KAC 310 DXB 0027 255 3 4 14 249 AMM 0235 JZR 320 - 0036 0 4 6 30 178 SAW 0244 KAC 340 BOM 0045 283 5 0 0 0 - 0247 KAC 300 TRV 0054 311 21 3 39 330 BEY 0301 DLH 737 FRA 0103 103 22 4 18 186 FRA 0313 JZR 320 DOH 0112 117 24 6 43 110 AMM 0319 AXB 737 CCJ/IXE 0121 136 25 2 42 147 IXE/CCJ 0325

… … … … … … … … … … … KAC 300 CMN/AGP 2315 248 22 7 31 328 CAI 2524 KAC 320 DXB 2324 100 Apron 9 30 101 DXB 2532 JZR 320 DEZ 2333 120 24 8 41 130 LXR 2538 QTR 320 DOH 2342 164 Apron 3 9 124 DOH 2546

62

Based on 30 replicates, Table 6.11 presents the average and the




Table 6.11 percentage of ungated flights using simulation approach (Method 3) Number of gates 10 11 12 Ungated flights 367 294 223

Percentage 32.77% 26.25% 19.91%

Table 6.12 percentage of walking distance cost using simulation approach (Method 3) Number of gates 10 11 12

Walking distance cost 2189787.4 2119571.5 2067572.3 Change% 45.04% 40.39% 36.95%

As expected, the results are almost same as the previous algorithms.

Using Method 3 we conclude also that we will reach to out target when we

let the number of gates equal to 12. The estimated number of ungated

flights is 223 and the walking distance cost will be increased 36.95%.

We will summary the simulation approach outputs for the 4 scheduling

algorithms. Table 6.13 represents the output for each scheduling algorithm

after reaching the simulation target which is the total number of ungated

flights should be at most 20% and the latest time that the flight can be

assigned to a gate is at least 30 minutes. This target will be reached if we

let the number of gates equal to 12 for all scheduling algorithms.

63

Table 6.13 summary of the simulation approach outputs for the 4 scheduling algorithms

Algorithm Percentage of the ungated flights

(Number of gates=12)

Change of walking distance cost percentage

Greedy 20.07% 37.78% Method 1 19.91% 36.33% Method 2 20.09% 37.20% Method 3 19.91% 36.95%

In chapter 5, we have showed by using the actual data that greedy

algorithm gives the best result among all the other scheduling methods

regarding the saving percentage for the ungated flights. But in the

simulation approach, all of the 4 scheduling algorithms give the same result.

We run the simulation model for each flight i and we let the arrival time

and the departure time isiai 9= [ ]100,90 ++= iii aad . In other word, we

let the inter arrival time for every flight is fixed constant and the service

time in the gate is random variable between 90 and 100 minutes. That

explains the similarity of the results for percentage of the ungated flights

and the walking distance cost for all aircrafts scheduling algorithms

(greedy algorithm, Method 1, Method 2 and Method 3) in the simulation

approach output since the only difference between those four algorithms is

the flights sorting method (arrival or departure) and criteria of choosing the

value which is explained in chapter 4. kg

64

Chapter 7 Conclusion

65

In this project, we considered the over constrained The AGAP (Airport

gate assignment problem) to minimize the number of flights assigned to the

apron while minimizing the total walking distances. We have used actual

aircrafts scheduling data from Kuwait international airport for a specific

week period. We provided a greedy algorithm that minimizes the number

of flights not assigned to gates. This algorithm can allocate the flights that

will be ungated as well as to provide an initial feasible solution while

putting in our considerations the size of each gate in the terminal and the

towing process for the aircrafts and the aircraft capacity. In addition, we

added some analysis for the buffer time which is added between two

continuous flights that assigned to the same gate. We then proposed a Tabu

search algorithm with a neighborhood search technique, the interval

exchange move, which is more flexible and more general than previously

employed exchange moves used for this problem. This search move allows

us to find good quality solutions more effectively for more diverse

neighborhoods.

Furthermore, in the AGAP, we have compared greedy algorithm with

other aircrafts scheduling algorithms (named Method 1, Method 2 and

Method 3) by comparing the saving percentage for number of flights

66

assigned to the apron and the total walking distances costs by using

different values of the buffer time.

In the last chapter, we proposed a simulation approach to find the

optimum number of gates required for a specific percentage of the total

number of ungated flights and buffer time limit. In the simulation approach,

we have forecasted the arrival rate for the flights and use greedy algorithm

and other scheduling methods to simulate flights scheduling for one week

period.

67

Bibliography

68

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[4] Al-Sultan,A.T., Ishioka, F. and Kurihara, K.(2011). Aircrafts

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[5] Al-Sultan,A.T., Ishioka, F. and Kurihara, K.(2011). Acomparative study

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[6] Babic, O.,Teodorovic, D. and Tosic, V. (1984). Aircraft stand

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[8] Braaksma, J. and Shortreed, J (1971).Improving airport gate usage with

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pages 187-203.

[9] Cheng, Y. (1998a). Arule- based reactive model for the simulation of

aircraft on airport gates. Knowledge-based systems, 10:225-236.

[10] Cheng, Y. (1998b). Network – based simulation of aircraft at gates in

airport terminals. Journal of Transportation Engineering, pages 188-196.

[11] Ding, H., Lim, A., Rodrigues,B. and Zhu, Y (2004): Aircraft and gate

scheduling optimization at airports. 37th Hawaii international conference

on system sciences, 3, 30074b.

[12] Haghani, A. and Ching, M.C. (1998). Optimizing gate assignments at

airport terminals, Transportation Research part A: Policy and Practice,

32(66): 437-454.

[13] F. Glover and M. Laguna. Tabu search. Kluwer Academic Publishers,

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[14] Obata, T (1979). The quadratic assignment problem: evaluation of

exact and heuristic algorithms. Tech. Report TRS- 7901, Rensselaer

Polytechnic Institute, Troy, New York.

[15] Xu, J. and Bailey, G. (2001). The airport gate assignment problem:

mathematical model and a Tabu search algorithm. 34th Hawaii

international conference on system sciences, 3, 3032.

[16]Yan, S. and Chang, C.M. (1998).A network model for gate assignment.

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[17] Yan, S. and Huo,C. M. (2001). Optimization of multiple objective gate

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71

Acknowledgements

Studying in Japan and especially in Okayama University was a greet

opportunity for me and really helped my research. The research period

which I spend in Okayama University will be always a good memory for

me. I really enjoyed studying in this University everybody was kind and

helpful. I am really happy that I could make a lot of friends in Japan. I

really want to thank my parents and my family for their encouragement and

support during my studying period in Japan.

I would like to give a special thanks to Professor Koji Kurihara who

gave me the opportunity to study in Japan and in Okayama University. I am

really gratitude to his guidance, encouragement and helpful comments and

supports for my research. I also want to express my gratitude to Dr. Fumio

Ishioka for doing the software programming and helping me for my

research. I also want to thank Professor Tomoyuki Tarmi, Kaoru Fueda and

Masaya Iizuka for their helpful comments and suggestions. I want to thank

everyone who gave comment and opinion on my research during the

seminar presentations.

72

Appendix

73

Daily movement for the actual data (One week data)

- Daily movement contents:

1. A/L: Airline.

2. A/C: Type of aircraft.

3. FLT: Flight number.

4. Arrival from / Departure to

5. Gate: gate number.

6. ND: Next day.

- Terminal gates: Gate 1, Gate 2, Gate 3, Gate 4, Gate 5, Gate 21,

Gate 22, Gate 24, Gate 25 and Gate 26.

ID A/L A/C Arrival Departure

FLT From Time Gate FLT To Time Gate 1 AXB - 737 - 0000 26 396 TRV/CCJ 0010 26 2 SYZ - 320 - 0000 22 342 DAM 0020 22 3 MSR - 320 - 0000 24 607 LXR 0030 24 4 LZB - 320 - 0000 - 778 BOJ 0040 Apron 5 IAC - 320 - 0000 - 994 BOM/MAA 0050 Apron 6 DLH - 330 - 0000 5 637 FRA 0055 5 7 AFG - 310 - 0000 4 406 KBL 0100 4 8 KAC 1504 320 BEY 0005 Apron - - - - 9 RJA 5256 319 AMM 0045 3 5257 AMM 0130 3 10 JAI 574 737 COK 0040 25 573 COK 0140 25 11 KAC 544 777 CAI 0050 2 - - - - 12 MEA 408 320 BEY 0110 21 409 BEY 0200 21 13 THY 1172 737 IST 0115 26 1173 IST 0215 26 14 JZR 513 320 SSH 0125 24 - - - - 15 ETH 620 737 ADD 0150 Apron 621 BAH/ADD 0230 Apron 16 MSR 614 737 CAI 0145 22 615 CAI 0245 22 17 JZR 477 320 AYT 0150 4 - - - - 18 JZR 539 320 RMF 0210 Apron - - - - 19 JZR 433 320 MHD 0230 3 - - - - 20 UAE 853 777 DXB 0225 5 854 DXB 0345 5 21 KAC 108 300 LHR 0305 21 - - - - 22 ETD 305 320 AUH 0250 25 306 AUH 0410 25 23 CSA - 320 - - - 295 PRG 0525 22

