Flow simulation - Casablanca Interrnational Airport

Réalisé par Nabil SI HAMMOU

للمطارات الوطني المكتبNational Office of Airports

National Institute of Statistics and Applied Economics

Modeling and simulation of passenger flow in Casablanca International Airport


Nabil Si HammouOperations Research Engineer

[email protected]

Jury:

M. Ghali Maan Abdellaoui (INSEA)

M. Adelhamid Benttaieb (ONDA)

ONDAINSEA

Introduction:–Outline of the company–Context and objectives

Modeling of passenger processing :–Process description–Data–Management rules

Development of the IT program Test and results Conclusion

Plan

2

Casablanca International Airport

The National Office of Airports (ONDA) is a public and industrial organism with financial autonomy. It was created in 1979 in order to achieve the following goals:

3

The guarantee of the safety of the aviation at the airports and in the air space under national jurisdiction

The guarantee of the safety of the aviation at the airports and in the air space under national jurisdiction

The arrangement, the exploitation, the interview and the development of the civil airports of Morocco.

The arrangement, the exploitation, the interview and the development of the civil airports of Morocco.

The boarding, the landing, the transit and the routing on the ground passengers and goods transported by air

The boarding, the landing, the transit and the routing on the ground passengers and goods transported by air

The connection with the bodies and the international airports to meet the needs of the air traffic.

The connection with the bodies and the international airports to meet the needs of the air traffic.

11

22

33

44

The National Office of Airports (ONDA) ensures the development and exploitation of 15 international airports. The biggest of them is Casablanca International Airport (Mohamed V)

Outline of the companyOutline of the company

The Casablanca International Airport was created in 1980. In 2005, the number of passengers at the airport was more than 5 millions passengers. It holds 3 terminals and 2 runways.

Terminals : The airport holds 3 terminals.

– Total surface of the terminals is 110.000 m2 .

– Total expected capacity is around 11,4 millions passengers annually.

Aerodromes: Besides traffic lanes and parking of aircrafts, the aerodrome consists of 2 runways:

– Width: 45m .

– length : 3720m

4

Surface : 40 000m²Capacity: 5 millions passengers annually




Surface : 4 000m²Capacity: 400 000 passengers annually

Surface : 4 000m²Capacity: 400 000 passengers annually

Terminal 1

Terminal 1

Terminal 2

Terminal 2

Terminal 3

Terminal 3

Casablanca AirportCasablanca Airport


Public hallPublic hall

Check-in desks Check-in desks

Carpets of sorting of luggage

Carpets of sorting of luggage

Customs countersCustoms counters

Immigration formalities Counters

Immigration formalities Counters

Control filtersControl filters

Boarding room.Boarding room.

Departure ZoneDeparture Zone Arrival zoneArrival zone

Immigration formalities CountersImmigration formalities Counters

Transit ZoneTransit Zone

Check-in desks .Check-in desks .

Counters to buy flight ticket.

Counters to buy flight ticket.

Hall.Hall.

Public hallPublic hall

Customs countersCustoms counters

Luggage conveyorsLuggage conveyors

In Each terminal of Casablanca International Airport, we can subdivide the traffic area to three parts: departure zone , arrival zone and transit zone.

Casablanca AirportCasablanca Airport

Context and objectivesContext and objectives

Department of Casablanca Airport ( DCA ) works to ensure the good quality and continuity of services, and for the safety and security of air traffic at the Casablanca airport. For these reasons, many decisions are taken in order to achieve the target.

6

Strategic Tactical Operations

Horizon decision

Traffic forecasting Planning of airport infrastructure

Traffic forecasting Planning of airport infrastructure

Scheduling Scheduling

Operations controlDaily planningOperations controlDaily planning


Every day at the evening, the Department of Casablanca Airport (DCA) collaborate with other organisms of Casablanca Airport (Handling firms like Royal Air Maroc, Police and Customs ) in order to plan the allocation of resources for meeting the expected traffic in the next day.


Flight Planning CoordinationResource allocation

The optimal resource planning have to keep a total duration of passenger processing lower and the surface of each queue respected.

7

The objective of DCA is to determine the Optimal number of each type of resource should be assigned in order to ensure the good quality of passenger processing. This optima should be proved by a quantitative approach in order to enhance the quality of the estimation and facilitate consequently the negotiation with the other organisms (Royal Air Maroc, Police and Customs).

For that, given a flight planning for the next day, DCA have to estimate at first the consequence of each resource allocation scenario on the quality of passenger processing by expecting (1) the duration of passenger processing and (2) the congestion.

The duration of passenger processing and the congestion result from the interaction of both random and deterministic behaviors. Therefore, it’s necessary to develop a program of flow simulation for better forecasting the duration of passenger processing and the area occupied. That’s the purpose of our project.


