pre-study of trafﬁc planning optimizer for potential ...448882/fulltext01.pdfabstract pre-study of...

Pre-study of traffic planning optimizer for potential

implementation at TUIfly Nordic

Per Enqvist, Senior AdvisorClement Berguerand, Supervisor at TUIfly Nordic

Department of Mathematics

Royal Institute of Technology

December 2010

Copyright © 2010 Mercedes Inal and TUIfly Nordic

All Rights Reserved

ABSTRACT

Pre-study of traffic planning optimizer for potential

implementation at TUIfly Nordic

Mercedes Inal


Master of Science

Optimization in the airline industry is hard to accomplish,there are many areas of equal importanceall of which with important and often contradicting parameters to take into account in order toachieve a model that represents real world situations. Thisreport gives a unique account of theplanning process as it is observed for the duration of this project at TUIfly Nordic. It is far froma complete documentation of airline planning processed butit is an insight as to how one suchprocess takes place. With focus on the scheduling process for air-traffic program it was found thata risk management analysis into the operating parameters ofa schedule is necessary and that beforeresearching the implementation of third-party software, an inventory of the software available atthe time of writing is more beneficiary to the current work flow.

Keywords: Airline optimization, air-traffic planning, TUIfly Nordic.

ABSTRAKT

Förstudie av ett optimerings program för flygtrafik

planering och dess potentiella implementation hos

TUIfly Nordic

Mercedes Inal


Master of Science

Optimering i flygbranschen är svårt att åstadkomma, det finnsmånga områden av lika stor bety-delse alla med viktiga och ofta motstridiga parametrar att ta hänsyn till för att uppnå en modell somrepresenterar verkligheten. Denna rapport ger en unik bakgrund av flygtrafikplanerings processensom observerats för det här projektet på TUIfly Nordic. Det ärlångt ifrån en komplett dokumenta-tion av hur flygbolag utför sin flygtrafikplanering, men det ären insikt i hur en sådan process sker.Med fokus på schemaläggnings processen för flygtrafiken konstaterades det att en riskanalys avdriftparametrar som påverkar ett flygtrafikschema är nödvändigt och att innan forsking läggs nerför investering av tredje partens programmvara är det rekommenderat att utföra en undersökning itillgänglig programvara som kan underlätta det nuvarande arbetsflödet.

Nyckelord: Airline optimization, air-traffic planning, TUIfly Nordic.

ACKNOWLEDGMENTS

I would like to acknowledge the people at TUIfly Nordic for their knowledge and insights that by

far make up the body of this project it has been a great experience. Thank you to my supervisors

for good advice and at times pushing the project forward oncemore, Clement Berguerand for

reviewing this document and for ripping it to shreds as everygood advisor should do to a thesis

draft, Anne-Lie Bråholm and Marcus Karlsson the Planners. There are several more that have

helped me along the way and I am grateful to have met all of you and appreciate all the help you

have given me along the way.

My most heartfelt thank you to my senior supervisor Lecturerand researcher at KTH Per

Enqvist for his patience, countless amounts of advice in hard times and general therapy.

Finally, I would like to offer condolences to a great teacher, Professor Ulf Jönsson, you will be

missed.

Contents

Table of Contents v

List of Figures vii

List of Tables viii

1 Introduction 11.1 Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 21.2 Literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 21.3 Purpose and problem description . . . . . . . . . . . . . . . . . . . .. . . . . . . 3

1.3.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41.3.2 Limitations and demands . . . . . . . . . . . . . . . . . . . . . . . . .. . 5

1.4 TUI AG Group . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61.4.1 TUIfly Nordic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61.4.2 Tour operator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

1.5 Cost structure for TUIfly Nordic . . . . . . . . . . . . . . . . . . . . .. . . . . . 8

2 Planning process 102.1 Timeframe and involved parties . . . . . . . . . . . . . . . . . . . . .. . . . . . . 112.2 Long term fleet plan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . 122.3 Product and content . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . 142.4 TUIgether . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .142.5 Planning and production . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . 142.6 Long and medium term traffic planning . . . . . . . . . . . . . . . . .. . . . . . 15

2.6.1 Maintenance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162.6.2 Slots and traffic rights . . . . . . . . . . . . . . . . . . . . . . . . . .. . 192.6.3 Resource planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .19

2.7 Handling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 212.8 Crew Pairing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .222.9 Duty limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . 252.10 Pricing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 26

v

CONTENTS vi

3 System processes 283.1 Systems description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . 29

3.1.1 IDPS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 303.1.2 Sabre Rocade Suite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .34

4 Airline Optimization 384.1 Schedule planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . 39

4.1.1 Schedule design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 404.1.2 Fleet assignment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .414.1.3 Aircraft routing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 434.1.4 Crew scheduling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

4.2 Background for combined model . . . . . . . . . . . . . . . . . . . . . .. . . . . 474.3 Simultaneous aircraft routing and crew scheduling . . . .. . . . . . . . . . . . . . 484.4 Solution suggestion for an extended simultaneous aircraft routing and crew schedul-

ing model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

5 Analysis 555.1 Software need . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 565.2 Key performance indicators . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . 585.3 Risk management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 605.4 Multiobjective optimization . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . 63

6 Discussion 666.1 Checklist for minimum software performance . . . . . . . . . .. . . . . . . . . . 666.2 Conclusions for the needs of TUIfly Nordic . . . . . . . . . . . . .. . . . . . . . 676.3 Future research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . 68

