1

AVIATION FUEL DISTRIBUTION PROCESS

ANALYSIS USING MODELING AND

SIMULATION

Miguel Iturrate

Unamente.com

INTRODUCTION

Beginning the 80´s, many organizations

worldwide started efforts leading to increase their

competitive advantages. Continuous improvement

techniques and quality management systems were

born and in the 90´s begun the very first approaches

to the formal establishment of reengineering and

process redesign methods.

Currently, these methods are supported by

computer assisted modeling and simulation

techniques allowing industrial and business process to

be redesigned or improved by studying the many as

possible scenarios without making actual changes to

physical processes, given that the system is simulated

through computational methods that include the

appropriate detail level required for every situation.

The practical application of this knowledge

allows companies to increase the degree of control in

decision making, usually related with the return over

investment of the overall supply chain, considering

global and local scope. Generally speaking, involved

variables considered in simulations are time, costs,

flexibility and quality.

Within this context, this paper presents the

interrelation between a aviation fuel storage facility

and one of its most important clients, the international

airport of an important city located in West Africa.

Given the facilities size and quantities involved, fleet

configuration and optimal quantities estimation are

some of the critical factors studied in this paper.

OVERVIEW

A simulation for the distribution of aviation

fuel from the storage facility to the airport tanks is

analyzed. The simulation model developed includes

the variation of the critical parameters that governs

the real system process. It is of interest to understand

the options that optimize the existing tank trucks fleet,

and also the effect of commissioning new charge and

discharge points to allow improving the overall

process.

To obtain the adequate process insight that

facilitates understand the scope and objectives, it was

necessary to design a simulation model of the process

that considers as parameters number of terminals in

each facility, number of tank trucks, and number and

availability of quality control inspections routines.

The mix of alternatives is shown in the table

below:

#

charge

points

#

discharge

points

#

tank

trucks

#

products

#

destination

points

1 - 4 1 to 4 1 to 15 1 1

Table 1. Mix of technological alternatives.

This way, the variations in the distribution

capacity as function of mix of charge/discharge points

configuration, and number of tank trucks is analyzed.

This communication summarizes the aviation

fuel distribution process model considered. Then, a

simulation of the process is built, input data sets and

hypotheses defined, actual model construction, and

validation and analysis of the results of the simulation

model performed.

As a result, it is expected that this simulation

model will be of help in the decision making for

including of new tank trucks or new charge/discharge

points in each facility.

THE JET A1 DISTRIBUTION PROCESS

The analyzed process is uni-modal and single

product. The daily transported volumes vary among

800 m3 to 1,500 m3, and the installed storage

capacity at the airport is of 1,500 m3. The current

supply contract include high penalties to the

aeronautical authorities if the airport tanks get dry or a

stock out occurs, because important alterations to the

flight schedule can cause to several airlines that

operate in the region.

A flow diagram of the process is presented in

Figure 1. Process begins when the tank truck is loaded

in the fuels storage facility located at 9 km from the

airport, in the central sector of the city. A bottom-

loading mode is utilized for loading 30 m3 of aviation

fuel in absence of vapor recovery systems.

Once concluded, and after a mandatory

period of resting time of 10 minutes, it is executed a

quality control test consisting in a visual inspection,

density and temperature determination, and detection

of water presence in a fuel sample. If the sample is

approved, the operator registers the transfer

documentation between facilities and after an exit

check control, the fleet goes out the facility heading to

the airport.

At this stage, the model considered the

vehicular congestion in function of the hour of the

day.

2

Figure 1.Diagram of the distribution process of Jet A1.

Once finalized the route to the airport, the

tank truck arrives and enters the airport facility, and

parks for another operation of quality control, similar

to the formerly described.

After the quality control inspection, the

operator removes the discharge seal and the tank truck

starts the discharge via bottom unloading. Afterwards,

the fleet leaves the airport and returns to the storage

facility.

THE SIMULATION MODEL

Several assumptions were validated through

various interviews to the direct operators of the

process and on site/on route direct observations. The

considered assumptions were:

An operation of 24 hours, in two shifts.

The graphs of speeds and times of transfer are

presented in the Figure 2 (case of a loaded truck

heading to the airport).

The operators eat lunch and they have dinner,

with a delay of approximately 60 minutes every

time.

It is not always practiced a quality control,

situation detected at the airport.

It is not included in the simulation the rain

possibility, a fact that impedes the discharge for

not having the installation an appropriate

protection for that case.

Figure 2. Times and speeds of circulation of a loaded

truck in Luanda.

To correctly modeling the described process,

direct measures were registered from each truck route,

and historical data for residence time of the trucks in

the facility were also considered.

For loading and discharge times, a logNormal

distribution was used with averages of 17 and 32,

and standard deviations of 2 and 3 minutes,

respectively.

For quality control times, a uniform distribution

was utilized, with maximums and minimum of 30

and 0 minutes respectively.

Figure 3 presents the used simulation model.

Figure 3. Extendtm Simulation model for the distribution

process of Jet A1.

The simulation was executed utilizing

Extendtm

software v5, created by Imagine That Inc.

(www.imaginethatinc.com). The block libraries

contain more than 150 blocks for a logical

interconnection for the representation of the problem

and the simulation of the exit variables for its study.

In this case, each simulation was executed 100 times,

with a medium time for execution of 2 sec.

