Aircraft Fleet Planning

T.C.
M 펴용 MARA UNIVERSI 멧￥
INSTITUTE FOR GRADUATE STUDIES IN
PURE AND'APPLIED SCIENCES
AIRCRAFT FLEET PLANNING
BY
ORHAN SIVRIKAYA
THESIS ADVISOR
ASSOC.PROF.DR.ZAFER GUL
SUBMITTED TO THE INSTITUTE FOR GRADUATE STUDIES IN
SCIENCE AND ENGINEERING IN PARTIAL FULFILLMENT OF
THE REQUIREMENTS FOR THE DEGREE OF :
MASTER OF SCIENCE
IN
INDUSTRIAL ENGINEERING
ISTANBUL 1997

~
k &
않
、 이
‘
Marmara Ûniversitesl
Kltlphane ve Dokllmantasyon Dall'e Ba ￥ kanh~l
”
”
”
”
”
”
”
”
”
”
”
”
”
”
매
매
배
매
삐
매
”
”
n
n
”
”
n
n
”
”
”
n
”
삐
삐
-
빼
빼
내
l
;
”
”
”
”
메
6
삐
삐
삐
빼

‘ /
T.C.
MARMARA UNIVERSITY
INSTITUTE FOR GRADUATE STUDIES IN
PURE AND APPLIED SCIENCES
AIRCRAFT FLEET PLANNING
BY
ORHAN SIVRIKAYA
.A /J/ !,/J
THESIS ADVISOR ASSOC.PROF.DR.ZAFER GUL !T:. ι Y...I. .....
MEMBER PROF.DR.ERKAN TÜRE ..!잦 k 센. 및벚.
MEMBER PROF .DR.AKIF EYLER 밝 '1 45.짧 γ
SUBMITTED TO THE INSTITUTE FOR GRADUATE STUDIES IN
SCIENCE AND ENGINEERING IN PARTIAL FULFILLMENT OF
THE REQUIREMENTS FOR THE DEGREE OF :
MASTER OF SCIENCE
IN
INDUSTRIAL ENGINEERING
ISTANBUL 1997

III
ACKNOWLEDG MENT
1am indebted to Professor Dr. Erkan Türe for his valuable guidance,
help and support throughout the course ofthe study.
1would like to express my sincere gratitude to Ekrem Duman for
his helpful suggestions and comments.
1 would also like to thank the THY staff who helped me to gather
necessary data and resources
Orhan Sivrikaya

1V
TABLE OF CONTENTS
Page
ACKNOWLEDGMENT.......................................................................111
TABLE OF CONTENTS....................................................................1V,V
L1ST OF TABLES................................................................................V1
ABSTRACT.........................................................................................V11
OZET..................................................................................................V111
1. 1NTRODUCT10N
1.1 Scope............................................................................................9
1.2 Characteristics ofAir1ine Business...............................................9
1.3 The 1mportance of Capacity Planning.………...…….............………11
1.4 Forecasting in the Air1ine Businεss ............................................13
1.4.1 Use offorecast in the air1ines.............................................1 3
1.4.2 Types of foreca 안 ............................................................... 14
1.4.3 Time series forecasting.......................................................1 5
1.4.4 Evaluation of forecasting accuracy.....................................17
1.5 Aim of the Study........................................................................20
2. WORLD A1R TRAVEL..................................................................21
3. FLEET PLANN1NG
3.1 Meaning ofFleet Planning.........................................................26
3.2 Approaches for Fleet Planning....................................................29
3.3 The Model..................................................................................31
3.3.1 Related Definitions..................................…….....................31
3.3.2 Problem.............................................................……...........34
3.3.3 A General Linear Model for Fleet Planning.......................36
4. CASE STUDY FOR THY.................................................................40

V
Page
APPENDIX A .......................................................................................50
BIBLIOGRAPHY...................................................................................51
REFERENCES NOT CITED..............................................................…
53
GLOSSARY............................................................................................55

VI
LIST OF TABLES
Page
Table 1.1 Bui1ding Forecasting Mode1.................................................18
Table 1.2 Difficulties encountered by air1ines in
forecasting process................................................................................19
Table 1.3 Methods used by airlines to forecast traffic...........................19
Table 2.1 The growth of air transport....................................................22
Table 2.2 Air traffic...............................................................................22
Table 2.3 World RPMs...........................................................................23
Table 3.1 Organization of f1eet planning................................................27
Table 3.2 Direct operating cost...............................................................33
Table 4.1 Number of scheduled block hours.........................................42
Table 4.2 Demand foreca 앙 .............•.•...•.... " ••..........•••.•..•......•.....••...•••..42
Table 4.3 THY f1eet composition...........................................................42
Table 4.4 Costs per block hour...............................................................43
Tablε 4.5 Answer report.........................................................................46

ι 키
VII
ABSTRACT
The aim of this project is to make optimal decisions on the number of
aircraft and aircraft fleet composition for given passenger demand
forecast and time period.
Linear programming is used with associated parameters estimated
from cost and demand forecasting analysis. The number of passengers to
be carried wiU be forecasted to determine the required number of aircraft
so that total costs are minimized.
Our model should generate a basic prototype fleet composition in the
long term so that the aircraft in the fleet are best utilized through the
flight network resulting in optimum number of aircraft for defined
aircraft types and minimum aircraft costs.

r ‘
vnr
OZET
Bu proje gelecege yonelik yolcu trafigini tahmin ederek ihtiyac olacak
ucak sayisini ve tanimli ucak tiplerini en iyi seki1de tesbit etme amacini
tasimaktadir.
Maliyet gider1eri ve yolcu tahminleri dikkate alinarak karar degisken
parametreleri tanimlanacak ve lineer program modeli olusturulacaktir. Bu
amacla yolcu tahmin metodlari kullani1acaktir.
En uretken seki1de kullani1acak ucak filosunu olusturacak, uzun donem
bazinda prototip ucak filosunun ucak saylsl ve tiplerine gore teskil
l
e어 d i1 me 엉 sini saglayacak sonuclar elde edi1ecektir.

1. INTRODUCTION
1.1 Scope
Periods of rapid change in the airline industry and its ref1ections to
airline operators have normal1y coincided with vigorous replacement of
the airline in-service f1eet. The more dramatic the change, the easier the
airline decision with respect to replacement. For example, periods of
rapid traffic growth require replacement with larger airplanes. Large
improvements in operating economics or passenger appeal features
through new technology simplify replacement decisions. Highly
leveraged external factors, such as a fuel price run up, can create
economic forces which benefit newer technology airplanes and make a
strong case for replacement.
1.2 Characteristics ofAirline Business
Economists usually describe the certificated airline industry as c10sely
approximating an oligopolistic market structure. An oligopoly is an
industry composed of a few firms producing either simi1ar or
differentiated products. Oligopolistic industries are typically
characterized by high barriers to entry. These usual1y take the form of
substantial capital requirements, the need for the technical know-how,
patent rights, and so forth[1].
In addition to few sellers, high obstac1es to entry, and simi1ar products,
oligopolistic industries tend to have several other characteristics:
l-Substantial economies of scale
2- Growth through merger
3- Mutual dependence
4- Price rigidity and none-price competition
9
) •
'. .....'、 1f.C 값녕}
I rJ ,.. •

With the general characteristics of oligopolies as a background we now
can analyze how the airline industry compares and then take a look at
several unique characteristics.
• The industry certainly meets the first criteria, by the limited quantity of
firms.
• There is no question that the stakes are high if one is considering
starting up an air carrier. The capital requirements alone will serve as
barrier to many an aspiring airline entrepreneur.
• Like all oligopolists, airlines must achieve a large volume of output in
order to lower the cost per unit of output( A seat departure ) and to
achieve economies of scale in principle of labor specialization.
• Another clear characteristic of oligopolists in general, and airlines in
particular, is growth through merger. It is a m 며 or factor in explaining
the small number of firms.
• Regardless of the means by which an oligopoly evolves, it is clear that
rivalry among a small number of firms interjects a new and
complicating characteristic : mutual dependence.
• Firms in oligopolistic industries find it much more comfortable to
maintain constant prices rather than increasing, because of mutual
dependence and fear of a price war.
• Unlike other oligopolistic industries, various government units play
major roles in financing the growth and development of a national
carrler.
• Again, unlike other oligopolistic industriεs airlinε industry has a high
technological turnover and high labor and fuel expenses. The airline
industry is very sensitive to εconomic fluctuations. The effects of a
recession on air travel are obvious. When the economy moves into a
recessionary period, the carriers find themselves with substantial
excess capacity.
n
u
---A