Bibliography 70

List of Figures

1.1 The TUI smiley . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .71.2 Cost structure comparison between short and long haul flights. . . . . . . . . . . . 8

2.1 Planning process at TUIfly Nordic . . . . . . . . . . . . . . . . . . . .. . . . . . 102.2 Timeframe in detail, overlooking several departments .. . . . . . . . . . . . . . . 112.3 Airplanes in production during 2010 and 2011. . . . . . . . . .. . . . . . . . . . 122.4 Decision chain during fleet planning stage . . . . . . . . . . . .. . . . . . . . . . 132.5 Inputs to the air-traffic program . . . . . . . . . . . . . . . . . . . .. . . . . . . . 152.6 Resource calculation chain . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . 202.7 Example of a current timeline for flight deck planning. . .. . . . . . . . . . . . . 212.8 Pairing demands . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . 232.9 Inputs into crew pairing stage . . . . . . . . . . . . . . . . . . . . . .. . . . . . . 242.10 Rules and regulations hierarchy . . . . . . . . . . . . . . . . . . .. . . . . . . . . 25

3.1 System overview at TUIfly Nordic . . . . . . . . . . . . . . . . . . . . .. . . . . 283.2 Systems interconnectivity and modules currently at useat TUIfly Nordic . . . . . . 293.3 IDPS module overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . 303.4 Screen shot of Arsis interface . . . . . . . . . . . . . . . . . . . . . .. . . . . . . 323.5 Screenshot of Opscon interface . . . . . . . . . . . . . . . . . . . . .. . . . . . . 333.6 Screenshot of Airpas interface . . . . . . . . . . . . . . . . . . . . .. . . . . . . 343.7 RM5 module overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 353.8 Screenshot of the RM5 module: PAR interface . . . . . . . . . . .. . . . . . . . . 36

4.1 Schedule design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . 39

5.1 Systems Today . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .565.2 Simple flight schedule example . . . . . . . . . . . . . . . . . . . . . .. . . . . . 595.3 Risk parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . 615.4 Risk assessment model . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . 625.5 Risk management actions . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . 635.6 Marginal allocation example . . . . . . . . . . . . . . . . . . . . . . .. . . . . . 65

vii

List of Tables

1.1 Current fleet at TUIfly Nordic. . . . . . . . . . . . . . . . . . . . . . . .. . . . . 71.2 Example of rotations with various crew demands. . . . . . . .. . . . . . . . . . . 9

5.1 Key performance indicators. . . . . . . . . . . . . . . . . . . . . . . .. . . . . . 58

1 Legs flown by aircraft type per month. . . . . . . . . . . . . . . . . . . .. . . . . 73

viii

1

Introduction

It’s not always easy to appreciate the workload that goes into planning an airplane route whenyou are stranded in an airport on the far-side of the planet waiting for that delayed flight home atthe end of your two week vacation. However the effort behind planning aircraft rotations is anintricate process and it is a daily challenge to accommodatethe needs of passengers in a changingenvironment, that make that two week vacation in, for instance Thailand, possible.

The airline business is a competitive and ever-changing world where charter companies have tokeep up with the rapid pace, forcing them to always provide the customers with high quality serviceand secure products at the same time.Air-traffic planningstarts well over a year and a half beforeexecution date and the process operates within very tight margins so that even small improvementsin aircraft rotationsefficiency can directly result in substantial cost reductions and savings. A fullyoperational air-traffic schedule is the result of compromises and sometimes negotiations betweenvarious and often opposing constraints. These constraintswill be studied throughout the stages ofthis project.

TUIfly Nordic has a growing need to rapidly evaluate and compare multiple traffic planningscenarios in order to determine and provide the customer (for TUIfly Nordic this customer is theTour Operator) with an optimal choice of air-traffic schedule. There is also a need to generateeconomical estimates for each scenario that yield alternatives or contingency plans if one scenarioshould fail. Multiple traffic scenarios would also permit the planning department to measure gainsor losses while followingkey performance indicators.

The objective of this thesis is to provide an answer to this need. The methodology used toreach this solution begins with evaluating the current process followed by a recommendation thatcould allow for implementation of additional software to accommodate this need. This softwareshall present any consequences, economical or other, in a correct and easy way while also bridgingthe gap between certain planning process stages.

1

1.1. STRUCTURE 2

1.1 Structure

The structure of this thesis is divided into three parts. Thefirst part (Chapters 1, 2 and 3), iscomprised by an introductory chapter that include several sections describing the background andpurpose of the problem presented by TUIfly Nordic. Chapter 1 also describes the limitations andneeds set by the airline business in general and the company specifically. A brief backgroundhistory of TUIfly Nordic is given but the weight of this chapter lies in the cost structure sectionexplaining how small changes to the air-traffic planning stage can result in large savings.

Chapter 2 provides a detailed description of the processes that are directly connected to theair-traffic planning stage at TUIfly Nordic. The descriptions of these processes are based primarilyon observations throughout the commitment of the thesis. While some sections of this industryare widely approached by scientific research (such as aircraft fleet planning and crew scheduling)others are left uncharted. The focus of this thesis lies in the early stages of planning the aircraftrotations but the influences exerted by the surrounding process are taken into account as they havea tremendous impact on the schedule making. This chapter will start describing the typical outlookand timeline for a winter season air-traffic planning, and, from there, describe each process as iscurrently carried out at TUIfly Nordic. Chapter 3, will give an overview of the software processesconcerning the planning stage, currently operative in TUIfly Nordic.