8Casablanca International Airport

9

We distinguish three types of process: departure process, arrival process and transit process.

The transit process consists essentially of stages of the others processes.

Process descriptionProcess description

Departure process Arrival process Transit process


Controlled VariablesUncontrolled Variables

Static Dynamic

Dep

artu

re

proc

ess

Arr

ival

P

roce

ss

Airport facilities : Departure zone

Airport facilities : Departure zone

Airport facilities : Arrival zone

Airport facilities : Arrival zone

Flight departure planning

Flight departure planning

Expected passenger departure

Expected passenger departure

Flight arrival planning

Flight arrival planning

Expected passenger arrival

Expected passenger arrival

Service durationService duration

Routing durationRouting duration

Passenger’s behavior


Service durationService duration

Routing durationRouting duration



Random

phenom

ena

Random

phenom

ena

Resource Planning:

Departure process

Resource Planning:

Departure process

Model ParametersDecision Variables

Resource Planning:

Arrival Process

Resource Planning:

Arrival Process

Data structureData structure

In order to facilitate data collection, we have organized data structure accordingly to the following design:

10

Dep

artu

re

proc

ess

Arr

ival

Pro

cess

Decision Variables

Resource Planning: Police agentsResource Planning: Police agents Resource Planning: Customs

agents

Resource Planning: Customs agents

Number of police agents

working in shift 1


working in shift 1 24h


working in shift 2


working in shift 2


working in shift 3


working in shift 3

Number of Customs

agents working in

shift 1

Number of Customs

agents working in

shift 1

24h

Number of Customs

agents working in

shift 2

Number of Customs

agents working in

shift 2

Number of customs

agents working in

shift 3

Number of customs

agents working in

shift 3

Decision variablesDecision variables

11Casablanca International Airport

Static data : We have collected data related to airport facilities through the diagnosis and the infrastructure documents besides the conversations with operation agents ( number of check in desk, number of customs desk ,….)

Dynamic data : ONDA is used to work with flight planning and expected passenger. These data are available in an Excel file

Random variables: we have collected data by using chronometer and we have modeled the statistical distribution of :

Each duration service , the routine duration of passenger .

Routing duration between each two successive stage in the process.

The arrival time of passenger in check-in zone.

For collecting data related to Model parameters, we have used specific ways for each type of data

A

B

C

BA C

Model parametersModel parameters

12


Dep

artu

re

proc

ess

Arr

ival

Pro

cess

Static Data (example terminal 1)

We have collected data related to airport facilities through the diagnosis and the infrastructure documents besides the conversations with operations agents

41 C

heck

-in

Des

k41

Che

ck-in

D

esk

2 cu

stom

des

k2

cust

om d

esk

At

mos

t, 3

cu

stom

s pe

r de

sk

At

mos

t, 3

cu

stom

s pe

r de

sk

8 Im

mig

ratio

n fo

rmal

ities

des

k8

Imm

igra

tion

form

aliti

es d

esk

At

mos

t, 2

Im

mig

ratio

n fo

rmal

ities

des

k

At

mos

t, 2

Im

mig

ratio

n fo

rmal

ities

des

k

8 in

tern

atio

nal

Boa

rdin

g ro

oms

8 in

tern

atio

nal

Boa

rdin

g ro

oms

1 na

tiona

l B

oard

ing

room

s1

natio

nal

Boa

rdin

g ro

oms

1 co

ntro

l file

r fo

r ea

ch

boar

ding

roo

m

1 co

ntro

l file

r fo

r ea

ch

boar

ding

roo

m

8 Im

mig

ratio

n fo

rmal

ities

des

k8

Imm

igra

tion

form

aliti

es d

esk

At

mos

t, 2

Im

mig

ratio

n fo

rmal

ities

des

k

At

mos

t, 2

Im

mig

ratio

n fo

rmal

ities

des

k

1 cu

stom

des

k1

cust

om d

esk

At

mos

t, 5

cu

stom

s pe

r de

sk

At

mos

t, 5

cu

stom

s pe

r de

sk

Static dataStatic data

13


Dep

artu

re

proc

ess

Arr

ival

Pro

cess

Dynamic Data

ONDA is used to work with flight planning and expected passenger. These data are available in an Excel file

Pla

nnin

g of

de

part

ure

fligh

tP

lann

ing

of

depa

rtur

e fli

ght

The

num

ber

of

expe

cted

pa

ssen

gers

The

num

ber

of

expe

cted

pa

ssen

gers

Pla

nnin

g of

ar

rival

fli

ght

Pla

nnin

g of

ar

rival

fli

ght

The

num

ber

of

expe

cted

pa

ssen

gers

The

num

ber

of

expe

cted

pa

ssen

gers

Dynamic dataDynamic data

14


More than 2 weeks for collecting data by using chronometers.