74


FLT From Time Gate FLT To Time Gate 24 QTR 138 321 DOH 0340 26 139 DOH 0530 26 25 JZR 503 320 LXR 0500 Apron - - - - 26 JZR - 320 - - - 452 DEZ 0605 3 27 JZR 527 320 HBE 0510 5 - - - - 28 JZR - 320 - - - 162 DXB 0615 4 29 JZR 517 320 HRG 0520 Apron - - - - 30 JZR - 320 - - - 446 DOH 0700 24 31 JZR 607 320 BOM 0600 25 - - - - 32 JZR - 320 - - - 478 SAW 0710 1 33 JZR - 320 - - - 164 DXB 0715 5 34 JZR - 320 - - - 406 BAH/DXB 0755 Apron 35 BAW 157 777 LHR 0630 22 156 LHR 0815 22 36 KAC 382 320 DEL 0720 Apron - - - - 37 IRA 615 727 CQD 0735 26 614 CQD 0835 26 38 KAC - 310 - - - 1801 CAI 0845 3 39 JZR - 320 - - - 422 DEL 0850 25 40 KAC 412 340 MNL/BKK 0615 21 171 FRA 0855 21 41 KAC - 777 - - - 117 JFK 0905 2 42 KAC - 310 - - - 1551 DAM 0910 4 43 KAC 206 320 ISB 0715 Apron 561 AMM 0915 Apron 44 JZR 161 320 DXB 0740 24 412 BEY 0920 24 45 GFA 220 320 BAH 0830 Apron 221 BAH 0925 Apron 46 ABY 121 320 SHJ 0850 26 122 SHJ 0935 26 47 UAE 855 330 DXB 0825 5 856 DXB 0940 5 48 QTR 132 321 DOH 0845 22 133 DOH 0945 22 49 KAC - 320 - - - 511 IKA 0955 Apron 50 KAC 676 320 DXB 0800 Apron 671 DXB 1000 Apron 51 JZR - 320 - - - 456 DAM 1005 1 52 ETD 301 320 AUH 0925 21 302 AUH 1010 21 53 JZR - 320 - - - 644 LCA 1020 Apron 54 IRA 603 727 SYZ 0920 25 602 SYZ 1020 25 55 JZR 447 320 DOH 1015 4 416 AMM 1105 4 56 IRC 6801 727 AWZ 1030 24 6802 AWZ 1130 24 57 GFA 211 320 BAH 1045 26 212 BAH 1135 26 58 MEA 404 321 BEY 1100 5 405 BEY 1200 5 59 KAC 372 310 HYD 915 3 1773 RUH 1205 3 60 JZR 453 320 DEZ 1125 25 430 MHD 1215 25 61 JZR 427 320 DXB/BAH 1130 1 344 BAH/SAH 1220 1 62 JZR 165 320 DXB 1120 22 522 HBE 1225 22 63 KAC 284 330 DAC 810 Apron 785 JED 1250 5 64 KAC 332 300 TRV 755 Apron 501 BEY 1300 21 65 MSR 610 320 CAI 1220 4 611 CAI 1320 4 66 JZR 171 320 DXB 1305 24 176 DXB 1350 24 67 JZR - 320 - - - 176 DXB 1350 24 68 ETJ 190 737 BGW 1255 Apron 189 BGW 1355 Apron 69 KAC 362 300 CMB 1010 2 541 CAI 1400 2 70 RJA 800 320 AMM 1335 26 801 AMM 1430 26 71 OMA 643 737 MCT 1400 5 644 MCT 1500 5 72 JZR 479 320 SAW 1450 24 216 IFN 1540 24 73 SVA 500 MD90 JED 1430 21 503 MED/JED 1545 21 74 KAC 672 320 DXB 1415 3 551 DAM 1550 3 75 JZR 413 320 BEY 1520 1 458 DAM 1610 1 76 JZR 457 320 DAM 1515 26 414 BEY 1615 26

75


FLT From Time Gate FLT To Time Gate 77 QTR 134 321 DOH 1505 2 135 DOH 1620 2 78 KAC 1552 310 DAM 1435 4 617 DOH 1625 4 79 KAC - 310 - - - 673 DXB 1630 5 80 KAC 1802 310 CAI 1530 5 673 DXB 1630 5 81 JZR 417 320 AMM 1605 Apron 182 DXB 1655 24 82 KAC 512 320 IKA 1425 22 613 BAH 1700 22 83 JZR 645 320 LCA 1620 24 448 DOH 1710 Apron 84 KAC 562 320 AMM 1420 25 743 DMM 1725 25 85 ETD 303 320 AUH 1710 26 304 AUH 1755 26 86 GFA 213 320 BAH 1705 3 214 BAH 1800 3 87 UAE 857 777 DXB 1655 2 858 DXB 1805 2 88 KAC - 300 - - - 1805 CAI 1810 Apron 89 THA 519 340 BKK 1635 21 520 BKK 1820 21 90 ABY 125 320 SHJ 1745 24 126 SHJ 1825 24 91 SVA 510 MD90 RUH 1720 5 511 RUH 1835 5 92 JZR 177 320 DXB 1755 1 184 DXB 1840 1 93 JZR 431 320 MHD 1750 Apron 486 BAH/DXB 1845 25 94 KAC 178 300 GVA/CDG 1805 Apron - - - - 95 ALK 227 330 CMB/DXB 1800 22 228 DXB/CMB 1910 22 96 JZR 217 320 IFN 1820 Apron 520 HBE 1915 Apron 97 JZR 423 320 DEL 1850 2 512 SSH 1950 2 98 OMA 645 737 MCT 1840 3 646 MCT 2000 3 99 KAC 102 777 JFK/LHR 1925 21 - - - - 100 SIA 458 777 SIN/AUH 1915 4 457 AUH/SIN 2045 4 101 JZR 523 320 HBE 1900 24 694 SYZ 2105 24 102 MEA 402 321 BEY 2020 5 403 BEY 2120 5 103 JAI 572 737 BOM 2030 22 571 BOM 2130 22 104 RJA 802 320 AMM 2045 3 803 AMM 2140 3 105 DLH 630 747 FRA 2055 2 630 DXB 2145 2 106 KAC 786 330 JED 1825 26 281 DAC 2145 26 107 GFA 215 320 BAH 2105 Apron 216 BAH 2150 Apron 108 KAC 502 300 BEY 1910 Apron 361 CMB 2155 Apron 109 KAC 614 320 BAH 2000 25 675 DXB 2210 25 110 KAC 552 320 DAM 2110 Apron - - - - 111 KAC 1774 310 RUH 1525 Apron 343 MAA 2215 Apron 112 KAC 172 340 FRA 2120 Apron - - - - 113 JZR 459 320 DAM 2125 4 188 DXB 2220 4 114 UAE 859 777 DXB 2115 Apron 860 DXB 2225 Apron 115 KAC 674 310 DXB 2100 Apron 381 DEL 2230 Apron 116 OAL 347 737 ATH 2145 24 348 DXB/ATH 2235 24 117 KAC 618 310 DOH 1950 Apron 371 HYD 2240 Apron 118 KAC - 777 - - - 301 BOM 2245 21 119 JZR 449 320 DOH 2040 1 526 HBE 2250 1 120 QTR 136 321 DOH 2200 22 137 DOH 2300 22 121 JZR 345 320 SAH 1935 Apron 528 ATZ 2300 Apron 122 KAC 744 320 DMM 2015 Apron 205 ISB 2305 Apron 123 KAC 542 300 CAI 2050 Apron 353 COK 2310 Apron 124 JZR 407 320 DXB/BAH 2210 3 502 LXR 2315 3 125 JZR 415 320 BEY 2200 5 636 ALP 2320 5 126 JZR 185 320 DXB 2240 25 516 HRG 2325 25 127 MEA 406 330 BEY 2230 26 407 BEY 2330 26 128 KAC - 340 - - - 411 BKK/MNL 2340 2 129 UAL 982 777 IAD 1715 4 981 IAD 2345 4