The second part (Chapter 4) is a mathematical approach to describe some of the planningprocesses that are represented in Chapter 2. This is a technical and mathematical introduction intothe stages of the planning process, describing some of the difficulties of dealing with charter airlineplanning compared to regular airlines.

The third part (Chapter 5) of this thesis is dedicated to an analysis made on an observationalbasis depicting the problem areas and where future awareness is recommended. These concernthe set up of key performance indicators, the nature of theseindicators and risk management. Thepurpose of describing the risks of air-traffic planning is tocreate a base for future research intooptimizing the planning process at TUIfly Nordic. This chapter will be followed by conclusionsreached concerning the needs and requirements of TUIfly Nordic (Chapter 6).

1.2 Literature

Literature available in the field of airline optimization isabundant, although the literature canbe said to concentrate towards certain hot spots in the airline industry, such as crew operationsscheduling, as most researchers state that this is the area where most savings can be made in theairline industry, see S Lavoie et al. [1], G. Desaulniers et al. [2]- [3] and N. Kohl et al. [4]. Themajority of literature in this field are mathematically oriented (for further reading, see Chapter 6),and as such do not cover the inner operations of an airline. Information regarding where and whythe planning process for an air-traffic schedule is hard, is non-existent.

1.3. PURPOSE AND PROBLEM DESCRIPTION 3

The reason for this is mainly thatregular airlinesare studied and notcharter airlines, the de-scribed models are based on a fixed traffic schedule, where thedaily operations are often repetitivethrough out the week and year and there is no seasonal structure (summer schedule, winter sched-ule) with changing destinations to take into account. Dailyscheduling, or modeling on a fixed setof data is both simpler and computationally more efficient.

Articles and papers concerning this subject, Barnhart et al. [5] describe the planning process infour hierarchical steps where each step is the input for the next,Schedule planning- flight schedule(based on passenger and travel statistics and demands forecasts)→ Fleet planning(matching theflight segments to specific types of aircraft, according to capacity and flight characteristics e.g. dis-tance)→ Aircraft routing (maintenance operations scheduling in order to meet flight regulationsand demands while assuring the best working conditions for the fleet)→ Crew scheduling(allo-cating tasks according to the flight schedule to the available crew members assuring that labour,operational and governmental regulations are met, this stage is divided into two interconnectedsubproblems the crew rotations scheduling and crew rostering, i.e. rotations planning and individ-ual assignment of each rotation).

Few articles bring insight into the workings of bringing forth an optimal flight schedule. N.Kohl et al. [6], grant some perspective into the disruptions(e.g. crew sickness, bad weather andtechnical problems) that occur during airline operations,mentioning that “. . . airlines have becomemore concerned with developing an optimal flight schedule, allowing little slack to accommodatevariations from the optimal solution”.

1.3 Purpose and problem description

The airline business is one of the hardest industries to produce growth in, the competition fromsurrounding airlines are rough and operative expenses can easily carry away. There is a constantdemand for improvement and the need to turn a profit is key to most airlines survival. Meeting thisdemand is handled individually depending on which company is under scrutiny. However, mostoften, the airline businesses are old fashioned and established in their internal processes, meaningthat introducing changes into the systems and processes in use often becomes a challenge.

Most charter airlines are part of holding companies, where there is often aparent companyholding the majority of shares within the ownership while there can be several smaller airlinesholding minority shares, so calledassociate companies. It is commonplace within such businessmodel that mergers and acquisitions occur on a frequent rate. This is especially noticeable in therapidly changing environment of the airline business. Withmergers of this size come internalchanges (e.g. management, operational structure, financial and software structure) appropriate forthe integration of the acquired company, these changes are requested by the parent company, andnot all of which will be considered agreeable with existing operations. TUIfly Nordic is part of thelarger TUI AG, and was acquired in the year 2000. The operations of the Planning department at


TUIfly Nordic is to a certain extent a consequence of the merger of the company with the GermanTUI AG, such that in order to maintain an interconnecting financial system the German one wasadopted, and following this software came several other that communicate with it.

The Planning department feels that the traffic planning software that is currently in use is notoptimal. Therefore, research was put into possible changesthat could improve the course of theplanning process, such as better software assistance. Highdemands are placed on such a change,such as the need for it to operate stably without contributing to already existing system difficul-ties. These difficulties are mainly caused by the lack of fastand reliable optimization softwares,resulting in manual and time consuming evaluations that unfortunately leave room for mistakes.

This is a wide area to research which is not easy with minimum of resources dedicated for thisproject. Due to time constraints and other constraints due to company considerations, the purposeof this project has been to assess the needs and requirementsto obtain a more optimal air-trafficplanning process for the Planning department. Among these needs defining the problem areas ofoperations today and assessing where support is needed, whether this is with new software or bysome other measure, were looked at. Clarity into this matterwill be provided in the oncomingsections about the planning and systems processes. But firstly a more detailed background will beprovided in the next section.