After examining several characteristics of an air1ine firm now we can
introduce the term load factor, an important indicator for carriers which is
one of the most vital statistics in the air1ine business. Given the
multimillion-dollar investment represented by the modern jet liner,
airlines are naturally concerned with equipment utilisation. One measure
ofutilization is the revenue passenger mi1es realized.
Load factor has a critical impact on the cost and quality of air
transportation services offered. A considerable amount of an airline’s
costs are directly related to the operation of aircraft and are almost
indepεndent of the number of passengers on thε aircraft. Therefore, a
high load factor will allow the allocation of these costs over a large
number of seats, resulting in lower costs per seat which may enable for
lower fares when the market necessitates. Airline load factor during any
year vaη from month to month depending upon season. Dai1y and evεn
hour1y loads fluctuate more[2].
1.3. The Importance of Capacity Planning
There is considerable amount of money involved in capacity planning
which means fleet planning. For example, Airbus 340 is 80 million USD
Dollars in price this equals to just a little less than 1 % of the AIR
FRANCE annual turnover. Capacity planning is sensitive and directly
related to the economic performance and survival of the airline in means
of consequences on the staff: i.e. training etc... and fitness to the market
expanslOn.
11

As we have noticed the realities of public transportation, whether by
bus, train, or air, result in an imbalance between the number of seats, or
capacity, avai1able and the current demand for travel by the public. The
two simple do not match precisely the same time, the same place, and the
same rate.
Airlines cannot adjust capacity to match demand, because capacity can
only be added or taken away in total plane loads. Within limits, the total
number of flight frequencies flown on a given day can be varied, and this
can be done where feasible. On business routes, it is common to reduce
잠 equencies on Saturdays. However, there are number of factors limiting
this effort to adjust daily seats to daily traffic.
First of all, many routes have too little frequency operated by each
carrier to permit much leeway for canceling trips on a particular day
without damaging the overall pattern. Second, the day-of-week pattern of
demand does not vary in a precise, predictable, or fully consistent
manner. Finally, the schedule pattern on any one route is too interrelated
with those of other routes ( and with operational constraints of various
kinds ) to permit an erratically scheduled operation from day to day. As a
result of these various factors, the supply of seats is necessarily much
more uniform than is the demand for them. The day-to-day and seasonal
variation in demand is considerable. It is plainly uneconomic to have
sufficient aircrafts in the company fleet to meet peak demand, because a
number of aircrafts would then be idle for most days of the year.
Similarly, it would be uneconomic to insist that each aircraft in the
company fleet be fully occupied throughout the year, as this would mean
excessive hiring costs at peak times. The fleet size giving the least cost
lies somewhere between these extremes [3].
12

1.4 Forecasting in the Airline Business
1.4.1 Use offorecast in the airlines
An important part of any planning process in business is a forecast of
future demands for the company’s products. This is particular1y
important in the travel industry which has the u1timate in a perishable
product: a seat not sold for a flight is lost-it can not be sold after the date
has occurred.
The purpose of forecasting is to make use of the best avai1able present
information to guide future activities toward organizational goals. Most
successful airlines anticipate the future demand for their avai1able seats
and translate that information into. relevant factors inputs required to
satisfy expεcted demand. Many environmental factors influence the
demand for an airline’s seats available.
Some major environmental factors are:
• GNP, inflation, exchange rate, disposable income, export/import, life
style
• Regulation framework, technological development, ATC and slot
constraint
• Market conditions
• Geographical area of interest
• Competitors actions
• Governmentallegislative actions.
13

A forecast is the link between the external, uncontrollable environment
and the internal, controllable affairs of an airline. All statistical
forecasting techniques assume to some extent that forces that existed in
the past will persist in the future. It should be taken into consideration
that poor forecasting can lead to disaster. For this reason, results driven
from forecasting shall be evaluatedby experts before implemεntation and
every new data shall be used and the model will be updated accordingly.
A short term forecast is generally more accurate than a long term one
and more unstable the demand,the more critical is the forecast accuracy
for airline operations, because of :
• Unforeseen events : strikes,wars, energy crises, etc.
• Uncertainty concerning the economic forecast.
• Distortion caused by seasonality, special events ( such as the Olympics
), new route awards or new carriers, etc.
• Lack of information allowing desegregation of the forecast into
component parts
• New service area definitions
• The general use of a traffic forecasting model using GNP or GDP and
yield as the explanatory variables is of limited value in some world
areas.
1.4.2 Types of forecast
There are three basic types of forecasting:~conometric ， iudgmentaL and
time serieâ which projects past data patterns. Economic forecasting is the
process of identifying economic variablεs that influence the number of
travelers who will arrive at a location during a period. In particular the
forecaster is looking for variables that are current1y being measured and
that occurred before the period to be forecast. Such variables are called
leading indicators, and banks and governments use them to forecast gross
national product.
14

The second typε of forecasting, judgmental forecasting, bases the
forecast on the managers' expectations or “feel" for the future.
The third approach applies statistical procedures to past data to
determine patterns in the data. These patterns are then projected into the
future. This technique is called time series forecasting. A time series is
the collection ofpast monthly figures.
Extensive studies of forecasting product sales have been made to
determine the best technique to use. In most cases, the conclusion has
been that judgmental forecasting is less accurate than econometric
forecasting, which is less accurate than time series forecasting
(Markidakis and Hiblon 1979).This is not to say that time series models
should be used exclusively. There are many situations where the
forecasting problem is best solved by a judgmental or an econometric
model.
1.4.3 Time series forecasting
The phi1osophical basis of time series forecasting is that the measured
values constituting the series are generated by an under1ying process that
remains stationaη over time(Markidakis and Wheelwright 1978).As a
result, past data patterns can be projected to produce a forecast. All time
series models use this notion. The primaη differences among methods lie
in the manner in which past data are statistically partitioned, aggregated,
and weighted prior to the projecting, as well as in the method of
projecting. Obviously, time series forecasting depends heavi1y on
gathering past data which accurately reflect the under1ying process that
generates the data.
The data pattern can be broken down into several components. Most
researchers refer to four components in a time series: trend, seasonality,
cycles, and noise or residuals. The greater the variability in each of these
components the greater the difficulty
15

in forecasting. Time series models partition the data to expose the
individual component patterns. A dual problem occurs with data used in
forecasting. First, some of the data points are not true ref1ections of the
underlying process. These data points are outliers, or atypical periods
during which some external force resulted in values that would not
normally occur. Second, the underlying processes may be changing over
time. The time series is not stable, and the past measurements are only
moderately useful for projecting the future.
Handling Outliers
Outliers can be viewed as a fifth component ofthe time series. Outliers
are sometimes caused by aηpical events that have an effect on only a few
ofthe data points. For example, a planned strike by air controllers in July
may inf1uence the number of tourists for the months of June, July, an
August: June and August may be larger than normal and July may be
smaller than normal.
Handling outliers is a difficult process in any type of data analysis, but
in forecasting it is critical function since the past data, including the
outliers, are the basis for forecasting. The dilemma is two-fold. If thε
outliers is either a measurement error or a nonrepresentative part of the
underlying process, its inclusion distorts the model’s perception of past
data and the estimation of the underlying process. To ignore outliers
reduces the amount of data available and create incomplete data sets that
may also distort the underlying process. The only other alternative is to
replace the outlier~ with a value that is no longer defined as an outliers.
To do so constitutes data manipulation, which is the procedure that most
time series models employ to obtain increased accuracy. The data
manipulation replacεs the actual data with a value produced by the
forecast. If the forecast is accurate, then the best estimate of what sales
would have been in the absence of the aηpical event is the forecast of
sales.
16

s
;3
9
’
9
:3a
ι
￥
The Shifting Under1ying Process
McKeown and Lorek (1978) have shown that when the under1ying
process is changing, a recursive forecasting procedure of redetermining
the model on a year1y basis will substantially increase forecasting
accuracy. The procedure is not to rely on the initial model identification
but to “ start over' ’ or to remodel at periodic intervals. The forecaster
suspects the process generating the time series is changing, so a recursive
forecasting approach is used.
The phi1osophy of recursive forεcasting is that the more recent data, the
better the forecasting. Forecasting sales next year can best be done using
this year’s sales patterns rather than sales patterns that occurred several
years ago.
1.4.4 Evaluation of forecasting accuracy
Mean absolute percentage error (MAPE) is used to measure the
accuracy of the forecasts. The MAPE is used to measure forecast
accuracy in all cases and is calculated as;
MAPE = 11m L tI(Et+1 - At-l )/ Et+11100 %
Where m is the number of forecasts,Et is the forecast for time t and At
is the actual value for time t .
17