76


FLT From Time Gate FLT To Time Gate 130 KAC 302 340 BOM 750 Apron 415 KUL 2350 Apron 131 MSR 612 330 CAI 2255 Apron 613 CAI 2355 Apron 132 RJA 5258 310 AMM 2310 Apron 5259 AMM 2405 Apron 133 AXB 389 737 CCJ/IXE 2305 24 390 IXE/CCJ 2410 24 134 KLM 447 330 AMS/BAH 2315 21 447 AMS 2435 21 135 JZR 189 320 DXB 2350 22 - - - - 136 DLH 636 330 FRA 2235 Apron 637 FRA 2455 Apron 137 IAC 981 320 HYD/AMD 2205 Apron 981 AMD/HYD 2505 Apron 138 PIA 205 310 LHE 2355 3 206 PEW/LHE 2510 3 139 JAI 574 737 COK 2440 5 573 COK 2540 5 140 KAC 1806 300 CAI 2450 Apron - - - - 141 MEA 408 320 BEY 2510 26 409 BEY 2600 26 142 THY 1172 737 IST 2515 2 1173 IST 2615 2 143 MSR 614 737 CAI 2545 21 615 CAI 2645 21 144 UAE 853 330 DXB 2625 3 854 DXB 2745 3 145 ETD 305 320 AUH 2650 5 306 AUH 2810 5 146 ETH 620 737 ADD/BAH 2735 26 621 ADD 2820 26 147 QTR 138 330 DOH 2740 2 139 DOH 2925 2 148 JZR 503 320 LXR 2900 21 446 DOH 3100 21 149 JZR 517 320 HRG 2920 22 162 DXB 3015 22 150 KAC 412 340 MNL/BKK 3015 2 - - - - 151 JZR - 320 - - - 164 DXB 3115 25 152 KAC 382 310 DEL 3120 22 547 LXR/SSH 3145 22 153 JZR - 320 - - - 406 BAH/DXB 3155 4 154 JZR 513 320 SSH 2525 4 - - - - 155 JZR 527 320 HBE 2910 3 524 HBE 3200 3 156 BAW 157 777 LHR 3030 5 156 LHR 3215 5 157 DLH 634 737 FRA 2850 Apron 635 FRA 3225 Apron 158 KAC 206 320 ISB 3115 26 545 ALK 3235 26 159 KAC - 320 - - - 677 AUH/MCT 3240 Apron 160 KAC - 320 - - - 1801 CAI 3245 Apron 161 JZR 637 320 ALP 2905 1 422 DEL 3250 1 162 JZR - 320 - - - 478 SAW 3310 24 163 JZR 521 320 HBE 2540 24 - - - - 164 JZR 161 320 DXB 3140 Apron 412 BEY 3320 Apron 165 GFA 220 320 BAH 3230 25 221 BAH 3325 25 166 ABY 121 320 SHJ 3250 4 122 SHJ 3335 4 167 UAE 857 777 DXB 3225 2 856 DXB 3340 2 168 QTR 132 321 DOH 3245 22 133 DOH 3345 22 169 KAC 676 320 DXB 3200 24 671 DXB 3400 24 170 JZR - 320 - - - 456 DAM 3405 Apron 171 JZR 695 320 SYZ 2410 Apron - - 2510 - 172 ETD 301 320 AUH 3325 26 302 AUH 3410 26 173 KAC 344 310 MAA 3310 3 551 DAM 3415 3 174 JZR - 320 - - - 500 LXR 3440 Apron 175 JZR 529 320 ATZ 2915 Apron - - - - 176 JZR 447 320 DOH 3415 1 416 AMM 3505 1 177 SYR 341 320 DAM 3420 22 342 DAM 3520 22 178 GFA 211 320 BAH 3445 24 212 BAH 3535 24 179 KAC 354 300 COK 3405 25 177 CDG/GVA 3540 25 180 JZR 165 320 DXB 3520 26 522 HBE 3625 26 181 KAC 302 777 BOM 3150 21 103 LHR 3630 21 182 KAC 282 330 DAC 3400 4 1785 JED 3655 4

77


FLT From Time Gate FLT To Time Gate 183 KAC 372 300 HYD 3315 5 501 BEY 3700 5 184 MSR 610 330 CAI 3620 3 611 CAI 3720 3 185 JZR 171 320 DXB 3705 24 176 DXB 3750 24 186 ETJ 190 737 BGW 3655 22 189 BGW 3755 22 187 KAC 362 300 CMB 3410 2 541 CAI 3800 2 188 RJA 800 320 AMM 3735 25 801 AMM 3830 25 189 SVA 500 MD90 JED 3830 21 501 JED 3945 21 190 JZR 525 320 HBE 3840 4 818 BAH 3955 4 191 JZR 457 320 DAM 3915 22 458 DAM 4010 22 192 JZR 413 320 BEY 3920 2 414 BEY 4015 2 193 QTR 134 321 DOH 3905 3 135 DOH 4020 3 194 KAC 672 320 DXB 3815 26 617 DOH 4025 26 195 KAC 1802 310 CAI 3930 Apron - - - - 196 KAC 548 310 LXR/SSH 3910 24 673 DXB 4030 24 197 KAC 118 777 JFK 4015 5 1803 CAI 4040 5 198 KAC 552 310 DAM 3940 25 771 RUH 4045 25 199 JZR 479 320 SAW 3850 1 182 DXB 4055 1 200 KAC 678 320 MCT/AUH 3935 Apron 613 BAH 4100 Apron 201 JZR 417 320 AMM 4005 Apron 448 DOH 4110 Apron 202 KAC 546 320 ALY 3950 Apron 743 DMM 4125 Apron 203 LMU 104 320 ALY/ATZ 4050 3 105 ATZ/ALY 4150 3 204 ETD 303 320 AUH 4110 22 304 AUH 4155 22 205 GFA 213 320 BAH 4105 4 214 BAH 4200 4 206 UAE 857 777 DXB 4055 21 858 DXB 4205 21 207 SVA 510 MD90 RUH 4120 26 511 RUH 4235 26 208 ABY 125 320 SHJ 4145 24 126 SHJ 4225 24 209 JZR 177 320 DXB 4155 25 184 DXB 4240 25 210 JZR 427 320 DXB/BAH 3530 Apron 486 BAH/DXB 4245 Apron 211 IRA 3407 300 MHD 4150 Apron 3406 THR 4250 Apron 212 ALK 227 330 CMB/DXB 4200 5 228 DXB/CMB 4310 5 213 KAC 104 747 LHR 4235 22 - - - - 214 JZR 501 320 LXR 4020 Apron 432 MHD 4345 Apron 215 OMA 645 737 MCT 4240 3 646 MCT 4400 3 216 DLH 628 330 FRA 4340 4 628 DMM 4420 4 217 JZR 819 320 BAH 4345 21 606 BOM 4435 21 218 KLM 445 330 AMS 4355 26 445 BAH/AMS 4455 26 219 KAC 772 310 RUH 4405 24 331 TRV 4500 24 220 KAC 674 310 DXB 4500 4 373 HYD 4515 4 221 MEA 402 321 BEY 4420 25 403 BEY 4520 25 222 JAI 572 737 BOM 4430 2 571 BOM 4530 2 223 KAC 1786 330 JED 4225 Apron 281 DAC 4545 Apron 224 RJA 802 320 AMM 4445 3 803 AMM 4540 3 225 KAC 542 300 CAI 4450 Apron - - - - 226 GFA 215 330 BAH 4505 21 216 BAH 4555 21 227 KAC 618 320 DOH 4350 Apron 675 DXB 4610 Apron 228 JZR 459 320 DAM 4525 26 188 DXB 4620 26 229 UAE 859 330 DXB 4515 22 860 DXB 4625 22 230 KAC 614 320 BAH 4400 Apron 381 DEL 4630 Apron 231 QTR 136 321 DOH 4535 24 137 DOH 4635 24 232 KAC 744 320 DMM 4415 Apron 203 LHE 4640 Apron 233 KAC 502 300 BEY 4310 Apron 301 BOM 4645 Apron 234 JZR 415 320 BEY 4600 4 526 HBE 4650 4 235 JZR 449 320 DOH 4440 5 528 ATZ 4700 5