1.3.1 Background

The main program used throughout the TUI group is a financial database called Airpas. Airpas is aGerman developed and based program that handles on a daily basis the financial information of theflights (e.g. fuel costs, landing taxes, handling costs. Airpas is part of a larger group of programsall directly connected to a larger database called IDPS, which provides airline operations services.These programs all communicate internally with each other,which can create an interconnectivityproblem when external (third party software) programs are introduced. The full extent of this willbe further explained in the systems section 3.

It might seem redundant to introduce an external element into a system that is already con-nected with all programs a charter company can possibly haveuse for, but this assumption wouldhave to take into account that all programs work satisfactorily. Opinions range wide in concerningthis as the views of the users can differ from the developers in terms of the optimality of their prod-uct. But it is the view of this project that a perfect trade offis rarely the case, and that optimumperformance is not met at TUIfly Nordic. Furthermore, if a system does not work satisfactorilywhy not just stop using it?

The answer to this question is both easy and yet hard to obtain. It has been mentioned thatairline companies are well established in their ways. This can be interpreted as an unwillingnessto change old ways of thinking, however although this statement certainly holds a partial truth, itdoes not fully address the issue.


Behind planning air traffic rotations lies long experience on behalf of the people laying thetraffic program and an absence in computer assistance. The easy answer to the previous questionis that a company will be unwilling to disregard an investment. For instance, researching andintroducing a program that performs a requested task is a long and most often expensive process.Several alternatives have to be explored with regards to constraints that are presented by the in-house systems, thus resulting in a cluster of operational software that together operate far fromoptimality.

The difficulties are twofold, one is that because of several software restrictions creating mul-tiple scenarios for a traffic plan is virtually impossible. The main reason for this connected to theabsence of computer assistance as scenarios have to be drawnup by hand and incorporating allnecessary inputs for a feasible air traffic scenario is difficult. Despite of this reason, scenarios arecreated and continuously evaluated by hand taking account amedley of inputs ranging from crewresources to maintenance. Although not highly detailed, these handmade scenarios provide a baseon which the traffic program will later be built upon. This leads to the second cause of concern.

The second reason is that the schedule building software currently in use does not allow for pa-rameter variability nor does it produce reports needed to evaluate or compare planning parameters.These parameters,(described in Chapter 2) provide the necessary information needed to decideupon an optimal traffic plan.

The lack of software causes a domino effect throughout the planning process. Starting with thefact that laying down a complete traffic program is a very timeconsuming process. Bear in mindthat only one schedule is being created at todays pace, several handmade scenarios are createdand re-created, evaluated and discussed, where the outcomeis oneflyable and rather satisfactoryschedule. Furthermore, there is no delimitation that marksthe completion of the traffic schedule.Therefore, handing over of the traffic schedule for construction of rotations for flight deck andcabin crew, a process calledcrew pairing, takes place at a rather late stage.

An assortment of changes, based often on requests from the Tour Operator, is being made atall times before, up until and during the execution of the schedule. Of course the traffic programhas to be flexible so that at any stage if a need for change should arise it can always be solved oraccommodated for (as long as it yields a benefit), but today’smethods do not allow for an easy andsystematical evaluation of the impacts of a certain alteration. A bottleneck problem is thereforecreated because of the lack of quick handling and evaluationtools.

1.3.2 Limitations and demands

There are limiting factors, and several requirements when it comes to introducing a new systeminto an already complicated compilation of existing systems. Difficulties include communicationwith existing systems without causing compatibility problems, training of personal to operate thenew software and budget.

1.4. TUI AG GROUP 6

Incorporating a system can have widespread effects throughseveral departments of a company.The influences vary in complexity, ranging from amount of personal that is affected by the changeto the amount of training required to operate the new system.Level of training depends on thenumber of personnel which will be directly, and perhaps alsoindirectly, involved with usage of theprogram, therefore the program should preferably be user friendly.

Commonly, there are several budgetary limitations to introducing a new system. Dependingon the extent of usability derived from a multi applicational program (air-traffic scheduling, crewscheduling), the budget allowed for implementation might vary. For instance, if the initiation,licensing fees and training costs are sufficiently small then the cost might be abided by a depart-mental budget. If the costs are larger than requisite budgetthen the purchase might need Boardapproval. Limitations of economic nature is one of greatestimportance when analyzing potentialsoftware, as costs have to be balanced with potential savings. Whether the costs of introducing anew software are large or small, the return on this investment is regarded to be more important inthe long run above the break-even point. Future profitability and an improvement is consideredand it is assessed whether it is a worthy investment or not.

1.4 TUI AG Group

TUI AG (German:Touristik Union International) was created in 1968, and back then TUI was anassociation of medium sized travel and tourism companies. During the nineties the company rein-vented itself to expand from tourism to incorporate shipping and logistics, by acquiring the miningindustry Preussag AG and transportation company Hapag-Lloyd AG groups, in 1998. Hapag-LloydAG later Hapag Touristik Union (HTU) was renamed and became TUI Group in 2000, which op-erated as the soul subsidiary of Preussag AG. During the early twenty-first century, the companyreorganized by selling off many of its industrial branches and purchasing several major travel andtransportation firms, and in 2002 the company was renamed from Preussag AG to TUI AG.

Today TUI AG is one of the worlds largest tourist firms with interests across Europe, owningtravel agencies, hotels, airlines, cruise ships and retailstores. Subsidiaries include TUI AG Air-lines, the largest holiday fleet in Europe. Its common brand TUIfly encompasses 7 airlines, amongthose TUIfly Nordic which will discussed in the next section (1.4.1).