One day for each stage of the process

The use of three distribution:• Exponential• Gaussian distribution• Triangular distribution.

Select the best the best distribution for each random variable after comparing different results.

In case of difficulty to collect data as some passenger’s behavior, we have used the uniform distribution

Uncontrolled parametersUncontrolled parameters

15


Du

ratio

n c

on

tro

l filt

ers

:D

ura

tion

co

ntr

ol f

ilte

rs:

Ro

utin

g d

ura

tion

to

Co

ntr

ol

filte

r a

nd

acc

ess

bo

ard

ing

ro

om

Ro

utin

g d

ura

tion

to

Co

ntr

ol

filte

r a

nd

acc

ess

bo

ard

ing

ro

om

Dep

artu

re

proc

ess

Arr

ival

Pro

cess

Random variablesA

cce

ss t

o t

he

te

rmin

als

Acc

ess

to

th

e t

erm

ina

ls

Ch

eck

-in D

ura

tion

C

he

ck-in

Du

ratio

n

Inte

rna

tion

al :

E

xpo

ne

ntia

l(2,1

)In

tern

atio

na

l :

Exp

on

en

tial(2

,1)

Tria

g(0

.33

; 0

.5 ;

1.2

5)

Tria

g(0

.33

; 0

.5 ;

1.2

5)

Cu

sto

m c

on

tro

lC

ust

om

co

ntr

ol

Lo

ng

-ha

ll :

Tria

g(0

,14

0,1

80

)L

on

g-h

all

: T

riag

(0,1

40

,18

0)

Me

d-h

all

: T

riag

(0,1

10

,14

0)

Me

d-h

all

: T

riag

(0,1

10

,14

0)

Na

tion

al :

Exp

on

en

tial(1

,3)

Na

tion

al :

Exp

on

en

tial(1

,3)

Co

ntr

ol r

ate

50

%C

on

tro

l ra

te 5

0%

Du

ratio

n c

on

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lU

nifo

rm (

0.5

,0.8

)D

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tion

co

ntr

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Un

iform

( 0

.5,0

.8)

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utin

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tion

to

Cu

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ou

ting

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ratio

n t

o C

ust

om

Imm

igra

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Du

ratio

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on

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l D

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tion

: E

xpo

ne

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):

Exp

on

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tial(1

,3)

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to

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ratio

nN

orm

al(0

,3,0

05

)

Ro

utin

g d

ura

tion

to

Im

mig

ratio

nN

orm

al(0

,3,0

05

)

Firs

t p

ass

en

ge

r tr

an

sfe

rF

irst

pa

sse

ng

er

tra

nsf

er

Fa

r la

nd

ing

: E

xp(1

0)

Fa

r la

nd

ing

: E

xp(1

0)

“Ch

arli

e”

La

nd

ing

:E

xp(7

)“C

ha

rlie

” L

an

din

g :

Exp

(7)

Firs

t l l

ug

ga

ge

t

ran

sfe

rF

irst

l lu

gg

ag

e

tra

nsf

er

Fa

r la

nd

ing

: E

xp(1

3)

Fa

r la

nd

ing

: E

xp(1

3)

“Ch

arli

e”

La

nd

ing

:E

xp(9

)“C

ha

rlie

” L

an

din

g :

Exp

(9)

Imm

igra

tion

co

ntr

ol

Du

ratio

nIm

mig

ratio

n c

on

tro

l D

ura

tion

: E

xpo

ne

ntia

l(1,3

):

Exp

on

en

tial(1

,3)

Lu

gg

ag

e d

eliv

ery

du

ratio

nL

ug

ga

ge

de

live

ry d

ura

tion

Un

iform

(0.3

, 0

.5)

Un

iform

(0.3

, 0

.5)

Cu

sto

m c

on

tro

lC

ust

om

co

ntr

ol

Co

ntr

ol r

ate

10

%C

on

tro

l ra

te 1

0%

Du

ratio

n c

on

tro

lT

riag

(0

.83

, 1

.6,

4)

Du

ratio

n c

on

tro

lT

riag

(0

.83

, 1

.6,

4)

Ro

utin

g d

ura

tion

to

lug

ga

ge

d

eliv

ery

No

rma

l(0,3

,00

5)

Ro

utin

g d

ura

tion

to

lug

ga

ge

d

eliv

ery

No

rma

l(0,3

,00

5)

Uncontrolled parametersUncontrolled parameters

16


The rule adopted is the first in first out ( FIFO)

Departure Process:

One queue for each check-in desk One queue for each customs desk One queue for each immigration formality desk One queue for each control filter