RESULTS

In order to validate the structure of the model

simulation, several meetings with the distribution

process responsible on each storage facility took

place. Some of the hypotheses were validated through

direct field measures. Other data showed the correct

assumptions considered in the model that evaluated

systematically and timely the validity of data that

initially were considered as reference, like residence

times of the fleet at the facility and the inclusion of

several modeling blocks types in the simulation.

The simulation model resulted in the following

groups of graphs, according to the combinations

presented in Table 1.

-

10

20

30

40

50

60

- 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hora do dia (hr)

Velocidade / Tempo IBV5-Aeropuerto IDA

km/hr

min

3V 1 2

N° Pedidos/dia

count

ev ent

C

# u

D

Recep pedidos

change

#use

u

use #

change

u

Flota

D

T U

CCalidad1

C

# u

D

Contr Salida

FAGV

Xspeed Y

ASR

D

T U

Control Calidad

D

T U

Pedidos recepcionados

C

# u

D

Contr Entrada

#

demand

Count

#r

C

demand

a

b

c

demand

a b cabc

Chof er

Chof er

Count

#r

C# camiones a ruta

D

T U

Pedidos atendidos

Tiempo CCalidad

F

L W

Count

#r

C

#

demand

Tiempo en ruta ida

D

T U#

demand

D

T U#

demand

D

T U#

demand

F

L W

D

T U#

demand

open 1

open 3

open 2

open 4

F

L W

a CCalidad1

F

L W

a Salida

F

L W

a CCalidad2

F

L W

a Descarga

D

T U

MD

sensor

Tiempo Llenado

Tiempo Llenado

Tiempo Llenado

Tiempo Llenado

t y

Tiempo CCalidad

C

# u

D

Contr EntradaF

L W

a Ruta

Tiempo en ruta v uelta1 2 3

Rand

Tiempo Control

F

Xspeed Y

Tiempo descarga

#

demand

D

T U#

demand

open 5

open 7

open 6

open 8

Tiempo descarga

Tiempo descarga

Tiempo descarga

t y

1 2 3

Rand

Tiempo Control

1 2 3

Rand

Tiempo Control

D

T U

T Reposo

D

T U

D

T U

Tiempo Reposo

Tiempo de Ciclo

Orders Reception

Fleet Configurati

on

Invoicing

Charge

Quality Control

Exit Control

Route Time

Discharge Return Time

Entrance Control

3

1. Number of delivered orders vs number of tank

trucks in operation for the actual configuration of

load/discharge points.

2. Number of delivered orders vs number of tank

trucks in operation for alternative configurations

of load/discharge points.

3. Number of delivered orders vs alternative

configurations of tank trucks number in operation

and number of load/discharge points.

The previous combinations were summarized

in just one graph called Deliveries / Number of tank

trucks / Number of load-discharge points. This graph

is represented in Figure 4.

Figure 4. Number delivered orders for fleet size and

load/discharge configuration.

ANALYSYS

Figure 4 summarizes the results of the

performed simulation. The abscissas represent

number of available tank trucks. The ordinates

represent number of delivered orders for several

combinations of loading and discharge points. Just

one graph presents the interaction of these two groups

of variables. For the actual load/discharge points

configuration (2/3), and the actual tank trucks number

in operation (5), the simulation model predicts that the

current configuration of the system should deliver 40

orders approximately per day, meaning close or more

than 1.200 m3 per day of aviation fuel.

The results confirm some restrictions

detected in field in the operation of the process. The

simulation model, as a management tool, can help to

determine shifts changes and their impact in the

distribution capacity. It is also possible to appreciate

the effects and influence of each variable in the

others, and the interaction of other functional areas

(for instance invoicing) and its impact in the global

result of the process.

To commission more discharge points in the

airport, and to include more tank trucks, will produce

an effect in the daily delivered volume only if the

number of quality controls is proportional to it and its

sequence is accomplished in parallel. In other words,

and as Figure 4 depicts, an increase in these variables

is constrained by the existence of a single quality

control point for multiple operations, without effects

in the delivered volume.

Bottom line final recommendation, it is

demonstrated that it is not necessary to invest in more

loading and discharging points, including both city

storage facility and airport’s. On the contrary, it is

necessary to refine procedures to consider multi-

quality control inspections in order to accelerate the

process with parallel jobs instead of sequential.

REFERENCES

Real Data from DIA Management Control System.

Study of times IBV5, January-March 2001.

Real Data from SPMP Stage I. Study of times DTR,

January-March 2001.

Real Data Study of times through fleet route

following, April 2002.

Diamond, R., Modeling with Extend, Imagine That

Inc, 1999.

Law, A., Kelton, D., Simulation Modeling and

Analysis, McGraw-Hill, 2000.

ABOUT THE AUTHOR

Miguel Iturrate is the founder and managing director

of unamente.com, a company which applies

computer-based process simulation modeling in order

to maximize business revenue (www.unamente.com).

With background in the natural resources industry

specifically in Oil & Gas, and Mining, currently

specializes in Supply Chain Management, MES, ERP

systems and Business Intelligence. Miguel is

Mechanical Civil Engineer and Master of Industrial

Engineering from Universidad de Santiago de Chile.

For more information please contact

info@unamente.com.

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Pedidos entregues vs N° de camiões