1.4.1 TUIfly Nordic

TUIfly Nordic is a charter airline based in Sweden, operatingholiday flights from airports in Swe-den, Denmark, Norway and Finland. The airline originates from Transwede Airways AB, anairline founded in 1985 by Thomas Johansson. The charter division was acquired by Swedish touroperator Fritidsresor in 1996 and renamed Blue Scandinavia. Britannia Airways took control ofthe company when Fritidsresor was acquired by Thomson (a UK based travel operator part of

1.4. TUI AG GROUP 7

the larger International Thomson Organization of Canadianuntil 2000) in 1998. The airline wasrenamed Britannia AB and later Britannia Nordic.

Figure 1.1 The TUI smiley, appears in all airline logos. Image courtesyof Google.

Preussag AG (later TUI AG) acquired the Thomson group in 2000. Because of a new marketingstrategy put forward by the TUI Group the subsidiary airlines were to add a “-fly” suffix to theircompany name. Due to this strategy in November 2005, the airline was rebranded as Thomsonflyand in May 2006 it became TUIfly Nordic. The TUI smiley, appearing on all airline and TourOperator logos, is illustrated in figure 1.1.

Aircraft In fleet Seats Notes

Boeing 737-800 5 189 All fitted with winglets.Boeing 757-200 3 (1 leased) 235 All fitted with winglets.Boeing 767-300ER 3 (2 leased) 291 (328 during summer) All fitted with winglets.Boeing 747-400 (1 leased) 582 All fitted with winglets.

Total 11 Last updated: August 30, 2011

Table 1.1Current fleet at TUIfly Nordic.

TUIfly Nordic fleet can be seen in table 1.1, currently the average age of the fleet is 13 years.During the winter of 2010, TUIfly Nordic leased one Boeing 757-200 and two Boeing 767-300ERaircraft from Thomsonfly for various charter routes. One Boeing 747-400 is leased from Corsairfor long haul flights during the winter season 2010/2011.

A note for further reading, there is a distinction made between TUI Nordic and TUIfly Nordic.While TUI Nordic represents the entire Scandinavian department of the TUI Group AG (that in-cludes all Tour Operators and the airline), TUIfly Nordic is the airline, and is also sometimesreferred to asBLX the ICAO code (International Civil Aviation Organization).

1.4.2 Tour operator

TheTour Operatorfor TUIfly Nordic are Fritidsresor in Sweden, Finnmatkat in Finland and StarTour in both Denmark and Norway. The Tour operator, referredto as T/O handles flight planning(popular destinations for the coming year, hotel capacities, amount of beds available for eachdestination) for TUIfly Nordic and other contracted airlines. The basic flight plan is establishedusing historical data, based on sales statistics. The process that follows takes into account customer

1.5. COST STRUCTURE FOR TUIFLY NORDIC 8

wishes for extended travel opportunities to several locations. A dialog is maintained between theT/O and travel partners about market changes, so that shiftsof the internal fleet are for instancemade to adapt to increases by redirecting flights from where demand is lower.

1.5 Cost structure for TUIfly Nordic

To understand where the stakes are when it comes to improvingtraffic planning, the cost structurehas to be studied. The division of costs for both long and short haul flights can be observedin figure 1.2. The corresponding values are taken from 2011/2012 winter season pricing (addreference here).

Figure 1.2 Cost structure comparison between short and long haul flights. Illustration: MiriamDanielsson at TUIfly Nordic, reformatted by Mercedes Inal.

It can be seen that fuel and crew related costs are the two maincomponents of the cost struc-ture, followed by outgoings such as leasing of aircraft and maintenance. It can be noted that thedistributional variations in costs between long and short haul are minor. Cost concerning naviga-tion, which are determined by the airspace flown over and distance, differ mostly for short haulflights compared to long haul. Onboard sales are incomes and thus marked negative (these showan increase on the short haul as well).

Maintenance costs don’t vary significantly, but the small difference can be explained by reg-ular short haul flights gathering more flight legs (i.e. Stockholm to Las Palmas is considered oneleg, the return trip is also one leg, the combination is arotation) and block hours, thus demand-ing more frequent maintenance checks (this will be explained further in Chapter 2, section 2.6.1Maintenance).

As mentioned previously fuel and crew related costs dominate the budget, the magnitude ofthese are predominantly unavoidable. Fuel unit costs are toan extent fixed or more precisely

1.5. COST STRUCTURE FOR TUIFLY NORDIC 9

regulated by long and short term agreements with the provider. Crew costs however depend onseveral factors, some of which are direct (DOC-Direct operational costs) and others which are notdirectly linked to the operations features and are thus saidto be indirect (IOC-Indirect operationalcosts). More on this will follow in the pricing section.

When consideration is given to several parameters at the beginning of the traffic planning pro-cess, there might be a considerable increase to gain economically. Planning for several scenarios,and being able to review the economical differences that canoccur, can reveal at an early stagewhere costs become unmanageable. This might also show whererevenue can be increased byproducing more efficient rotations, should the schedule allow for feasible connections.