Table 1.1
BUILDING A FORECASTING MODEL
써때
짜뼈
뼈
鐵
R
““
nν
.,,
A
Qu
πnu
야
따
ι1ι
*L
nm
핑
mmt
m
빼
熾
.‘,
i
--
•
Model development
specification
data collection
calibration
evaluation
‘ I Calibrated Model
Traffic forecast
Evaluation of results
Final forecast
18
Data
•- I quantification
reliability
consistency
size ofbase
projected value
-
ssumptions on
ariables
rojected values
f variables
+

Table 1.2
Difficulties encountered by airlines in forecasting process
웹 Lack of data
口 Lack oftool
II1II Lack of
피 Unprediction
0
Table 1.3
Methods used by airlines to forecast traffic
口Pur ely
35t| j 띠 gem 때띠
• •? 、 (
3o lij 」 IliI E 빼마
Er 、 model
25 iI ，， ~H ' : " : : ; " ' : " "• 「二
r 口 Survev method
20 ’ V
3 힘텀뼈 g :號따11Extrapolation
of past
19

1.5 Aim ofthe Study
Fleet planning is a permanent and continuous process. A fleet plan
starts from zero only for a ‘’start-up" carrier. For an existing carrier, it
starts from a current reality with all its rele 、 rant constraints, risks and
dilemmas to changes. A fleet plan has a time frame over a long period ( 5
to 15 years depending on thε airline, its environment, ...).1t must be
reviewed and modified each year, by introducing all the new events, in
conformity with the evolution of the strategic objectives and adding a
time span sufficient to cover these new events. It is normally assumed
that the first year of the fleet plan is adopted as operational plan as
recycling process is continuous.
The aim ofthis study can be summarized as follows:
• to analyze the available fleet size and composition
• to develop an approach for f1eet size determination
• to define a ‘’credible" future to the airline
• to propose solutions of minimizing airline’s risks for each of the
scenarios which are studied
• to be fundamentally f1exible due to the extreme changes in the airline
and air transport environment
• to be able to give reliable data for the allocations of resources within
the whole airline and finally
• to give an exεmplifying case study for THY
In this respect, this study is aimed firstly to introduce some passenger
forecasting methods which will enable an airline to make sound analysis
and secondly, a model approach to be used in f1eet size and composition
determination.
20

2. WORLD AIR TRAVEL
Wor1d Air Travel Market
The driving force behind replacement is mostly the expanding air
travel market. The wor1d air travel market now re f1 εcts more favorable
conditions and is entering a fifth stage of development since the advent
ofjet airplanes[4].
Expectations:
• Consistent moderate economic progress
• Wor1d εconomic rebound led by the U.S.
Stable interest rates at moderate levels
• Lower inf1ation
• Increased employment
• Improved wor1d trade
• Beneficial trends in operating costs resu1ting in substantial declines in
fares and yields
• Declining near-term fuel prices
• Controlled labor costs
• Greater productivity and efficiency
• Continued government support to promote tourism
• Less regulation
Thεse favorable inf1uences on travel market income and costs, plus
continued quality of service improvements, should resu1t in moderate
wor1d air travel growth for the remainder ofthe decade.
21

Table 2.1
The growth of air transport
RPK(OOO billions) vs. years
1600
1400
1200
1000
800
600
400
200
0
헐 홉 훌 훌 룹 훌 톨
As it is shown in the table 11. a there has existed a very rapid growth of
air transport since the beginning of last half of the century. Where RPK
stands for Revenue Passenger Kilometers.
Table 2.2
Passengεr air traffic regional growth: international schεduled RPK’s
Average annual growth %
1976-80 1981-85 1986-90
Europe +8.2 +3.5 +7.6
Africa +13.5 +4.7 +2.9
Asia and Pacific +17.0 +7.8 +1 1.1
Latin America and
Caribbean +14.6 +2.7 +5.7
Middle East +24.7 +10.1 +4.5
North America +1 1.0 +5.6 +7.5
Source: 1CAO Civil Aviation Statistics ofthe Wor1d
22

Table 2.3
Wor1d RPMs Versus Wor1d Economic Growth
15
10
5
0
-5 ................
룹 룹 혈 혈 훌
-용-% change
inRPMs
-톨-% change
inGDP
Within the wor1d economic community today, a growing
interrelationship is being established. An impact in one countη now
more than ever affects other countries. Air travel growth between nations
is affected by these wor1d economic forces as a result of changing
affluence and income distribution.
As it is obvious in the table II. c at air transport is very sensitive to
macro-economic parameters and general cycles of boom followed by
recession. The period 1973 - 1995 is monitored in the perspective of air
transport progress and certain typical terms are mentioned with their
characteristics as follows:
1973-1975: Wor1d air travel growth declined to 4.7 % per year
• The first wor1d oi1 crisis bεgan in 1973.
• Economic stagnation and recession plagued the wor1d in 1974 and
1975.
• Rising energy and fuel costs brought rapid increases in air fares-
averaging 13 % per year in current terms and 3 % in real terms.
23

1975-1979: World air travel rebounded to a rate of 10.7 % per year.
• The world economy recovered to a level ofmoderate growth (4.4% per
year in real terms).Consumer spending accelerated.
• Energy prices stabi1ized, enabling more gradual current-terms fare
increases and substantial fare decreases in real terms averaging 2% per
year.
• "Open skies' ’ policies and deregulation increased airline competition
and brought new travel bargains.
1979-1983: World travel growth declined to 2.4% per year.
• In 1979, another oi1 crisis presaged a second m 매 or world recession in
this era.
• Airline fuel costs skyrocketed.
• Air fares initially rose rapidly as airlines struggled to stem the
increases in fuel prices. Yields increased 23 % in 1980.
• High interest rates detrimentally affected consumer spending and
economic problems.
1983-1987: With lower oi1 prices beginning in 1983, the previous trends
have reversed.
1988-1990: World travel growth resumed to decline approximately 2.5 %
per year.
1990-1992: World travel growth declined to 2.4 % per year.
• Another recessionary era had been realized due to Gulf Crisis.
• Oi1 prices rapidly increased contributing to escalate air fares which
have sharply curtai1ed demand for air trave1.
• Investments were stopped and business relations got slower.
24

1992-1995: World air travel growth resumed to increase up to 2.1% per
year. After Gulf Crisis economic conditions have been stabilized.
25

3. FLEET PLANNING
3.1 Meaning ofFleet Planning
The general f1eet planning problem of an air1ine can be summed up as
follows: how many airplanes of different type should an air1ine have in
this f1eet to meet its objectives.
In air1ine operations many factors must be considered before reaching
the critical decision to acquire a particular number of a particular aircraft.
All operating departments become involved in determining the number
and type of aircraft required to implement the corporate strategy in the
future periods. This process is referred to as f1eet planning or the aircraft
selection procession. In this study we will call this process f1eet size and
composition process. This section provides an overview of the m 메 or
approaches to the f1eet size and composition process and presents a
model which will be a starting point for future more complex models.
From an individual air1ine’s standpoint, the f1eet size and composition
determination is an ongoing function coordinated by a generalist group,
such as corporate planning, with major help from technical, or specialist
administrations such as finance and property, marketing, engineering,
maintenance and f1ight operations.
Basically, corporate planning is interested on four different areas in the
f1eet planning process. They are carrier’s current resources, corporate
objectives,projected industry environment and marketing strategy.
26