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FLT From Time Gate FLT To Time Gate 236 JZR 407 320 DXB/BAH 4610 25 502 LXR 4715 25 237 JZR 423 320 DEL 4250 1 636 ALP 4720 1 238 JZR 523 320 HBE 4300 Apron 516 HRG 4725 Apron 239 JZR 185 320 DXB 4640 Apron - - - - 240 KAC - 340 - - - 411 BKK/MNL 4740 2 241 UAL 982 777 IAD 4115 2 981 IAD 4745 21 242 MSR 612 330 CAI 4655 3 613 CAI 4755 3 243 AXB 393 737 CCJ/COK 4705 26 394 COK/CCJ 4810 26 244 KAC 1804 777 CAI 4725 22 - - - - 245 PIA 239 737 ISB/SKT 4715 24 240 SKT 4830 24 246 IAC 575 320 MAA/GOI 4330 Apron 576 GOI/MAA 4850 Apron 247 JZR 189 320 DXB 4750 4 - - - - 248 SFW 215 737 KBL 4650 Apron 216 KBL 4900 Apron 249 DLH 629 330 DMM 4820 5 626 FRA 4920 5 250 JAI 574 737 COK 4840 25 573 COK 4940 25 251 THY 1172 737 IST 4915 3 1173 IST 5015 3 252 TAR 327 319 TUN 4935 26 328 DXB/TUN 5025 26 253 MSR 614 321 CAI 4945 24 615 CAI 5045 24 254 UAE 853 777 DXB 5025 2 854 DXB 5145 2 255 ETD 305 320 AUH 5050 21 306 AUH 5210 21 256 QTR 138 321 DOH 5140 22 139 DOH 5330 22 257 CSA 294 320 PRG 5210 5 295 PRG 5325 5 258 DLH 634 737 FRA 5250 25 635 FRA 5625 25 259 BAW 157 777 LHR 5430 2 156 LHR 5615 2 260 UAE 855 330 DXB 5625 22 856 DXB 5740 22 261 GFA 220 320 BAH 5630 Apron 221 BAH 5725 Apron 262 QTR 132 321 DOH 5645 Apron 133 DOH 5745 Apron 263 ETD 301 320 AUH 5725 25 302 AUH 5810 25 264 GFA 211 320 BAH 5845 5 212 BAH 5935 5 265 MEA 404 321 BEY 5900 22 405 BEY 6000 22 266 MSR 610 737 CAI 6020 24 611 CAI 6120 24 267 RJA 800 320 AMM 6135 22 801 AMM 6230 22 268 OMA 643 737 MCT 6200 26 644 MCT 6300 26 269 SVA 500 MD90 JED 6230 3 501 JED 6345 3 270 QTR 134 321 DOH 6305 4 135 DOH 6420 4 271 THA 519 340 BKK 6435 21 520 BKK 6620 21 272 UAE 857 777 DXB 6455 22 858 DXB 6605 22 273 GFA 213 320 BAH 6505 3 214 BAH 6600 3 274 ETD 303 320 AUH 6510 25 304 AUH 6555 25 275 UAL 982 777 IAD 6515 5 981 IAD 7145 5 276 SVA 510 MD90 RUH 6520 4 511 RUH 6635 4 277 ABY 125 320 SHJ 6545 24 126 SHJ 6625 24 278 ALK 227 330 CMB/DXB 6600 2 228 DXB/CMB 6710 2 279 MLR 403 320 CMB/DXB 6615 Apron 404 DXB/CMB 6715 Apron 280 OMA 645 737 MCT 6640 25 646 MCT 6800 25 281 SIA 458 777 SIN/AUH 6715 21 457 AUH/SIN 6845 21 282 SYR 341 320 DAM 6740 24 342 ALP/DAM 6840 24 283 KLM 443 330 AMS 6755 2 443 BAH/AMS 6855 2 284 IAC 993 320 MAA/CCJ 6815 Apron 994 BOM/CCJ 7250 Apron 285 MEA 402 321 BEY 6820 5 403 BEY 6920 5 286 JAI 572 737 BOM 6830 Apron 571 BOM 6930 Apron 287 MSR 618 320 ALY 6835 Apron 619 ALY 6935 Apron 288 RJA 802 320 AMM 6845 Apron 803 AMM 6940 Apron

79


FLT From Time Gate FLT To Time Gate 289 GFA 215 330 BAH 6905 Apron 216 BAH 6955 Apron 290 UAE 859 777 DXB 6915 21 860 DXB 7025 21 291 QTR 136 321 DOH 6935 2 137 DOH 7035 2 292 OAL 347 737 ATH 6945 4 348 DXB/ATH 7040 4 293 DLH 636 330 FRA 7020 3 637 FRA 7255 3 294 MSR 612 330 CAI 7055 Apron 613 CAI 7155 Apron 295 AXB 389 737 CCJ/IXE 7105 24 390 IXE/CCJ 7210 24 296 PIA 205 310 LHE/PEW 7155 25 206 LHE 7310 25 297 KAC 104 777 LHR 6635 3 - - - - 298 KAC - 777 - - - 101 LHR/JFK 5730 5 299 KAC - 777 - - - 1805 CAI 6610 3 300 KAC 676 320 DXB 5600 26 671 DXB 5800 26 301 KAC 672 320 DXB 6215 Apron 1773 RUH 6445 Apron 302 KAC 1774 320 RUH 6805 Apron 675 DXB 7010 Apron 303 KAC 382 320 DEL 5520 Apron 743 DMM 5955 Apron 304 KAC 744 320 DMM 6245 2 617 DOH 6425 2 305 KAC 618 320 DOH 6750 Apron - - - - 306 KAC - 320 - - - 569 SSH/ATZ 5635 Apron 307 KAC 550 320 ATZ 6445 Apron - - - - 308 KAC 204 320 LHE 5510 Apron 1801 CAI 5645 Apron 309 KAC 1802 320 CAI 6330 Apron 613 BAH 6500 Apron 310 KAC 614 320 BAH 6800 Apron - - - - 311 KAC 374 310 HYD 5530 Apron 511 IKA 6340 Apron 312 KAC 512 310 IKA 6745 4 351 COK 6905 4 313 KAC - 310 - - - 561 AMM 5715 Apron 314 KAC 562 310 AMM 6220 25 551 DAM 6350 25 315 KAC 552 310 DAM 6910 25 381 DEL 7030 25 316 KAC 332 300 TRV 5555 4 501 BEY 6100 4 317 KAC 502 300 BEY 6710 3 371 HYD 6915 3 318 KAC 178 300 GVA/CDG 6605 Apron 343 MAA 6900 Apron 319 KAC 302 300 BOM 5555 3 121 CMN/AGP 5750 3 320 KAC 282 330 DAC 5800 2 785 JED 6050 2 321 KAC 786 330 JED 6625 Apron 283 DAC 6755 Apron 322 KAC 412 340 MNL/BKK 5415 21 165 FCO/CDG 5945 21 323 KAC 416 340 CGK/KUL 5435 22 541 CAI 6200 21 324 KAC 542 340 CAI 6850 22 301 BOM 7045 22 325 KAC - 340 - - - 673 DXB 6430 Apron 326 KAC 674 340 DXB 6900 26 411 BKK/MNL 7140 26 327 JZR - 320 - - - 214 IFN 5655 1 328 JZR 215 320 IFN 5935 25 492 JED 6035 25 329 JZR 493 320 JED 6550 Apron 486 BAH/DXB 6645 Apron 330 JZR 189 320 DXB 7150 Apron - - - - 331 JZR 637 320 ALP 5305 26 164 DXB 5515 26 332 JZR 165 320 DXB 5920 3 692 SYZ 6040 3 333 JZR 693 320 SYZ 6310 Apron 182 DXB 6455 Apron 334 JZR 407 320 DXB/BAH 7010 Apron 636 ALP 7120 Apron 335 JZR - 320 - - - 446 DOH 5500 Apron 336 JZR 447 320 DOH 5815 24 416 AMM 5905 24 337 JZR 417 320 AMM 6405 1 448 DOH 6510 1 338 JZR 449 320 DOH 6840 1 526 HBE 7050 1 339 JZR - 320 - - - 646 LCA 6410 Apron 340 JZR 649 320 LCA 7005 Apron 528 ATZ 7100 Apron 341 JZR 517 320 HRG 5320 Apron 406 BAH/DXB 5555 Apron

80


FLT From Time Gate FLT To Time Gate 342 JZR 171 320 DXB 6105 5 176 DXB 6150 5 343 JZR 177 320 DXB 6555 Apron 184 DXB 6640 Apron 344 JZR 185 320 DXB 7040 Apron - - - - 345 JZR 510 320 HBE 5310 Apron 438 IKA 5550 Apron 346 JZR 439 320 IKA 5940 1 522 HBE 6025 1 347 JZR 523 320 HBE 6700 22 436 IKA 6750 22 348 JZR 437 320 IKA 7145 21 - - - - 349 JZR 161 320 DXB 5540 24 412 BEY 5720 24 350 JZR 413 320 BEY 6320 5 414 BEY 6415 5 351 JZR 415 320 BEY 7000 Apron - - - Apron 352 JZR 503 320 LXR 5300 3 162 DXB 5415 3 353 JZR 427 320 DXB/BAH 5930 Apron 342 SAH 6020 Apron 354 JZR 343 320 SAH/BAH 6735 26 606 BOM 6835 26 355 JZR - 320 - - - 456 DAM 5805 Apron 356 JZR 457 320 DAM 6315 24 458 DAM 6410 24 357 JZR 459 320 DAM 6925 24 188 DXB 7020 24 358 JZR 433 320 MHD 4920 4 452 DEZ 5405 4 359 JZR 453 320 DEZ 5925 26 500 LXR 6010 26 360 JZR 501 320 LXR 6550 Apron 502 LXR 7115 Apron 361 JZR 529 320 ATZ 5315 Apron - - - - 362 JZR 607 320 BOM 5400 Apron - - - - 363 MEA 408 320 BEY 7310 22 409 BEY 7400 22 364 THY 1172 737 IST 7315 26 1173 IST 7415 26 365 KNE 703 E95 RUH 8625 25 704 MED 8715 25 366 MSR 614 737 CAI 7345 5 615 CAI 7445 5 367 ETH 620 737 ADD 7350 24 621 BAH/ADD 7430 24 368 JAI 574 737 COK 7240 4 573 COK 7340 4 369 UAE 853 777 DXB 7425 22 854 DXB 7545 22 370 ETD 305 320 AUH 7450 3 306 AUH 7610 3 371 QTR 138 330 DOH 7540 26 139 DOH 7725 26 372 BBC 46 DC10 DAC 7600 Apron 46 DAC 8605 Apron 373 BAW 157 777 LHR 7830 21 156 LHR 8015 21 374 GFA 220 320 BAH 7955 4 221 BAH 8050 4 375 UAE 855 330 DXB 8025 5 856 DXB 8140 5 376 QTR 121 320 DOH 8045 3 133 DOH 8145 3 377 ABY 121 320 SHJ 8050 25 122 SHJ 935 25 378 IRA 603 727 SYZ 8120 21 602 SYZ 8220 21 379 ETD 301 320 AUH 8125 24 302 AUH 8210 24 380 IRC 6791 727 MHD 8150 4 6792 MHD 8250 4 381 GFA 211 320 BAH 8245 2 212 BAH 8335 2 382 MEA 404 321 BEY 8300 22 405 BEY 8400 22 383 MSR 610 321 CAI 8420 3 611 CAI 8520 3 384 MSR 1610 320 CAI 8425 24 1611 CAI 8525 24 385 ETJ 190 737 BGW 8455 Apron 189 BGW 8555 Apron 386 MSR 621 321 ATZ 8525 26 622 ATZ 8625 26 387 SVA 508 MD90 RUH 8530 2 509 MED 8645 2 388 RJA 800 320 AMM 8535 22 801 AMM 8630 22 389 SVA 500 777 JED 8630 21 505 JED 8800 21 390 QTR 134 321 DOH 8705 4 135 DOH 8820 4 391 LMU 110 320 ALY/LXR 8830 5 111 ALY 8930 5 392 IYE 822 737 SAH 8840 24 823 DOH/SAH 8940 24 393 UAE 857 777 DXB 8855 21 858 DXB 9005 21 394 GFA 213 320 BAH 8905 22 214 BAH 9000 22