Minor improvement in the traffic schedule can lead to significant crew cost reductions. Crewcosts can be measured by so calledproduction days, which simply implies days where productionis carried out (this is also an estimate for crew supply and demands, for further reading section 2.8).Optimization of the air-traffic schedule in combination with crew scheduling can result in reduc-tions of production days where possible, the outcome being savings in superfluous costs caused byundesirable scheduled rotations. This can be achieved partly by automatizing some of the manuallabour that is the process today and by creating a more efficient evaluation process.

Departure Arrival

ARN Time HKT Time ARN Time HKT Time Flight leg Pilots Cabin Rest days

15.30 21.30 02.30 08.30 ARN-HKT 2 8 302.30 08.30 14.30 20.30 HKT-ARN 2 8 319.30 06.30 06.30 12.30 ARN-HKT 3 8 308.30 14.30 20.30 02.30 HKT-ARN 2 8 3

Table 1.2Example of rotations with various crew demands.

Table 1.2 shows an example of arrival and departure times from Arlanda, Sweden (ARN) to Phuket,Thailand (HKT). Since aircraft are not grounded for duration of the required rest period for thecrew, a crew is assumed in place to cover the return journey inthis example (this is most oftenthe case, but deviations occur and flying in crew for the return journey is needed). There areseveral limitations for flight time, for instance dependingon departure time there are rules thatdictate the amount of available pilots for one flight. In the case of the example in table 1.2, threepilots are required for departures after 5pm, for the long haul flight. There are situations whenextra production days and thus costs are generated, due to certain required rules and regulations,because of the layout of the air-traffic schedule.

2

Planning process

There are several steps over which the air traffic schedule isplanned and brought into production.An overview of this process, as it is carried out today at the main office of TUIfly Nordic, can beseen in figure 2.1. This process starts for each season approximately a year and a half previous tocarry out date and carries on until execution of the traffic program (without necessarily involvingall of the functions, each seen in the flow chart). Several seasons are therefore constructed simul-taneously resulting in an overlapping process. The focus ofthis project will be on how to optimizelong term traffic planning and the factors that contribute toits taking shape.

Figure 2.1 Current planning process at TUIfly Nordic head office. Illustration: Mercedes Inal.

Figure 2.1 shows the traffic schedule for a season, taking shape from an early on and continu-

10

2.1. TIMEFRAME AND INVOLVED PARTIES 11

ous fleet planning stage, with constant interactions and assessments from resource planning andbusiness control. This is followed by the planning of crew rotation schedules and pricing of theseasons schedule, which is presented to the Tour Operator for decision and sales. How these stagesare implemented and influenced will be discussed in the coming sections, starting with a processtimeframe and an overview of all involved parties.

2.1 Timeframe and involved parties

The timeframe for a typical winter season planning process can be seen in figure 2.2. The com-plexities of planning an operational air-traffic program can be assessed from this flowchart. Ontop of the input variables that directly affect scheduling,which will be discussed in the long andmedium term traffic planning section, there are decision factors along the way influenced by theseveral departments that provide their own input into the layout of the traffic plan. There is a con-stant process of analysis, evaluation, change and then re-evaluation taking place, which is mainlymarket driven.

Figure 2.2 Planning process timeline for a typical winter season. Thisshows at which pointeach department gets involved throughout the creation of the traffic schedule to its production.Illustration: Alexander Huber.

As seen in figure 2.2, the process from development to production is covered in five sequential

2.2. LONG TERM FLEET PLAN 12

steps. A concept program is prepared by the T/O for the comingseason and uploaded into theflight database, FDB. The flight database is an internal system used through out TUIfly Nordic,it contains past statistics and capacities for available departure and travel destinations. This isa historically set program, operating with constantly updated information. Information such asallotments, airlines (external capacity), shared fleets and cost reference data that supplies standardcosts to the customer. The flight database also relays information about the seat sales (volume)which are important, for instance when ordering hotel rooms.

Before this concept program there is a long term fleet planning stage, which will be describedalong with each step in the next few sections.

2.2 Long term fleet plan

The fleet planning stage is a continuous long term process partially due to the fact that it is boundto the leasing contracts of the airplane fleet or delivery direct from the factory. These contracts arenegotiated for over a long period of time, that stretch from anywhere between 7 to 12 years. The

Figure 2.3 Airplanes in production during 2010 and 2011. Illustration: Mercedes Inal.

main purpose of fleet planning is to provide the necessary fleet capacity in order to accommodatethe tour operators needs throughout the year. TUIfly Nordic provides an airplane fleet that coversup to 60% of what the tour operator sells. The rest is hired from external companies to supplydemand.

2.2. LONG TERM FLEET PLAN 13

The main reason for this is because expansion with profit in the airline industry is hard toachieve, having a large airplane fleet can be unsustainable should the economic climate proveharsh. This is of course due to the fact that an aircraft is by no means a small expense and further-more a grounded aircraft is a colossal expense, resulting ina massive loss of revenue. Since thisis an unattractive option, long term planning and external seasonal capacity purchasing becomenecessary to ensure that all aircraft at hand are used with maximum efficiency.

This process holds a lot of interest, since it affects all major areas of air-traffic planning. Tocarry out a need presented by the tour operator a certain number of aircraft are required. A decisionis made about either an expansion or a reduction of the fleet isrequired, varying from one season toanother, this in turn decides the layout of the traffic program over the aircraft rotations. Figure 2.3shows the fleet as it has been throughout year 2010 and how it will be in the year 2011. Togenerate the seat production seen in the graph, data on amount of legs flown per aircraft (see table 1,Appendix A) is used, multiplied by seat numbers given in table 1.1, calculations also assume thatthe aircraft fly with maximum take-off weight (MTOW, maximum fuel capacity, passengers andcargo). Flight legs are derived from information sheets acquired from a given air-traffic schedule.