Table 3.1
ORGANIZATION OF FLEET PLANNING
AIRLINE ACTIVITIES
involved in f1eet planning
cenario oftech
PASSENGER
&FREIGHT
SALES &
MARKETING
뼈
m
r
빼
.짜
·m
빼
t
싸때
빠빠
씨때
m
야따
4ι
mA
t
하
t
k
Objectives
m
Rm
υ
빼뼈
t
없
熾
빼
m
聯
1
씨
r
rr
nv
「
L
。‘。
Resources
ptimization of
redit
CREW
Acquisition
Training(Ini-
tial and on
type)
Commonality
Stations
Rentals
Handling ag-
reements
Slots
Current rεsources inc1ude its present f1eet inventory by type of aircraft.
Also inc1uded are the financial and technical data on aircraft. Financial
data inc1ude:
• Acquisition cost (purchase or lease),
• Start-up costs (primari1y maintenance and f1ight training),
• Unit operations costs.
27

Technical data on aircraft inc1ude:
• Payload/range data,
.. Cruise performance
• Runway requirements,
• Noise,
• Parts and service avai1ability,
• Flight characteristics.
Corporate objectives, or top management’s objectives for the company,
include forecasted profitability operating revenue and expenses,
operating income, nεt earnings, and return on investments, systemwide
load factor, acceptable level of cash on hand, market share on prime
routes and general guidelines regarding new aircraft acquisition.
Projected industry environments inc1ude the out1ook for the national
economy, the industry out1ook and the carrier’s performance within the
industry.
Projected industry environments inc1ude the out1ook for the national
economy, the industry outlook and the carrier’s performance within the
industry.
Given the company’s current resources, corporate objectives and
projected industry environments and market strategy determines how the
carrier is going to implement thε plan. Significant items to be considered
are the level of service between key city pairs, emphasis on long-haul or
short-haul markets or both, which weak markets should be penetrated
now or later or eliminated, and which markets profit can be traded off for
market share or vice versa. A critical area of consideration are fare and
rates structure levels in various markets for both passenger and cargo
servICe.
28

3.2 Approaches for Fleet Planning
The papers appearing in the literature dealing with vehicle fleet size
and composition can be classified into two [9]:
(1) those dealing with problems where the type of vehicles to be
operated are given and the decision to be made relates to the number of
each ηpe to operate (vehicle fleet size problems).
(2) those dealing with problems where the decisions to be made relate
to both the type of vehicles to operate and also the number of each type
(vehicle fleet composition problems).
Vehicle fleet size problems have received more attention in the
literature than vehicle fleet composition problems, which have been
relatively inadequately treated.
An ear1y attempt to tackle the problem of fleet size optimization was
made by Kirby[10]: He tackled the two-sided problem ofboth pre 、 renting
a low utilization of owned wagons in a small rai1way system and
conversely preventing the frequent hire of costly extra wagons. By
detεrmlnlng the relative cost of owned and hired wagons per day,
together with a probability distribution for the number of wagons needed
each day, he obtained an expression for the total expected cost per day.
From this he was able to determine the number of owned and hired
wagons that would minimize cost. Wyatt [11] later extended Kirby’s
approach.
Gould [12] developed a linear programming model for vehicle fleet
composition. His paper consisted of a case study of a company having to
meet considerable (seasonal) variations in (known) demand with twelve
possible types ofvehicle avai1able.
29

Besides these studies Etezadi [9] has made a survey and found out that
Maskel1, Ei10n et., Mole, Parkish, Woods and Harris, and Levy et.al. also
had made studies and had adopted differεnt approaches to the f1eet size
composition. This survey is summarized below.
Maskel1 [16] adopted a simulation approach to the problem of
determining the optimal size of a vehicle f1eet for local deliveries. He
assumed a f1eet composed solely of one type of vehicle and found by
simulation of many days' operations, for a number of different vehicle
f1eets, the balance between owned and hired f1eet capacity that would
minimize costs. These results were then compared with those obtained by
using Wyatt’s approach to the problem and found to agree.
Ei10n et a1.[17] in their book on distribution management devote to
only a few pages to vehicle f1eet composition, presenting an integer
programming formulation ofthe problem.
Mole [18] developed a dynamic programming formulation of the
problem of deciding the timing of investment in new vehicles in response
to the presence of demand trends, such as an increasing level of demand
(Albeit with seasonal f1uctuations).His formulation however, was limited
to considering only a single type ofvehicle.
Pari 없 1 [19] adopted a completely different approach to the problem of
deciding the optimal f1eet size. He considered the service level achievεd
(where service level was defined as the time between receipt of an order
and its delivery).
Woods and Harris [20] used a simulation approach developed by
Sikora [21] for investigating f1eet composition for concrete distribution.
30

Levy et. al. [22] presented an integer programming formulation ofthe
vehicle fleet composition problem and also developed several heuristics
for the problem.
A mode1 is developed by Christopher Colin New [13] for planning the
acquisition and disposal of equipment in a transport fleet. It deals
specifically with the composition over time of a commercial airline fleet
but is generally applicable to any problem of fleet planning under
conditions of technological change with budgetaη constraints on
Ìnterrelated projects and dealing with items which deteriorate with use.
The model used in case study for THY is a simplified version of this
one,developed by Christopher Colin New [13].
3.3 The Model
3.3.1 Related Definitions
Present Value
The present value is a future amount discounted at a given interest rate.
This value enables us to recognize time valuε of money, to consider f10w
of funds throughout planning period, and to identify best investment by
selecting project whose discounted returns are higher than alternatives.
31

Formula:
PV = FV / (1 + i t
PV = Present value of a cash f10w
FV = Future value of a cash f10w
i = Interest rate
n = Number ofperiods
Costs
Cost evaluation for an airline takes a higher dεgree of importance since
revenue gained is nothing if it does not cover total cost incurred.
Direct Variable Operating Cost
+
Direct Fixed Operating Cost
Capital Cost
= cash Direct Operating Cost (cDOC)
+
= Direct Operating Cost (DOC)
+
Indirect Fixed Cost and Overheads (IOC)
= TOTAL COST (TOC)
cDOC and DOC depend directly on the chosen f1eetlaircraft type.
32

Table 3.2
Direct Operating Cost
Fuel
~ ariablecrew
Maintenance
Airport/en
route
Variable
DOC
FixedDOC
33
gb
t
·m
랴
없
”
m
m
m
T·K
O
C
11 fudirect OC
11 Variable DOC
口 FixedDOC
Station
passenger
erVlces
lSales
bverhead
Uwnershìp
Insurance
Fixed crew
Maintenance
overheads

The A.E.A. (Association of European Air1ines) Model for Traffic
Forecasting
TRAFFIC = a*GDP b * YIELD-c
;b ,c z 0
Where:
GDP = d*GDPl + (1 -d)*GDP2
YIELD = Weighted average yield / (d*PCDl + (1-d)PCD2)
GDP = best found εconomic aggregate predictor for country i
PCDi = private consumption dεflator of country i
d,l-d = proportion of sales in each country.
3.3.2 Problem
The airlinε fleet planning problem can be defined as the allocation of
aircrafts to its various routes in such a way that it
(1) meets all the expected demand (or some proportion of it) for its
flights on those routes
(2) keeps the cost of its operations to a minimum (provided that a
certain specified demand is mεt the revenue will be constant and
maximizing contribution is equivalent to minimizing costs.)
34