81


FLT From Time Gate FLT To Time Gate 395 ETD 303 320 AUH 8910 26 304 AUH 8955 26 396 UAL 982 777 IAD 8915 4 981 IAD 9545 5 397 SVA 510 MD90 RUH 8920 2 511 RUH 9035 2 398 ABY 125 320 SHJ 8945 25 126 SHJ 9025 25 399 ALK 227 330 CMB/DXB 9000 3 228 DXB/CMB 9110 3 400 SYR 341 320 DAM 9005 5 342 DAM 9105 5 401 OMA 645 737 MCT 9040 24 646 MCT 9200 24 402 IAC 575 320 MAA/GOI 9130 Apron 576 GOI/MAA 9650 Apron 403 DLH 628 330 FRA 9155 5 628 DMM 9235 5 404 MEA 402 321 BEY 9220 25 403 BEY 9320 25 405 JAI 572 737 BOM 9230 24 571 BOM 9330 24 406 RJA 802 320 AMM 9245 Apron 803 AMM 9340 Apron 407 GFA 215 330 BAH 9305 5 216 BAH 9355 5 408 UAE 859 330 DXB 9315 3 860 DXB 9425 3 409 QTR 136 321 DOH 9335 26 137 DOH 9435 26 410 MEA 406 330 BEY 9430 22 407 BEY 9530 22 411 SAI 441 737 LHE/KHI 9435 Apron 442 LHE 9535 Apron 412 MSR 612 340 CAI 9455 2 613 CAI 9555 2 413 AXB 393 737 CCJ/COK 9505 3 394 COK/CCJ 9610 3 414 KLM 447 330 AMS/BAH 9515 26 447 AMS 9635 26 415 MSR 606 320 LXR 9530 25 607 LXR 9630 25 416 PIA 215 737 KHI 9555 24 216 KHI 9710 24 417 KAC - 777 - - - 117 JFK 8105 2 418 KAC 102 777 JFK/LHR 9125 Apron 301 BOM 9445 Apron 419 KAC 676 320 DXB 8000 Apron 671 DXB 8200 Apron 420 KAC 672 320 DXB 8615 Apron - - - - 421 KAC - 320 - - - 675 DXB 9410 Apron 422 KAC - 320 - - - 617 DOH 8825 Apron 423 KAC 618 320 DOH 9150 Apron 381 DEL 9430 Apron 424 KAC - 320 - - - 545 ALY 8035 Apron 425 KAC 546 320 ALY 8750 25 613 BAH 8900 25 426 KAC 614 320 BAH 9200 Apron 203 LHE 9440 Apron 427 KAC 352 310 COK 8005 Apron - - - - 428 KAC 382 310 DEL 7920 22 561 AMM 8115 22 429 KAC 562 310 AMM 8620 5 547 SSH/LXR 8740 5 430 KAC 548 310 SSH/LXR 9510 Apron - - - - 431 KAC 372 300 HYD 7930 Apron 541 CAI 8600 Apron 432 KAC 542 300 CAI 9250 Apron 343 MAA 9415 Apron 433 KAC 344 300 MAA 8020 Apron 501 BEY 8500 Apron 434 KAC 502 300 BEY 9110 2 1805 CAI 9255 2 435 KAC 122 300 CMN/AGP 7610 24 543 CAI 8045 24 436 KAC 544 300 CAI 8730 2 771 RUH 8845 2 437 KAC 772 300 RUH 9205 26 331 TRV 9300 26 438 KAC - 300 - - - 551 DAM 8110 Apron 439 KAC 552 300 DAM 8635 26 673 DXB 8830 26 440 KAC 674 300 DXB 9300 Apron 353 COK 9510 Apron 441 KAC 284 330 DAC 8010 Apron 785 JED 8450 26 442 KAC 786 330 JED 9025 Apron 281 DAC 9345 Apron 443 KAC 166 340 CDG/FCO 9215 21 415 KUL/CGK 9550 21 444 KAC 412 340 MNL/BKK 7815 26 171 FRA 8055 26 445 KAC 172 340 FRA 9320 Apron - - - - 446 KAC 302 340 BOM 7950 Apron - - - - 447 KAC 103 747 LHR 8430 21 - - - -

82


FLT From Time Gate FLT To Time Gate 448 KAC 1806 777 CAI 7250 2 - - - - 449 JZR - 320 - - - 456 DAM 8205 1 450 JZR 457 320 DAM 8715 1 414 BEY 8815 1 451 JZR 415 320 BEY 9400 25 526 HBE 9450 25 452 JZR 637 320 ALP 7705 22 162 DXB 7815 22 453 JZR 427 320 DXB/BAH 8330 5 430 MHD 8415 5 454 JZR 431 320 MHD 8950 1 486 BAH/DXB 9045 1 455 JZR 189 320 DXB 9550 1 530 ATZ 9645 1 456 JZR 527 320 HBE 7710 Apron 422 DEL 8050 Apron 457 JZR 423 320 DEL 9050 21 538 RMF 9140 21 458 JZR 529 320 ATZ 7715 3 165 DXB 7915 3 459 JZR 165 320 DXB 8320 25 522 HBE 8425 25 460 JZR 523 320 HBE 9100 25 512 SSH 9150 25 461 JZR - 320 - - - 406 BAH/DXB 7955 1 462 JZR 171 320 DXB 8505 4 176 DXB 8550 4 463 JZR 177 320 DXB 8955 Apron 184 DXB 9040 Apron 464 JZR 185 320 DXB 9440 Apron 516 HRG 9525 Apron 465 JZR - 320 - - - 446 DOH 7900 4 466 JZR 447 320 DOH 8215 26 416 AMM 8305 26 467 JZR 417 320 AMM 8805 3 448 DOH 8910 3 468 JZR 449 320 DOH 9240 Apron 636 ALP 9520 Apron 469 JZR - 320 - - - 182 DXB 8855 Apron 470 JZR 407 320 DXB/BAH 9410 24 502 LXR 9515 24 471 JZR 607 320 BOM 7800 25 524 HBE 8000 25 472 JZR 525 320 HBE 8640 3 496 RUH 8730 3 473 JZR 497 320 RUH 9045 22 694 SYZ 9305 22 474 JZR 161 320 DXB 7940 Apron 412 BEY 8120 Apron 475 JZR 413 320 BEY 8720 22 458 DAM 8810 22 476 JZR 459 320 DAM 9325 2 188 DXB 9420 2 477 JZR 503 320 LXR 7700 5 478 SAW 7910 5 478 JZR 479 320 SAW 8650 24 818 BAH 8755 24 479 JZR 819 320 BAH 9145 3 432 MHD 9240 3 480 DLH 629 330 DMM 9620 5 629 FRA 9720 5 481 JAI 574 737 COK 9640 21 573 COK 9740 21 482 THY 1172 737 IST 9715 2 1173 IST 9815 2 483 OAL 346 737 ATH/DXB 9720 3 346 ATH 9810 3 484 MSR 614 737 CAI 9745 24 615 CAI 9845 24 485 UAE 853 330 DXB 9825 5 854 DXB 9945 5 486 ETD 305 320 AUH 9850 3 306 AUH 10010 3 487 SAI 771 737 PEW/MCT 9915 2 772 PEW 10000 2 488 ETH 620 737 ADD/BAH 9935 24 621 ADD 10020 24 489 QTR 138 320 DOH 9940 4 139 DOH 10130 4 490 DLH 634 737 FRA 10050 Apron 635 FRA 10420 Apron 491 BAW 157 777 LHR 10230 4 156 LHR 10415 4 492 IRA 605 727 IFN 10345 25 606 MHD 10445 25 493 UAE 855 330 DXB 10425 5 856 DXB 10540 5 494 GFA 220 320 BAH 10430 24 221 BAH 10525 24 495 QTR 132 320 DOH 10445 21 133 DOH 10545 21 496 ABY 121 320 SHJ 10450 4 122 SHJ 10535 4 497 ETD 301 320 AUH 10525 25 302 AUH 10610 25 498 GFA 211 320 BAH 10645 24 212 BAH 10735 24 499 MSR 601 330 CAI 10820 26 611 CAI 10920 26 500 RJA 800 320 AMM 10935 21 801 AMM 11030 21