Arrival process:

One queue for each immigration formality desk One queue for customs control with capacity is

limited to 4 people. The probability to be controlled by custom agent is 10%

Queue managementCheck-in desk management

The number of the available desk for each flight:

Big carrier : 4 desk Medium carrier : 2 desk Small carrier : 1 desk

The opening time of check-in :

Long-haul flight: 4 hours before flight time

National flight : 2hours before flight time

Others : 3 hours before flight time

The closing time of check-in : 40 min before flight time

The number of the available desk for each flight:

Big carrier : 4 desk Medium carrier : 2 desk Small carrier : 1 desk

The opening time of check-in :

Long-haul flight: 4 hours before flight time

National flight : 2hours before flight time

Others : 3 hours before flight time

The closing time of check-in : 40 min before flight time

Boarding room management

Management rulesManagement rules

17

ProcessProcess Collecting Data & statistical modelingCollecting Data & statistical modeling Management rulesManagement rules

Simulation model consists of the three parts : (1) Process (2) Data ( 3) Management rules

After constructing the simulation model , we have developed an IT program for implementing the flow simulation model

Modeling : SummaryModeling : Summary

18

We have studied the possibility to use a programming language or simulation software for better implementing the simulation model

IT Program designingIT Program designing

Programming language Simulation software

Development

C++ Java

VBA ……

After analyzing the context of our project and taking into account many factors related to the development environment, we have decided to develop our program with a simulation software “Awesim”.

19


IT program is divided on three parts : (1) Inputs (2) Simulator (3) Output

IT Program designingIT Program designing

InputInput

Flight Planning

Resource allocation

ProgramProgram

Expectation of waiting time and people waiting

Network simulation

OutputOutput

Visual simulation

1 2 3

20

IT Program: InputIT Program: Input

InputInput

Flight Planning

Resource allocation

1

Flight planning: flight time, departure or arrival, flight type, aircraft type, parking , boarding room, number of passenger expected

Resource planning: number of police, customs , luggage handlers planed to work during each shift (the program give us the possibility to substitute shift by hour )

21


IT Program :NetworkIT Program :Network

ProgramProgram

Network simulation

2

We have modeled the departure and arrival process in network simulation with “Awesim” software. We have included in the network all static data and uncontrolled variable of our simulation model.

22

1. Graphic result : represent the expectation of different indicators. For example:

the waiting time in each stage of the process, the number of people waiting in each queue, the total processing time.

IT Program: OutputIT Program: Output

Expectation of waiting time and people waiting

OutputOutput

Visual simulation

3

Waiting time for Immigration formality

stage ( police)

Waiting time for Immigration formality

stage ( police) people waiting in

police queue

people waiting in police queue Total Processing

time

Total Processing time

We have defined in simulation program two kind of the output:

2. Visual simulation: represent a dynamic simulation in order to better analyze the flow and detect easily the enhancement could be done in order to improve the processing quality.

23


Test & ResultTest & Result

After developing the IT program, we have backtested the results given by the model.

Because of constraint of project duration, we have focused on testing one indicator for evaluation the quality of the model: The number of people waiting in each queue.

By using the IT program, we have forecasted the number of people will be waiting in different queues (both departure and arrival process) during different period of the day. On the other hand, we have collected data related the number of people waiting each queue during different period of the day. These information have allowed us to compare the reality with the simulation model

For departure process , we have found that quality of the model is almost 81% for departure process, and more than 85% for arrival process.

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ConclusionConclusion

As we have said at fist, the simulation is the first step of the process of resource optimization. The use of simulation results through the sensitive analysis help easily to find a good solution of resource planning problem.

It seems interesting to develop an advanced approach in order to better use the simulation results . This approach consists to combination of an heuristic method like genetic algorithm and simulation results in order to find an optimal ( local) solution for resource optimization problem.

25

Generation of initial

population

Executing the

simulation for each

mem

ber of the Initial population

Executing the

crossover , mutation

operator

Endl

For improving the quality of simulations results, we can introduce other random factor by investing more time on analyzing the disruptions and modeling this random behavior.

Executing the

simulation for each

mem

ber of the population

If Local optima

Combination of simulation results and genetic algorithm

Combination of simulation results and genetic algorithm

Thanks you for your attention

Casablanca International Airport 26

Réalisé par Nabil SI HAMMOU

للمطارات الوطني المكتبNational Office of Airports

National Institute of Statistics and Applied Economics



Nabil Si HammouOperations Research Engineer

[email protected]

Jury:

M. Ghali Maan ABDELLAOUI (INSEA)

M. Adelhamid BENTAIEB (ONDA)

ONDAINSEA

Documents

Flow simulation - Casablanca Interrnational Airport