Hence, given a set of aircraft (the existing fleet) and a set of routes-
demands it would be possible to choose an allocation pattern giving us a
minimum cost, this would thén probably have to be modified in the light
of scheduling difficulties not inc1uded in the simple allocation model.
The real problem, however, is only apparent when we consider a multiple
period model in which:
(1) Existing aircraft become increasingly costly to operate in terms of
maíntenance expense
(2) Somε existing aircraft must be scrapped or given m 며 or overhauls.
(3) New aircrafts become avai1able with different capacities and cost
profiles.
(4) The total demand for seats changes over time both in terms of the
balance across route classes and the actuallevel.
(5) The airline may be faced with quite tight budgetaη constraints in
respect of new purchases in any one period.
The simple allocation model therefore (even if we prepared to ignore
the scheduling problems) is rapidly getting more complicated.
Replacement of the fleet with technologically advanced aircraft is
required following steps; capital invεstment under budgetary constraints
on interrelated projects and matching avai1able capacity against rising (or
falling) demand on different route classes.
35

3.3.3 A General Linear Model For Fleet Planning
Assumptions:
(1) The m ‘ jor assumption of the model concerns the questions of profit
contribution maximization or cost minimization. The objective sεt up
below is to minimize operating costs over the period considered. The
basis of this is that to a large extent price-setting is outside the control of
a single airline, so that once the airline has decided on (a) which routes to
fly, (b) which ‘’scheduling" pattern it will try to use (i.e. stop-over,
etc...) and (c) either what share of the market it can expect or what share
it is going to aim for, then the only way it can increase profit is to cut
costs.
(2) There is, in general, a fixed cost associated with introducing a new
aircraft ηpe into an existing fleet. As a first approximation we may
regard this as an extra cost associated with the first aircraft of that type
purchased. Thε cost of additional aircraft beyond the first, or of additions
to the existing f1eet types is simply the cost ofthe aircraft.
(3) The airline is only concerned with satisfying (i.e. non-cargo) traffic
on scheduled (i.e. non-charter) flights.
(4) The cost of depreciation and maintenance of an aircraft changes
according to the age ofthe aircraft.
(5) The resale value of an aircraft is simi1ar1y dependent on its age.
(6) In the general case all variables and constants are time dependent.
The Model by Christopher Colin New [13]:
Nomenc1ature (Decision variables are small case, whereas constants are
denoted by capitals):
36

aity- number of aircraft oftype i owned in period t, of age y,
Xijt= number ofaircraft oftype i operating on routes oftype j in t,
Mity= maintenance cost ofaircraft i ofage y in period t,
Iity insurance cost of aircraft i of age y in period t,
Eity= depreciation expense ofaircraft i of age y in period t,
bit- number of aircraft oftype i purchased at start ofperiod t,
Pit- price ofaircraft oftype i purchased at start ofperiod t,
Sity number ofaircraft ofηpe i sold at start of period t ofage y,
Rity= resale price ofaircraft i ofage y in period t,
Cijt cost per flight of operating aircraft i on routes type j in t,
Dijt- demand (in seats required) on routes type j in t,
Bt budget available for expenditure above resales in period t,
Ki = number of seats on aircraft type i,
Fij= number offlights possible with aircraft i on routes type j in a period,
Ti= service length ofaircraft type i,
Qit- the fixed cost of adding the first aircraft oftype i to an existing fleet
in period t,
Pt- discount rate in the t’th period,
H= planning horizon over which operations are considered;t=l...H,
Zit= number of aircraft ofηpe i which could be purchased in the market
in period t,
aioy Aioy defines the initial fleet size and age.
The objective is to minimise the Net Present Value (NPV) ofthe total
costs of operation and depreciation over the planning horizon of H
periods so that we satis 함 all the planned demands for flight seats.The
inc1usion of a depreciation term does not imply that this is a true cash
flow, it Ís merely a convenient method of avoiding problems concerned
with the terminal value ofthe fleet at the end ofthe planning horizon.
The total cost in period t is;
LiLj Xijt(FijCijt)
”
뼈
m
p
ν
때
꺼
기
「
m
녕
m
M
/
----‘

+ L iLy (Mity + Iity) aity (Maintenance + Insurance costs)
+ L iLyEitya ity
鋼
m
f
p
ν
n
.m
따
.
m
m
해
i
D
/
l
l

- L iLyRitySity (Sales of aircraft)
so that the problem is to;
min Lt=l ,’I ’ (I1t=l,T 1/(1+Pt)( Li=l,N(Lj=l,M XijtCijtFij +
L y= o 퍼 (Mity+Iity+Eity)aity - ε y= O ， Ti RitySity)))
subject to the following constraints:
(1) Demand on each route ηpe in each period must be met:
Li=l,NXijtFijKi ~ Djt j=I...M; t=I...T,
(2) The total number of aircraft flying on routes of each type can
not exceed the number on hand:
Lj=l,M Xijt ~ L y= o 퍼 aity i=I...N;t=I...T,
(3) The aircraft inventory must be consistent:
aity=ai(t-l)(y-l) - St(yi-l) i=I...N;t=1...T;y=I...Ti,
(4) Budgetary constraints:
Lt=l,n bitPit - L t=l,NLy=o,TRitySity ~ Bt t=1...T,
(5) Sale of aircraft constraints:
Sity ~ ai(t-l)(y-l) i=I...N;t=I...T;y=I...Ti
Sit(Ti) = ai(t-l)(Ti-l)
38

(6) Availability of aircraft for purchase constraints:
bit ~ Zït i=l...N; t=l...T.
The model above inc1udes all the required variables except the
fixed cost off1eet set-ups (Qit)
One formulation is to use the binary selection variables specified
as;
o,if aircraft type i is not added to f1eet in period t
Oit = {
1, ifaircraft type i is added t fleet in period t
Total set-up cost involved are
LiOitQit for period t
with Oit =(0,1) all (i,t) and Lt=l,T Oit < 1 for all i.
However,as indicated above this would add considerably to the
computational problems.The number of a1ternative new aircraft over a
reasonable time span is likely to be quite small so that it is almost
always feasible to locate the global optimum by finding local optima for
each po~sible combination of new aircraft additions and then adding the
LiOitQit to these, the optimum would then be the lowest of the resu1ting
costs.
Integer solution requirements in the model can be summarÎzed as
follows:
39

Since all the aity are necessarily integer and it might be contended that
the xijt should be also, it is necessary to consider the effect of ignoring
any integer contraints in the solution [13] ;
(1) The interpretation of fractional Xijt is clear1y that one can allocate a
proportion of aircraft availability to a particular route mileage class.For
example,a flight New York-Brussels-Istanbul consists of one flight in the
long haul class and one in the medium haul class.The air1ine should
allocate as closely as possible to attain the required proportions.
(2) The aity , although integers, are likely to be large enough to ignore
the integer effects on the optimal value.
(3) Sales and purchase of fractional aircraft create considerable
problems and their effect can only be studied through the values of the
respectivε dual variables.
4. Case Study for THY
As we already noticed an airline operation has a very complex structure
and therefore, it may become very difficu1t to construct a viable model
since the number of variables involved in any time period solution would
be so large that integer programming solutions would not be useful.
Thereforε ， in order to be able to run a simple model on a time sharing
basis to illustrate some of the possible uses of such a model a number of
simplifications were made to reduce the size.
40

THY is basically operating on the three route classes postulated (i.e.
short, medium and long) using at present eight aircraft types.
Thε data avai1able concerns :
(1) The number of block hours each aircraft can fly in one year.
(2) The cost for each block hour for each aircraft on each route type.
(3) The numbεr of seats avai1able on each aircraft.
(4) The present make up ofthe fleet in terms ofaircraft types.
The basic data relevant to the problem is tabulated in Table IV.a, Table
IV.c and Table IV.d, objective function is the computation of the total
cost of operating an aircraft on a route in one year such that the number
of seats avai1able from operating a certain aircraft on a certain route in
one year is going to meet estimated passengers flow.The model considers
one year with the demand shown in Table IV.b.
This is a decision making problem.The scenario is to purchase a number
of the aircraft types B734 and A343 but not to buy the other ηpes;
instead they may even be sold.As the experiences have shown that B734
is the most convenient a/c type regarding of revenue yield and A343 is
essential for particular long haul routes present in the THY network.
However, purchasing more than one A343 is restricted due to crew and
schedule rεquiremen t.
41