83


FLT From Time Gate FLT To Time Gate 501 MSR 621 321 ATZ 10955 5 622 ATZ 11055 5 502 KNE 745 320 JED 11005 25 746 JED 11050 25 503 SVA 500 777 JED 11030 2 505 JED 11200 2 504 QTR 134 321 DOH 11105 24 135 DOH 11220 24 505 SYR 341 320 DAM 11120 5 342 DAM 11220 5 506 THA 519 340 BKK 11235 2 520 BKK 11420 2 507 GFA 213 330 BAH 11245 21 214 BAH 11345 21 508 BAB 344 320 BAH 11250 Apron 345 BAH 11345 Apron 509 UAE 857 777 DXB 11255 5 858 DXB 11405 5 510 ETD 303 320 AUH 1710 3 304 AUH 11355 3 511 UAL 982 777 IAD 11315 22 981 IAD 11945 21 512 SVA 510 MD90 RUH 11320 25 511 RUH 11435 25 513 IRA 617 727 AWZ 11340 Apron 616 AWZ 11440 Apron 514 ABY 125 320 SHJ 11345 24 126 SHJ 11425 24 515 ALK 227 330 CMB/DXB 11400 26 228 DXB/CMB 11510 26 516 MLR 403 320 CMB/DXB 11415 Apron 404 DXB/CMB 11515 Apron 517 OMA 645 737 MCT 11440 5 646 MCT 11600 5 518 SIA 458 777 SIN/AUH 11515 2 457 AUH/SIN 11645 2 519 MEA 402 321 BEY 11545 26 403 BEY 11640 26 520 KLM 445 330 AMS 11555 22 445 BAH/AMS 11655 22 521 JAI 572 737 BOM 11630 4 571 BOM 11730 4 522 MSR 618 320 ALY 11635 21 619 ALY 11735 21 523 RJA 802 320 AMM 11645 Apron 803 AMM 11740 Apron 524 GFA 215 320 BAH 11705 25 216 BAH 11750 25 525 UAE 859 330 DXB 11715 26 860 DXB 11825 26 526 QTR 136 321 DOH 11735 22 137 DOH 11835 22 527 IAC 981 320 HYD/AMD 11805 Apron 982 AMD/HYD 12105 Apron 528 DLH 636 330 FRA 11820 5 637 FRA 12055 5 529 MEA 406 330 BEY 11830 4 407 BEY 11930 4 530 SFW 215 737 KBL 11850 Apron 216 KBL 12100 Apron 531 MSR 612 330 CAI 11855 2 613 CAI 11955 2 532 BBC 43 310 DAC 11900 26 44 DAC 12015 26 533 AXB 389 737 CCJ/IXE 11905 22 390 IXE/CCJ 12010 22 534 MSR 606 320 LXR 11930 3 607 LXR 12030 3 535 AUI 374 737 KBL/DXB 11935 24 382 KBP 12035 24 536 LZB 777 737 VAR 11950 25 778 VAR 12050 25 537 PIA 205 310 LHE 11955 Apron 206 LHE 12110 Apron 538 KAC 118 777 JFK 11215 4 543 CAI 11410 4 539 KAC 302 777 BOM 10350 2 101 LHR/JFK 10530 2 540 KAC - 320 - - - 677 AUH/MCT 10440 Apron 541 KAC 678 320 MCT/AUH 11135 Apron - - - - 542 KAC 676 320 DXB 10400 Apron 617 DOH 11225 Apron 543 KAC 618 320 DOH 11550 Apron 675 DXB 11810 Apron 544 KAC 382 320 DEL 10320 Apron 511 IKA 10555 Apron 545 KAC 512 320 IKA 11025 3 673 DXB 11230 3 546 KAC 674 320 DXB 11700 Apron - - - - 547 KAC 204 320 LHE 10310 26 549 ATZ 10505 26 548 KAC 550 320 ATZ 11140 Apron 613 BAH 11300 Apron 549 KAC 614 320 BAH 11600 Apron 205 ISB 11905 Apron 550 KAC - 310 - - - 671 DXB 10600 Apron 551 KAC 672 310 DXB 11015 4 561 AMM 11135 4 552 KAC 562 310 AMM 11640 Apron 381 DEL 11830 Apron 553 KAC - 310 - - Apron 1551 DAM 10510 Apron

84


FLT From Time Gate FLT To Time Gate 554 KAC 1552 310 DAM 11035 26 771 RUH 11245 26 555 KAC 772 310 RUH 11605 3 351 COK 11705 3 556 KAC 344 300 MAA 10510 Apron 541 CAI 11000 Apron 557 KAC 542 300 CAI 11650 Apron 361 CMB 11805 Apron 558 KAC - 300 - - - 1501 BEY 10450 Apron 559 KAC 1502 300 BEY 11050 Apron 1807 CAI 11320 Apron 560 KAC 332 300 TRV 10355 Apron 501 BEY 10900 Apron 561 KAC 502 300 BEY 11510 24 371 HYD 11715 24 562 KAC 354 300 COK 10605 22 551 DAM 11150 22 563 KAC 552 300 DAM 11710 Apron 301 BOM 11845 Apron 564 KAC 282 330 DAC 10600 3 785 JED 10850 3 565 KAC 786 330 JED 11425 21 283 DAC 11555 21 566 KAC 412 340 MNL/BKK 10215 3 173 FRA/GVA 10455 3 567 KAC 174 340 FRA/GVA 11959 Apron - - - - 568 KAC - 340 - - - 411 BKK/MNL 11940 Apron 569 KAC - 340 - - - 165 FCO/CDG 10745 2 570 KAC 104 747 LHR 11435 3 - - - - 571 JZR 527 320 HBE 10110 24 406 BAH/DXB 10355 24 572 JZR 171 320 DXB 10905 2 176 DXB 10950 2 573 JZR 177 320 DXB 11355 1 486 BAH/DXB 11445 1 574 JZR 189 320 DXB 11950 Apron - - - - 575 JZR 531 320 ATZ 10305 Apron - - - - 576 JZR - 320 - - - 806 BEY 10905 1 577 JZR 807 320 BEY 11455 Apron - - - - 578 JZR - 320 - - - 240 AMM 11315 Apron 579 JZR 241 320 AMM 11830 25 502 LXR 11915 25 580 JZR - 320 - - - 452 DEZ 10205 26 581 JZR 453 320 DEZ 10725 5 344 BAH/SAH 10820 5 582 JZR 345 320 SAH 11535 25 606 BOM 11635 25 583 JZR - 320 - - - 164 DXB 10315 25 584 JZR 165 320 DXB 10720 4 492 JED 10835 4 585 JZR 493 320 JED 11350 Apron 184 DXB 11440 Apron 586 JZR 185 320 DXB 11840 Apron 516 HRG 11925 Apron 587 JZR - 320 - - - 456 DAM 10605 1 588 JZR 457 320 DAM 11115 21 414 BEY 11215 21 589 JZR 415 320 BEY 11800 3 526 HBE 11850 3 590 JZR 637 320 ALP 10105 2 446 DOH 10300 2 591 JZR 447 320 DOH 10615 26 416 AMM 10705 26 592 JZR 417 320 AMM 11205 1 182 DXB 11255 1 593 JZR 407 320 DXB/BAH 11810 24 528 ATZ 11900 24 594 JZR 503 320 LXR 10100 5 438 IKA 10350 5 595 JZR 439 320 IKA 10740 25 522 HBE 10825 25 596 JZR 523 320 HBE 11500 4 512 SSH 11550 4 597 JZR - 320 - - - 162 DXB 10215 22 598 JZR 427 320 DXB/BAH 10730 21 722 SLL 10815 21 599 JZR 723 320 SLL 11340 Apron 476 AYT 11500 Apron 600 JZR 161 320 DXB 10340 22 412 BEY 10520 22 601 JZR 413 320 BEY 11120 25 458 DAM 11210 25 602 JZR 459 320 DAM 11725 2 188 DXB 11820 2 603 JZR - 320 - - - 524 HBE 10400 21 604 JZR 525 320 HBE 11040 Apron 448 DOH 11310 Apron 605 JZR 449 320 DOH 11640 5 218 IFN 11745 5 606 JZR 695 320 SYZ 9610 22 - - - -