Figure 2.4 Fleet plan process. Illustration: Mercedes Inal followingset up by Per Sylvan atTUIfly Nordic.

Fleet and capacity planning is a structured process; figure 2.4 shows how the fleet planning processis carried out currently. Following the example above when aneed is presented, a principal decisionis made by the Tour Operator, TUIfly Nordic (BLX in the flowchart) and the airline management.This decision is first discussed in TUIgether, a group consisting of BLX key functions and the T/O.Decisions here are approved or declined by the management and those in charge of product and

2.3. PRODUCT AND CONTENT 14

content. An executive group (LTM) will review the decision proposed and if favorable, negotiationswith unions will take place before a final decision is reachedand an investment committee basedin the United Kingdom is involved. If and when the investmentcommittee sign their approval, thematter reached the TUI Travel PLC board (PLC is an abbreviation forpublic limited company) andthe matter is processed further.

Aircraft sourcing is a long process going back and forth several times before reaching a deci-sion appropriate for all concerns. In the mean time negotiations with a lessor market, or the TUITravel order pool concerning which options to be chosen is undergone, and provided that inter-est still resides with the attributors throughout the internal process, a letter of intent is drawn upand presented to the TUI Travel PLC board, which will, if decided favorable, lead to a contract.Aircraft are also redistributed between the airlines in theTUI Group, the process of acquiring anaircraft this way is similar to the process described above.

2.3 Product and content

TUI Nordic is a charter airline, and like most charter airlines their holidays contain a lot moreplanned activities compared to regular flights. The planning of all activities that make up theentire holiday experience (e.g. the holiday itself, hotels, activities at resorts, pick ups to and fromdestination etc.) is done by the product and content group.

2.4 TUIgether

TUIgether is an internal group within TUI Nordic, with representatives from all areas of TUINordic including the Tour Operator and the airline TUIfly Nordic, gathering on a weekly basis.The discussions that go on here are changes to the long and short term air-traffic program andchanges of commercial nature, for example whether or not incorporating a new destination shouldbe considered.

2.5 Planning and production

Planning and production refers to a meeting held every otherweek, involving those that are affectedby the flight schedule on an operative basis. The main purposefor the group is of an evaluationaland informative nature. This meeting is held for the operative side, handling ground operations,cabin crew and maintenance. The group discusses the air-traffic program as it is built up and findsolutions to problems that arise. For example, if the TUIgether group proposes a new destination

2.6. LONG AND MEDIUM TERM TRAFFIC PLANNING 15

or a change to the program, will there be enough time to reach maintenance requirements in the air-traffic schedule? This is presented to the group and possibleways of going about the issue will beregarded and if the result of this groups evaluation is favorable then the maintenance requirementwill be accommodated for.

2.6 Long and medium term traffic planning

When the long term fleet plan has been decided for a given season, the long term traffic programcan start taking shape. With requests from fleet planning andthe tour operator, expansion of theairplane fleet and additions to the destinations list, the traffic program is planned in several stages.It starts with a rough sketch that outlines several possibilities of managing the air traffic programwithin legal and operational boundaries as well as cost efficiency. This is a collaboration, andin large parts a compromise, between several departments whose respective inputs into the trafficplanning result in a flyable air-traffic program for the season. These inputs are both external andinternal and can be seen in figure 2.5.

Figure 2.5 Various inputs that determine the optimality of a traffic program. Illustration: Mer-cedes Inal.

The planning process begins approximately 18 months previous to the season of interest e.g. sum-mer or winter. Several simple scenarios are put together andevaluated without computer assis-tance. A scenario will consist of approximately two weeks, since it is both reasonably short periodto overlook and also because two weeks cover the regular amount of vacation time chosen by trav-elers. The choice of period for a scenario is based on the seasonal peak, this is necessary since thework capacity needed is planned to accommodate for this period. For example this peek period canbe seen in figure 2.3, between february and april 2011, where there are 11 aircraft in production.Therefore the resources available have to satisfy the need for this period.

A scenario takes into account inputs from all involved parties. These are discussed and rear-ranged as to grant each input the least amount of inconvenience. This will allow for an opera-


tionally stable traffic program. What is difficult at the longterm planning stage is the amount ofinputs that need to be taken into account. Combining all of the inputs into an optimal traffic pro-gram that will provide operational stability and flexibility is challenging. These parameters have atendency to clash with one-another whenever change is required. Therefore, representatives fromall parts associated with these inputs come together at TUIgether (section 2.4) meetings to discussthe traffic program.

Air-traffic planning is done by few people (as such it is a group effort) where inputs are gatheredfrom all involved parties, however the actual scheduling isdone by less than a handful of people.At TUIfly Nordic these people (from here on referred to asPlanners) work with programs that arenot designed to optimize several inputs into the best flyableschedule. Therefore laying the body ofthe air-traffic schedule falls on the Planners, with their combined years of experience and abilitiesto see scheduling possibilities, in order to obtain a feasible schedule.