Table 4.1
A)NUMBER OF SCHEDULED BLOCK HOURS PER A/C
A/C SHORT HAUL MEDIUM HAUL LONGHAUL
RJ70 11,840
RJ100 27,800
A312 7,150 8,520 3,190
A313 1,240 3,000 6,780
A318 1,870 1,600 5,160
A343 560 1,670 23,725
B734 38,545 80,670
B735 1,512 4,670
Table 4.2
B)DEMAND FORECASTS (PASSENGERS)
SHORT HAUL MEDIUM HAUL
8,200,000 5,000,000
42
LONGHAUL
900,000

Table 4.3
C) THY FLEET COMPOSITION
SEAT MAX
A/C NUMBER CAPACIT Y RANGE( km)CLASSIFICATION
RJ70 4 79 2,407 SHORTHAUL
RJ1 00 10 99 2,259 SHORTHAUL
A312 7 225 6,480 LONGHAUL
A313 4 210 8,100 LONGHAUL
A318 3 176 8,950 LONGHAUL
A343 5 279 11,952 LONGHAUL
B734 28 150 3,350 MEDIUMHAUL
B735 2 117 3,865 MEDIUMHAUL
Table 4.4
D)COSTS PER BLOCK HOURS(USD)
A/C SHORT HAUL
RJ70
RJ1 00
A312
A313
A318
A343
B734
B735
MEDIUM HAUL LONG HAUL
2,988
3,313
7,262
7,698
7,185
9,065
3,993
3,246
5,929
5,679
5,344
5,971
3,432
3,102
2
5
8
πι
，、)
。。
<
J
,
J
K
O
3
A
Q
각
0.
Q4
1
」
---」
43

Let’s denote related variables as follows;
RJ70SH = the number of aircraft type RJ70 assigned for short haul,
RJ1 OOSH = the number of aircraft type RJ1 00 assigned for short haul,
A312SH = the number of aircraft type A312 assigned for short haul,
A312MH = the number of aircraft type A312 assigned for medium haul,
A312LH = thε number of aircraft type A312 assigned for long haul,
A313MH = the number of aircraft type A313 assigned for medium haul,
A313LH = the number of aircraft type A313 assigned for long haul,
A318MH = the number ofaircraft type A318 assigned for medium haul,
A343 I좌-I = the number of aircraft type A343 assigned for medium haul,
B734SH = the number of aircraft type B734 assigned for short haul,
B7341 딴 I = the number of aircraft type B734 assigned for medium haul,
B735SH = the number of aircraft typε B735 assigned for long haul,
B735LH = the number of aircraft type B735 assigned for medium haul.
then LP formula shall be constructed as below;
Min C = { 11,840 x 2,988 x RJ70SH + 27,800 x 3,313 x RJ1 00SH +
7,150 x 7,262 x A312SH + 8,520 x 5,929 x A312MH + 3,190 x 9,852 x
A312LH + 1,240 x 7,698 x A313SH + 3,000 x 5,679 x A313MH + 6,780
x 10,085 x A313LH + 1,870 x 7,185 x A318SH + 1,600 x 5,344 x
A318MH + 5,160 x 9,358 x A318LH + 560x 9,065 x A343SH + 1,670
x 11,065 x A343MH + 23,725 x 13,479 x A343LH + 38,545 x 3,993 x
B734SH + 80,670 x 3,432 x B734 I따-I + 1,512 x 3,246 x B735SH +
4,670 x 3,102 x B735MH}
44

4
m
7
4
3
5
%ω
2
2
2
2
2
2
2
2
>-
S.T.
A) Inventory constraints;
RJ70SH
RJ100SH
A312SH+A312MH+A312LH
A343SH+A343 I좌 I+A343LH
B734SH+B734MH
B735SH+B735MH
B)Demand Constraint;
8 x 365 x (79RJ70SH + 99RJ100SH + 225A312SH + 210A313SH +
176A318SH + 279A343SH + 150B734SH + 117B735SH)
~ 8,200,000
4 x 365 x (225A312MH + 210A313MH + 76A318MH + 279A343MH +
B734MH + B735 1'v깐 1)
~ 5,000,000
2 x 365 x (225A312LH + 210A313LH + 176A318LH+279A343LH)
~ 900,000
C) Feasibi1ity Constraints;
RJ70SH, RJ100SH, A312SH, A312MH, A312LH, A313SH, A313MH,
A313LH, A318SH, A318MH, A318LH, A343SH, A343MH, A343LH,
B734SH,B734MH,B735SH,B734 I따 1 ~ 0
We assume that an aircraft can rotate 8 times for short haul routεs ， 2
times for medium haul routes and once for long haul routes.
The solution is reported in Table IV.e .1t shows optimum number of
each aircraft types in the fleet regarding of specified parameters besides
the number of each aircraft types which is distributed over the three
different ranges.The required decision is to sell one of RJ1 00’s and lease
140fB734’s and 2 ofA-343’s.
45

Table 4.5 Answer Report
Present Proposed Numbers
Aircraft Numbers
SH MH LH TOTAL
RJ70 4 4 0 0 4
RJ1 00 10 9 0 0 9
A312 7 0 2 5 7
A313 4 0 2 2 4
A318 3 0 0 3 3
A343 5 0 4 2 6
B734 28 11 31 0 42
B735 2 0 2 0 2
TOTAL 63 24 41 12 77
Results
1. One ofRJ 100’s is required to be sold, 14 of B734’s and 1 of A343 ’s
are to be leased to meet rising passenger demand with minimum cost
which is 11,707,076,085 USD.
46

2. Not on1y the required number of each subfleet is determined, but a1so
the utilisation of aircraft on each route ηpe is found. For examp1e, 2 of
A313’s are used in medium hau1 range and the remaining ones are used
in 10ng hau1 range.
3. THY is fully uti1izing the present fleet and needs to 1ease additiona1
medium and 10ng hau1 aircrafts to meet passenger demand.The optima1
number of aircrafts resu1ts in an amount of excess capacity 142,440
seats for short hau1, 15,100 seats for medium hau1 and 60,315 seats for
10ng hau1.These surplus values would be marginal benefit if demand
forecast is underestimated.
4. None ofthe aircrafts with big capacity is assigned to short haul ranges
because oftheir relatively high operational costs.
5. For integer 1inear programming models sensitivity ana1ysis in
c1assica1 sense is not avai1able. But, different scenarios can be evaluated
by changing related variables. For example, if THY intends to replace 3
of RJ100’s with 2 of new B734’s it saves 10,473,219 USD per yeat.
However, there are some domestic airports such as Samsun, Elazl 용，
Sivas etc. that on1y RJl00 can operate. For this operational reason, THY
shall maintain present number of RJI00’s in its fleet. Or, another
scenario that Airbus industry can not supply 1 A343 on due time,
consequently THY has to lease 2 of B734’s to meet medium haul
demand by costing 858,892,637 USD and doub1ing excess capaciη for
medium hau1.
6. THY shall purchase aircrafts for medium and long haul ranges
because of corresponding market expansion and re1atively low
operationa1 costs.
47

Conclusion;
With this study a brief analysis of THY demand forecasting process is
made and a general model for fleet planning is proposed.The general
model is tested in a limited version ofthe problem.
The conclusions can be summarized as follows:
THY with its monopolistic character in domestic market has an
increasing demand locally and by using the observations of last 6 years .
For the intεrnational routes quantitative approaches do not seem to be
suitable due to the variation in demand and THY’s efforts for entering
new markets.So mostly judgemental approaches should be utilised for
international market passenger demand forecasting.
On the other hand, linear programming solutions of basic modelleads
us to following conclusions;
THY’s existing fleet is almost fully utilised.THY should soon include
new aircrafts to its existing fleet after a careful examination of various
costs which has been disregarded to a large extent in our analysis.
Finally, a long term company po1icy will be very helpful in the
selection of aircraft type and sound demand forecasts, alternative cost
analysis will be used in the fleet size determination.
Also it is necessary to use the general model for more detai!ed analysis
including purchase, leasing, maintenance and sale for multiple period
planning process.This may be a topic for further study.
48