85


FLT From Time Gate FLT To Time Gate 607 JZR 513 320 SSH 9725 25 - - - - 608 JZR 539 320 RMF 9730 26 - - - - 609 JZR 433 320 MHD 9830 21 - - - - 610 JZR 517 320 HRG 10120 Apron - - - - 611 KAC 1806 300 CAI 9945 Apron - - - - 612 MEA 408 320 BEY 12110 2 409 BEY 12200 2 613 JAI 574 737 COK 12040 4 573 COK 12140 4 614 THY 1172 737 IST 12115 22 1173 IST 12215 22 615 MSR 614 737 CAI 12145 24 615 CAI 12245 24 616 UAE 853 777 DXB 12225 5 854 DXB 12345 5 617 ETD 305 320 AUH 12250 3 306 AUH 12410 3 618 QTR 138 321 DOH 12340 4 139 DOH 12530 4 619 CSA 294 320 PRG 12410 2 295 PRG 12525 2 620 BAW 157 777 LHR 12630 2 156 LHR 12815 2 621 UAE 855 330 DXB 12825 22 856 DXB 12940 22 622 GFA 220 320 BAH 12830 26 221 BAH 12925 26 623 QTR 132 321 DOH 12845 24 133 DOH 12945 24 624 ABY 121 320 SHJ 12850 5 122 SHJ 12935 5 625 ETD 301 320 AUH 12925 2 302 AUH 13010 2 626 GFA 211 320 BAH 13045 25 212 BAH 13135 25 627 MEA 404 320 BEY 13100 26 405 BEY 13200 26 628 OMA 643 737 MCT 13135 2 644 MCT 1235 2 629 MSR 610 330 CAI 13220 21 611 CAI 13320 21 630 PIA 239 737 SKT 13250 25 240 SKT 13340 25 631 RJA 800 321 AMM 13335 24 801 AMM 13430 24 632 IYE 824 737 SAH/DOH 13355 4 824 SAH 13455 4 633 QTR 134 321 DOH 13505 5 135 DOH 13620 5 634 THA 519 340 BKK 13635 2 520 BKK 13820 2 635 UAE 857 777 DXB 13655 22 858 DXB 13805 22 636 GFA 213 320 BAH 13705 24 214 BAH 13800 24 637 ETD 303 320 AUH 13710 25 304 AUH 13755 25 638 UAL 982 777 IAD 13715 21 981 IAD 14345 22 639 SVA 510 MD90 RUH 13720 26 511 RUH 13835 26 640 ABY 125 320 SHJ 13745 5 126 SHJ 13825 5 641 ALK 227 330 CMB/DXB 13800 4 228 DXB/CMB 13910 4 642 OMA 645 737 MCT 13840 25 646 MCT 14000 25 643 IAC 575 320 MAA/GOI 13930 Apron 576 GOI/MAA 14450 56 644 TAR 327 319 TUN/DXB 13935 22 328 TUN 14025 22 645 DLH 628 330 FRA 13940 2 628 DMM 14010 2 646 MEA 402 321 BEY 13945 5 403 BEY 14040 5 647 JAI 572 737 BOM 14030 Apron 571 BOM 14130 Apron 648 SVA 506 MD90 JED 14035 4 507 JED 14155 4 649 RJA 802 321 AMM 14045 25 803 AMM 14140 25 650 GFA 215 320 BAH 14105 22 216 BAH 14150 22 651 QTR 136 321 DOH 14155 Apron 137 DOH 14255 Apron 652 UAE 859 777 DXB 14200 5 860 DXB 14310 5 653 MEA 406 330 BEY 14230 Apron 407 BEY 14330 Apron 654 SAI 441 737 LHE/KHI 14235 25 442 LHE 14335 25 655 MSR 612 330 CAI 14255 4 613 CAI 14355 4 656 AXB 393 737 CCJ/COK 14305 3 394 COK/CCJ 14410 3 657 KLM 447 330 AMS/BAH 14315 26 447 AMS 14435 26 658 MSR 606 320 LXR 14330 2 607 LXR 14430 2 659 DLH 629 330 DMM 14355 21 629 FRA 14455 21

86


FLT From Time Gate FLT To Time Gate 660 KAC - 777 - - - 117 JFK 12905 21 661 KAC 102 777 JFK/LHR 13925 21 - - - - 662 KAC - 320 - - - 671 DXB 13000 Apron 663 KAC 672 320 DXB 13415 22 561 AMM 13535 22 664 KAC 562 320 AMM 14040 Apron - - - - 665 KAC 676 320 DXB 12800 Apron 743 DMM 13155 Apron 666 KAC 744 320 DMM 13445 Apron 613 BAH 13700 Apron 667 KAC 614 320 BAH 14000 Apron - - - - 668 KAC - 320 - - - 545 ALY 12835 Apron 669 KAC 546 320 ALY 13550 Apron 675 DXB 14210 Apron 670 KAC 206 320 ISB 12715 Apron 551 DAM 12910 Apron 671 KAC 552 320 DAM 13435 Apron 617 DOH 13625 Apron 672 KAC 618 320 DOH 13950 Apron 203 LHE 14240 Apron 673 KAC - 310 - - - 343 MAA 14100 Apron 674 KAC 382 310 DEL 12720 Apron 1803 CAI 13640 Apron 675 KAC 1804 310 CAI 14325 Apron - - - Apron 676 KAC 352 310 COK 12805 Apron 771 RUH 13645 Apron 677 KAC 772 310 RUH 14005 26 381 DEL 14230 26 678 KAC 362 300 CMB 13010 3 541 CAI 13400 3 679 KAC 542 300 CAI 14110 24 351 COK 14305 24 680 KAC - 300 - - - 673 DXB 13630 Apron 681 KAC 674 300 DXB 14100 2 331 TRV 14235 2 682 KAC 372 300 HYD 12730 Apron 501 BEY 13300 Apron 683 KAC 502 300 BEY 13910 Apron - - - - 684 KAC 302 300 BOM 12750 Apron 785 JED 13650 Apron 685 KAC 786 300 JED 14225 Apron - - - - 686 KAC 166 300 CDG/FCO 14015 3 361 CMB 14205 3 687 KAC 284 330 DAC 12810 4 1801 CAI 12955 4 688 KAC 1802 330 CAI 13640 Apron 281 DAC 14145 Apron 689 KAC 416 340 CGK/KUL 12635 3 171 FRA 12855 3 690 KAC 172 340 FRA 14120 21 301 BOM 14245 21 691 KAC - 340 - - - 411 BKK/MNL 14340 Apron 692 KAC - 747 - - - 103 LHR 13230 22 693 KAC 1808 300 CAI 12005 Apron - - 12105 - 694 KAC 544 777 CAI 12050 21 - - 12150 - 695 JZR - 320 - - - 456 DAM 13005 1 696 JZR 457 320 DAM 13515 25 458 DAM 13610 25 697 JZR 459 320 DAM 14125 1 188 DXB 14220 1 698 JZR 503 320 LXR 12500 24 446 DOH 12700 24 699 JZR 447 320 DOH 13015 21 416 AMM 13105 21 700 JZR 417 320 AMM 13605 3 182 DXB 13655 3 701 JZR 407 320 DXB/BAH 14210 Apron - - 14310 - 702 JZR - 320 - - - 524 HBE 12800 1 703 JZR 525 320 HBE 13440 2 496 RUH 13530 2 704 JZR 497 320 RUH 13845 Apron - - 13945 - 705 JZR 517 320 HRG 12520 22 452 DEZ 12605 22 706 JZR 453 320 DEZ 13125 24 500 LXR 13210 24 707 JZR 501 320 LXR 13750 1 486 BAH/DXB 13845 1 708 JZR 189 320 DXB 14350 Apron - - 14450 - 709 JZR 527 320 HBE 12510 5 164 DXB 12715 5 710 JZR 165 320 DXB 13120 5 308 BAH 13330 5 711 JZR 309 320 BAH 13620 4 448 DOH 13710 4 712 JZR 449 320 DOH 14040 Apron 636 ALP 14320 Apron

