Aviation Fuel Distribution Simulation
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Transcript of Aviation Fuel Distribution Simulation
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
1 boca de descarga
2 a 4 bocas de descarga
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Pedidos entregues vs N° de camiões