APPENDIX A
THY NETWORK
l-Domestic Lines
Adana Izmir
Istanbu1
Batman
Da1aman
Erzurum
Kahramanmara~
Mu~
Kayseri
Bodrum
Denizli
E1azl 흥
Kars
Sanhurfa
Siirt
Van
Samsun
Trabzon
Isparta Edremit
2- Internationa1 Lines
Tiran Hannover Kuwait
Vienna Hamburg Bishkek
Baku Munich Beirut
Bahrain Nuremberg Amsterdam
Brusse1s Stuttgart Kharachi
Sarajevo Ber1in Seoul
Sofia Athens Bucharest
Lefko~a Budapest Kazan
Copenhagen Jakarta Moscow
Cairo Tehran Jeddah
Lyon Tel Aviv Riyadh
Nice Rome Singopore
Paris Milan Capεtown
Ankara
Anta1ya
Diyarbaklr
Erzincan
Gaziantep
Ma1atya
Sinop
Tokat
Sivas
A 용 n
Zurich
Da1nascus
Bangkok
Tunis
Ash 암 1abat
Kiev
Odessa
Dubai
London
Manchester
NewYork
Chicago
Tashkent
Strassbourg Venice Johannesbourg
Tbilisi Osaka Madrid
Cologne Tokyo Stoc 암 101m
Dusseldorf Amman Basel
Frankfurt Almaty Geneva
49

BIBLIOGRAPHY
1. Munson, H.C. Aircraft Economic Obsolescεnce: Somε of the Factors
Involved in Making a Replacement Decision. Seattle, Washington.
Boeing Commercial Airplane Company, 1981.
2. Wells, A. T. Air Transportation: A Management Perspective
Wadsworth, 1984.
3. Airline Scheduled Planning and Evaluation Mode1, Califomia,
MC.Donell Douglas Corporation, 1988.
4. v..T
orld Air Travel Market Perspectiv~ ， Seattle, Washington . Boeing
Commercial Airline Company, 1985.
5. Replacement Fleet Analvsis for THY, Seattle, Washington. Boeing
Commercial Airplane Company, 1985.
6. Makeower, M.S. and Wil1iamson E 0perational Research , Hodder
and Stoughton, 1985.
7. Tersine, R.J. Production/Operations Management: Concepts,
Structure, and An alvsi~ ， Elsevier Science Publishing Inc. , 1985.
8. Forecasting Traffic and Capacitv Growth in Europ~ ， Airbus Industry,
1989.
9. Etezadi, T. and Beasley, J.E. “ Vehic1e Fleet Composition 끼 뇨끄파떠
of Ooerational Research Society, Vo1.34, pp. 87-91, 1983.
10. Kirby, D. “Is your fleet the right size 7" 0oerational Research
Ouarterly, Vol. 10, pp.252-253, 1959.
11. Wyatt, J.K. Optimal Fleet Size, 0perational Research QUé1rterly,
Vol. 12, pp. 186-187, 1961.
50

12. Gould, J. The Size and Composition of a Road Transport Fleet,
Operational Research Ouarterly, Vol. 20, No.1 , pp. 81-83, 1969
13. New, Christopher C. Transport Fleet Planning for Mu1ti Period
Operations , 0perational Research Ouarterly , Vol. 26, No.1, ii, pp.1 51-
166, 1975.
14. Witt, Stephan F. and Moutinko, Luiz, Tourism Marketing and
Management Handbook;, Prentice Hall International, Hertfordshire,
1989.
15. Ozkul A. E1αem ， Fiziksel Dagitim Sistemlerinde Tasima Sorunlarina
Analitik Yaklasim, Anadolu Universitesi Yayinlari No.260, Eskisehir,
1988.
16. T.W. 0 ’C. Maskell (1966) The optimal size ofvehic1e f1eet for local
d 려 iveries. M. Sc. Report, Management Engineering Section, Imperial
CoIlege, London.
17. S. Eilon, C. D. T. Watson-Gandy and N.Christofides (1970)
Distribution Management: Mathematical Modelling and Practical
Analvsi~ ， Griffin, London.
18. R. H. Mole (1 975) Dynamic optimization of vehic1e f1eet size.Opl
Res. Q. 26. 25-34 .
19. S. C. Pari 암 1 (1977) On a f1eet slzlng problem and allocation
problem. Mgmt Sci. 23, 972-977.
20. D. G. Woods and F. C. Harris (1979) Truck f1eet size composition
for concrete distribution. Int. J. physical Distribution 10, 3-14.
51

21. Z. M. Sikora (1 975) A study of vehicle composition in concrete
distribution. M. Sc. Report, Department of Management Science,
Imperial College, London.
22. L. Levy, B. Golden and A. Assad (1 980) The f1eet size and mix
vehicle routing problem. Management Science and Statistics Working
Paper No. 80-011. College of Business and Management, University of
Maryland at College Park.
52

REFERENCES NOT CITED
Iktisadi RapoI ,TOOB, Yayin No : Gene1165, 1990.
Air1ine Business Magazin~ , Issues: February, May, August, September
1989.
Educational Seminarê, xvrth
ASTA International Conference
Heidelberg, W-Germany,March 1st, March 5th
, 1988.
Leite, Si1vio C. , The Effectiveness of an Air1ine’s Passenger Service
sy 얄흐쁘. Swissair Marketing Data Services, Switzer1and.
Traffic Svstem Facilitieê. Swissair, 23 November 1981.
Some Past Present and Future Trends In the Travel Industrv. Airline
Executive,pp. 27-29, August 1985.
J. P. Hanlon ,Air Fares , Pricing and Tourism. Paper Presented to L.T.M.
,Birmingham University . ,p.153 ,March 1982.
Traffic Forecast and Scheduleê. California Mc. Donell Douglas,
Corporation,May 1988.
Current Market Outlook. Wor1d Travel Market Demand and Airplane
Supply Requirements , Seattle, Washington Boeing Commercial
Airplane Company, 1988.
Turk Hava Yollari Yillik Faalivet Rapon! , THY Genel Mudur1ugu ,
1989.
Wlor1d Tourism Organizatio!! , Economic Review of Wor1d Tourism
(WTO , 1986).
53

Box ; G.E.P. and Jenkins , G.M. , Time Series A1.1.alvsis: For~casting and
드 Q 따띤 1 ， Holden-Day , 1970.
Witt , S.F. and Martin , C.A. , Forecasting Future Trends in European
Tourist Demands , Tourist Review , Vol. 40 ,No.4 , pp.l2-20 , Oc t.띠 ec.
1985.
Martin , C.A. and Witt , S.F. , Forecasting Performance , ToUf1sm
Managemen1 , Vo1.9,No.4 ,pp.326-329,Dec.l988.
Map Programme , Airline Fleet Planning and Aircraft Economics 3-7
June 1996 , Euresas
54

GLOSSARY
Air transportation the commercial system of air transportation
consisting of domestic and international certificated or charter carriers.
A 뇨얀효 ft all airborne vehicles supported either by buoyancy or dynamic
action.Used in this text in a restricted sensε to mean an airplane-any
winged aircraft , including helicopters but excluding gliders and guided
missi1es.
Aircraft departure schedule4 a take off scheduled at an airport , as set
forth in published timetables.
Aircraft hour. block to block the, time elapsed from the moment an
aircraft first moves under its own power for purposes of flight unti1 it
comes to rest at the next point of landing. Block time includes taxi time
before take off and after landing, take off and landing time, as well as
airborne time. Also referred to as ramp to ramp hours.
Aircraft hour ‘ revenue an aircraft’s airborne hours in revenue service,
computed from the moment it leaves the ground unti1 it touches the
ground at the next point of landing.
Aircraft industry the industry primari1y engaged in the manufacture of
the aircraft , aircraft engines and parts , aircraft propellers and parts , and
aircraft parts and auxiliary equipment. A sector ofthe aerospace industry.
Aircraft mi1es (plane mi1es2 the mi1es computed in airport to airport
distances for each inter-airport hop actually completed , whether or not
performed in accordance with the scheduled pattern.
55