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FLT From Time Gate FLT To Time Gate 713 JZR - 320 - - - 422 DEL 12850 Apron 714 JZR 423 320 DEL 13850 24 606 BOM 14035 24 715 JZR - 320 - - - 162 DXB 12615 26 716 JZR 427 320 DXB/BAH 13130 4 522 HBE 13225 4 717 JZR 523 320 HBE 13900 Apron 502 LXR 14315 Apron 718 JZR - 320 - - - 406 BAH/DXB 12755 Apron 719 JZR 171 320 DXB 13305 26 176 DXB 13350 26 720 JZR 177 320 DXB 13755 3 184 DXB 13840 3 721 JZR 185 320 DXB 14240 Apron - - 14340 - 722 JZR 161 320 DXB 12740 25 412 BEY 12920 25 723 JZR 413 320 BEY 13520 26 646 LCA 13610 26 724 JZR 649 320 LCA 14205 Apron 526 HBE 14250 Apron 725 JZR - 320 - - - 478 SAW 12710 Apron 726 JZR 479 320 SAW 13450 21 414 BEY 13615 21 727 JZR 415 320 BEY 14200 Apron 528 ATZ 14300 Apron 728 JZR 219 320 IFN 12025 Apron - - 12125 - 729 JZR 513 320 SSH 12125 26 - - 12225 - 730 JZR 477 320 AYT 12150 Apron - - 12250 - 731 JZR 529 320 ATZ 12515 Apron - - 12615 - 732 JZR 607 320 BOM 12600 Apron - - 12700 - 733 PIA 215 737 KHI 14405 24 216 KHI 14520 24 734 JAI 574 737 COK 14440 5 573 COK 14540 5 735 THY 1172 737 IST 14515 4 1173 IST 14615 4 736 OAL 346 737 ATH/DXB 14520 25 346 ATH 14610 25 737 MSR 614 737 CAI 14545 3 615 CAI 14645 3 738 ETH 620 737 ADD 14550 22 621 BAH/ADD 14630 22 739 UAE 853 330 DXB 14625 2 854 DXB 14745 2 740 QTR 138 321 DOH 14740 26 139 DOH 14930 26 741 BAW 157 777 LHR 15030 22 156 LHR 15215 22 742 UAE 855 330 DXB 15225 2 856 DXB 15340 2 743 GFA 220 320 BAH 15230 26 221 BAH 15325 26 744 QTR 132 321 DOH 15245 4 133 DOH 15345 4 745 ABY 121 320 SHJ 15250 Apron 122 SHJ 15335 Apron 746 ETD 301 320 AUH 15325 22 302 AUH 15410 22 747 GFA 211 320 BAH 15445 21 212 BAH 15535 21 748 MSR 610 330 CAI 15620 2 611 CAI 15720 2 749 MSR 621 321 ATZ 15725 24 622 ATZ 15825 24 750 RJA 800 320 AMM 15735 3 801 AMM 15830 3 751 OMA 643 737 MCT 15800 Apron 644 MCT 15900 Apron 752 KNE 745 320 JED 15805 25 746 JED 15850 25 753 SVA 500 777 JED 15830 5 505 JED 16000 5 754 QTR 134 321 DOH 15905 2 135 DOH 16020 2 755 IRC 6791 727 MHD 15955 24 6792 MHD 16055 24 756 THA 519 340 BKK 16035 21 520 BKK 16220 21 757 GFA 213 330 BAH 16045 4 214 BAH 16145 4 758 UAE 857 777 DXB 16055 22 858 DXB 16205 22 759 ETD 303 320 AUH 16110 Apron 304 AUH 16155 Apron 760 UAL 982 777 IAD 16115 2 981 IAD 16745 Apron 761 SVA 510 MD90 RUH 16120 Apron 511 RUH 16235 Apron 762 BAB 344 320 BAH 16135 24 345 BAH 16215 24 763 ABY 125 320 SHJ 16145 Apron 126 SHJ 16225 Apron 764 ALK 227 330 CMB/DXB 16200 Apron 228 DXB/CMB 16310 Apron 765 MLR 403 320 CMB 16215 Apron 404 DXB/CMB 16315 Apron

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FLT From Time Gate FLT To Time Gate 766 OMA 645 737 MCT 16240 Apron 646 MCT 16400 Apron 767 IRA 607 727 MHD 1905 5 604 IFN 16405 5 768 SIA 458 777 SIN/AUH 16315 22 457 AUH/SIN 16445 22 769 KLM 445 330 AMS 16355 3 445 BAH/AMS 16455 3 770 IAC 993 320 MAA/CCJ 16415 Apron - - ND - 771 MEA 402 320 BEY 16420 25 403 BEY 16520 25 772 JAI 572 737 BOM 16430 Apron 571 BOM 16530 Apron 773 MSR 618 320 ALY 16435 Apron 619 ALY 16535 Apron 774 RJA 802 320 AMM 16445 Apron 803 AMM 16540 Apron 775 GFA 215 330 BAH 16505 26 216 BAH 16555 26 776 UAE 859 777 DXB 16515 5 860 DXB 16625 5 777 QTR 136 321 DOH 16535 2 137 DOH 16635 2 778 MEA 406 330 BEY 16630 Apron 407 BEY 16730 Apron 779 DLH 636 330 FRA 16635 26 - - ND - 780 MSR 612 340 CAI 16655 5 613 CAI 16755 5 781 AXB 395 737 CCJ/TRV 16705 25 - - ND - 782 AFG 405 310 KBL 16715 24 - - ND - 783 SYR 341 320 DAM 16720 2 - - ND - 784 MSR 606 320 LXR 16730 4 - - ND - 785 KAC 118 777 JFK 16015 3 543 CAI 16210 3 786 KAC - 777 - - - 101 LHR/JFK 15330 21 787 KAC - 320 - - - 547 LXR 15710 Apron 788 KAC 548 320 LXR 16255 Apron 381 DEL 16630 Apron 789 KAC - 320 - - - 771 RUH 15605 Apron 790 KAC 772 320 RUH 15925 Apron 675 DXB 16610 Apron 791 KAC 676 320 DXB 15200 Apron 617 DOH 16025 Apron 792 KAC 618 320 DOH 16350 Apron - - 16450 - 793 KAC 204 320 LHE 15110 Apron 671 DXB 15400 34 794 KAC 672 320 DXB 15815 Apron 205 ISB 16705 Apron 795 KAC 344 310 MAA 15220 25 551 DAM 15310 25 796 KAC 522 310 DAM 15835 22 561 AMM 16005 22 797 KAC 562 310 AMM 16510 4 371 HYD 16640 4 798 KAC - 310 - - - 1501 BEY 15255 Apron 799 KAC 1502 310 BEY 15855 Apron 613 BAH 16100 Apron 800 KAC 614 310 BAH 16400 Apron - - 16500 - 801 KAC 382 310 DEL 15120 3 161 FCO 15250 3 802 KAC 162 310 FCO 16410 Apron - - 16510 - 803 KAC 352 300 COK 15405 25 177 CDG/GVA 15540 25 804 KAC 332 300 TRV 15350 Apron 331 TRV 16500 Apron 805 KAC - 300 - - - 501 BEY 15700 22 806 KAC 502 300 BEY 16310 24 361 CMB 16605 24 807 KAC - 300 - - - 1801 CAI 15245 Apron 808 KAC 1802 300 CAI 15930 Apron - - 16030 - 809 KAC 362 300 CMB 15410 26 1701 JED 15805 26 810 KAC 1702 300 JED 16320 2 - - 16420 2 811 KAC 282 330 DAC 15400 5 785 JED 15650 5 812 KAC 786 330 JEB 16225 4 283 DAC 16355 4 813 KAC 302 340 BOM 15150 Apron - - 15250 - 814 KAC 412 340 MNL/BKK 15015 4 - - 15115 - 815 KAC - 340 - - - 541 CAI 15800 21 816 KAC 542 340 CAI 16450 21 411 BKK/MNL 16740 21 817 KAC - 340 - - - 301 BOM 16645 51 818 KAC 104 747 LHR 16235 Apron - - 16335 -

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FLT From Time Gate FLT To Time Gate 819 JZR 427 320 DXB/BAH 15210 24 545 DEZ 15500 24 820 JZR 455 320 DEZ 16105 1 436 IKA 16230 1 821 JZR 437 320 IKA 16610 Apron - - 16710 - 822 JZR - 320 - - - 162 DXB 15000 1 823 JZR 165 320 DXB 15355 1 722 SLL 15615 1 824 JZR 723 320 SLL 16140 26 476 AYT 16300 26 825 JZR - 320 - - - 510 SSH 15515 Apron 826 JZR 511 320 SSH 16125 Apron 486 BAH/DXB 16340 Apron 827 JZR - 320 - - - 406 BAH/DXB 15155 Apron 828 JZR 171 320 DXB 15720 4 176 DXB 15835 4 829 JZR 407 320 DXB/BAH 16405 1 460 DAM 16725 1 830 JZR - 320 - - - 456 DAM 15455 Apron 831 JZR 457 320 DAM 15955 Apron - - 16055 - 832 JZR - 320 - - - 526 HBE 16650 1 833 JZR 607 320 BOM 15000 5 342 SAH 15300 5 834 JZR 343 320 SAH/BAH 16020 Apron 606 BOM 16510 Apron 835 JZR 503 320 LXR 14900 24 446 DOH 15025 24 836 JZR 447 320 DOH 15345 3 416 AMM 15505 3 837 JZR 417 320 AMM 16025 26 432 MHD 16055 26 838 JZR 433 320 MHD 16625 Apron - - 16725 - 839 JZR - 320 - - - 492 JED 15525 Apron 840 JZR 493 320 JED 16040 25 448 DOH 16215 25 841 JZR 449 320 DOH 16530 22 516 HRG 16730 22 842 JZR - 320 - - - 412 BEY 15510 Apron 843 JZR 413 320 BEY 16100 5 184 DXB 16200 5 844 JZR 185 320 DXB 16600 3 410 BEY 16720 3 845 JZR - 320 - - - 500 LXR 15610 Apron 846 JZR 501 320 LXR 16150 Apron 502 LXR 16715 Apron 847 JZR - 320 - - - 462 DAM 14401 Apron 848 JZR 461 320 DAM 14905 Apron - - 15005 - 849 JZR 527 320 HBE 14910 Apron - - 15010 - 850 JZR 529 320 ATZ 14915 Apron - - 15015 - 851 JZR 637 320 ALP 14935 Apron - - 15035 -

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