Aircraft . passenger/car-gQ an aircraft configured to accomadate
passengers and cargo in the above-deck cabin.
Aircraft tyo~ a distinctive model , as designated by the manufacturer.
Aircraft . wide body a generic and commonly used term applied to jet
aircraft (turbo-fans) with a fuselage diameter exceeding 200 inches
whose per-engine thrust is greater than 30,000 pounds (for example ’
Boeing 747 ,Mc.Donell-Dougles DC-I0 ,Lockheed L-I0l 1).
Aircraft Traffic Conference (ATC) a part of the Air Transport
Association , which represents the U.S. scheduled airline industη.The
ATC is made up of a number of committees and subcommittees whose
members are representatives from the member carriers.Improved service ,
streamlined procedures , and reduced costs are all goals sought by the
members of the Air Traffic Conference.Onε of the m 매 or functions
performed by the conference is the approval of travel agents who do
business with the airlines.
Air transportation industry all civi1 flying performed by the certificated
air carriers and general aviation.
Avai1able seat m i1 e~ the total of the products of aircraft mi1es and
number of avai1able seats on each flight stage, representing the total
passenger-carrying capacity offered.
Avai1able seat~ the number of seats installed in an aircraft (including
seats in lounges) , exclusive of any seats not offered for sale to the public
by the carrier , and inclusive of any seat sold.
56

Available seats oer aircrafï the average number of seats available for sale
to passengers , derived by dividing the total available seat miles by the
total aircraft revenue miles in passenger service.
Avai1able ton mile~ the aggregate of the products of the aircraft miles
f10wn on each f1ight stage multiplied by the available aircraft capacity
tons for that f1ight stage, representing the traffic-carrying capacity
offered.
탤뭘흐 r thε charter ofthe entire capacity of an aircraft or a part of it by a
tour operator , a person or a company for a specific purpose.Most of
charter operation is bεing made for inclusive tour which is rεquired to be
a round trip and its cost must include all accomodations and surface
transportation.
Charterer An air carrier holding a certificate of public convεnience and
necessity authorising it to engage in charter air transportation.
Citv pair~ the origin and destination cities of an air trip.
Common air carrier an air transportation firm that holds out its services
for public hire.
E 딱브_ the amount per passenger or group of persons stated in the
applicable tariff for the transportation thereof , including baggage unless
the context otherwise requires.
Fleet planning the aircraft selection process
표묘12. a city or standard metropolitan statistical area requiring aviation
services. Communities fall into four classes , as determined by their
percentage of the total enplaned passengers in scheduled service of the
domestic certificated route airlines in the 50 states , the Destrict of
Columbia , and other U.S. areas dεsignated by the Federal Aviation
Administration. A large hub is a community that enplanes 1 percent or
more oftotal enplaned passengers for all air
57

services in the United States; a medium hub from 0.25 percent to 0.99
percent; a smal1 hub from 0.05 to 0.24 percent; and non-hub less than
0.05 percent.
Hub and spoke network a system that feeds air traffic from smal1
communities to the traveler’s destination via connections at the larger
communlty.
IATA (International Air Transport Association2 a voluntary organization
open to any scheduled air carrier whose home country is a member (or
eligible to be a member) of the International Civi1 Aviation Organization
(ICAO). IATA’s main function is the economic rεgulation of
international air transportation, in particular, international rates and fares
those are set by one of seven regional or joint traffic conferences and
subject to unanimous resolutions of the carriers, provided that the
countries do not object.
ICAO (International Civi1 Aviation Organization2 a specialized agency
ofthe United Nations composed of contracting states whose purpose is to
develop the principles and techniques of international air navigation and
to foster the planning and development of international air transport.
Load factor. revenue passenger the proportion of aircraft seating
capacity that is actually sold and utilised.As it equals to revenue
passenger mi1es divided by avai1able seat mi1es.
h1aintenance. direc1 the cost of labor, materials, and outside services
consumed directly in periodic maintenance, repair, or upkeep of
airframes, aircraft engines, other f1ight equipment, and ground property
and equipment.
Operating revenue~ revenues from the performance of air transportation
and related incidental services,including (1)
58

transport revenues from the carriage of all c1asses of traffic in scheduled
and non-scheduled services, inc1uding the performance of aircraft
charters, and (2) non-transport revenues, consisting of federal subsidy
(where applicable) and the net amount of revenues less related expenses
from services incidental to air transportation.
Passenger enplanement~ the total number of revenues passenger
boarding aircraft, inc1uding or야 r더 191na 따 tion 괴 1 ，’ S와 to 야 p- 애 O아、 verζ ’ and on- ‘.쇠.
passenger.
Passenger m i1 e~ one passenger transported one mile.Passenger mi1es are
computed by multiplying the aircraft mi1es f10wn on each f1ight stage by
the number ofpassengers transported on that stage.
Passenger mi1e, nonrevenu~ one nonrevenue passenger transported one
mi1e.
Passenger mi1e, revenu~ one revenue passenger transported one mi1e in
revenue serVICe.
Passenger revenu~ a person recelvlng air transportation from the air
carrier for which remuneration is received by the air carrier. Air carrier
employees or others recelvlng air transportation against whom token
charges are levied, are considered nonrevenue passengers.Infants for
whom a token fare is charged are not counted as revenue passengers.
Passenger. revenue per alfcraft the average number of passengers carried
per aircraft in revenue passenger services, derived by dividing the total
revenue passenger mi1es by total aircraft mi1es f10wn in revenue
passenger serVlces.
Passenger revenue ton-m i1~ one ton of revenue passenger weight
(including all baggage) transported one mi1e.The passenger weight
standard for both domestic and international operations is 200 lb.
59

Passenger sεrvicε expense~ costs of activities contributing to the
comfort, safety, and convenience of passengers whi1e in flight and when
flights are interrupted.Includes salaries and expenses of cabin attendants
and passengers food service.
Pavloafl the actual or potential revenue producing portion of an aircraft’s
take-off weight in passengers, free baggage, excess baggage, freight,
express, and mai1.
Revenue aircraft mi1es the total aircraft mi1es flown in revenue service.
Revenue passenger a person recelvlng air transportation from an air
carrier for which remuneration is received by the air carrier.Air carrier
employees, or others recelvlng air transportation against whom token
service charges are 1εVlεd ， are considered to be nonrevenue
passengεrs .I nfants for whom a token fare is charged are not counted as
revenue passengers.
Revenue passenger mi1e (RPMì one revenue passenger transported one
mi1e in revenue service. Revenue passenger mi1es are computed by
summation of the revenue aircraft mi1es flown on εach inter airport flight
stage multiplied by the number ofpassengers carried on that flight stage.
Revenue ton-m i1~ one ton of revenue traffic transported one mi1e.
Revεnue ton-mi1es are computed by mu1tiplying tons of revεnue traffic
by the mi1es this traffic is flown.
Ro 파흐 a system of points to be served by an air carrier, as indicated in its
certificate of public convenience and necessity.A route may include all
points on a carrier’s system or may represent only a systematic portion of
all ofthe points within a carrier’s total systεm.
60

SchedulinK- the art of designing systemwide f1ight patterns that provide
optimum public service, in both quantiη and quality, consistent with the
financial health ofthe carrier.
Seasonal trendê. changes in an economic index that are caused by or
related to changes in the seasons ofthe year.
Seat m i1~ one passenger seat transported one statute m i1 e.Usεd to report
avai1able passenger carrying capacity on an aircraft; however, when the
seat is occupied by a revenue passenger,the measurement unit is referred
to as a revenue passenger mi1e (RPM).
Seat mi1e. avai1able the aircraft m i1 εs flown on each f1ight stagε
multiplied by the number of seats avai1able for revenue use on that stage.
Sl 으 1 arrival or departure time of a flight schεduled at a particular station
Traffic. air the passengers and cargo (freight, express, and mail)
transported on any aircraft movement.
Yi 벌다 the air transport revenue per unit of traffic carried in air
transportation. May be calculated and presented several ways, such as
passenger revenue per passenger mi1e, per aircraft mi1e, per passenger
ton-mi1e, or per passenger.
61

Aircraft Fleet Planning

Recommended

Recommended

More Related Content

Similar to Aircraft Fleet Planning

Similar to Aircraft Fleet Planning (20)

More from Sara Alvarez

More from Sara Alvarez (20)

Recently uploaded

Recently uploaded (20)

Aircraft Fleet Planning