Creating a flyable schedule does not only cover the rotationsof the fleet, there are several otherfactors, the most important of which are crew rotations (also calledpairings) and maintenanceopportunities. Finding a middle ground for these factors makes an air-traffic program viable. Ifcrew pairings do not hold, then the fleet rotations would not hold, for the simple reason that if thereis nobody to operate the aircraft then it can not possibly fly.This means that while planning theaircraft rotations, the Planners have to take into account the rules and limitations for crew flightand duty times.

While crew conditions must be taken into account in order to obtain a flyable schedule,main-tenance schedulingis one of the most common optimization parameters that is sought to com-bine with air-traffic rotations. The importance of maintenance will be covered in the next section.Briefly however, while planning aircraft rotations it is very important to take into account the main-tenance needed for each aircraft individual (maintenance is needed between flights, short checksas well as long checks) and therefore plan arrival and take off times accordingly. Longer mainte-nance checks need to be coordinated with the technicians in charge of maintenance in order to planan optimal schedule, swap opportunities need to be planned in for the aircraft so each and everyaircraft in the fleet rotate to home maintenance base (hangar).

There have been several studies to scheduling aircraft rotations and many combine mainte-nance and crew pairings planning in some form into the basic fleet planning model to obtain abetter overall optimization. L. W. Clarke et al [7] try to provide modeling devices to achieve an in-corporation of maintenance constraints and crew scheduling into the fleet assignment model (FAMfor short, is a model that optimizes the amount of aircraft ina fleet to a network of flights underseveral constraints, see Chapter 4) while retaining its solvability.

2.6.1 Maintenance

Safety is a crucial issue in the airline service, the objective being to keep the aircraft flying for asmuch of its service time as possible in order to accumulate asmuch revenue as possible without


compromising customer safety. With small margins and huge fixed costs (e.g. an aircraft) anyincident would jeopardize the survival of airlines, especially the small ones.

Maintenance is therefore an important parameter, aircrafts are heavy duty machines that do notregularly come of the construction line, since they are often leased over a period of time they areused and need regular service checks in order to fulfill safety requirements. Each aircraft modelhas its own maintenance program and its own range of intervals where this must be performed.These intervals can range from anywhere between five to sevenweeks, and this only takes accountthe major maintenance checks. It is unsustainable to have the checks so far apart from both main-tenance and traffic planning perspective. Maintenance checks are therefore planned as loops thatare woven into the traffic schedule.

Maintenance duty intervals depend on flight hours, cycles orcalendar time, or what is morecommon, a combination of these. Since these parameters depend on the flight schedule and useof aircraft, the general rule is that maintenance need is decided by which ever limit for these pa-rameters is reached first. This is clearly stated in the manuals that accompany each aircraft model.Information regarding maintenance was gathered by consulting the maintenance technicians andalso via access to maintenance manuals provided by Boeing [8] and internal company guidebooksfor maintenance handling [9].

The maintenance team at TUIfly Nordic performs mainly two types of checks, A and C, thesewill be described shortly. The A- and C-checks have a recommended interval, within which thecheck is meant to be carried out. The intervals of these checks might seem large and thereforeset fairly far apart, so naturally several smaller checks are performed along the way, for instance,pre-flight inspections, 48 hour service checks and 100 hour service checks.

A-checks

A-checks are performed, depending on aircraft model, every500-750 flight hours. Thesechecks are specified as a routine check to ascertain the general condition and serviceabilityof the airplane. It is essentially a visual inspection of theexterior structure and flight controlsurfaces and some internal areas. Therefore the requirements of this check include that flapsand spoilers being extended and cargo, landing gear, passenger, avionics and engine cowldoors be opened. Tasks needed to be performed can be counted to the hundreds, rangingfrom changing filters and lubricating bolts to operational and function checks.

A-checks are divided into groups, all of which are performedat defined intervals. Themaintenance team for TUIfly Nordic solves these with regularnumeration of A-checks, (anexample will be given. For the Boeing 767-300 an 1A-check is performed every 750 flighthours, when the aircraft has collected twice the amount of flight hours a 2A check will beconducted, and so on. This has a more specific interval described in Appendix A.)


C-checks

C-checks are performed, also depending on aircraft model approximately every 6000 flighthours, there are therefore extensive and long checks performed over several weeks. Thecheck consists of a complete and detailed area check of all interior/exterior zones of the air-plane including systems, installations, visible adjacentstructure and performance of specialservice items. The full extent of the C-check is to be performed without disturbing systemintegrity, removal of components or insulation unless removal is performed with plannedinterval.

It is not hard to understand why planning and performing regular maintenance checks are of suchimportance. It is however harder to grasp the complexity of planning each maintenance stop opti-mally without disturbing a well planned flight schedule. Itseasier to state that a traffic plan is notoptimally planned without proper maintenance stops. Its therefore a crucial parameter to take intoaccount from the very beginning

Airlines try to maintain a homogenous airplane fleet choosing to have several planes of thesame line of model and preferably also the same type. The typeof model decides seat numbersand arrangement of interior. The reason for this is so that the aircraft are easily interchangeableshould the need arise without impacting crew efficiency. Various aircraft types require differenttraining needs for flight deck and cabin crew.

Swaps and tail assignment

The home maintenance base for TUIfly Nordic is in Stockholm, Arlanda, which means itsaircraft have to circle home occasionally. To achieve this,aircraft swaps are planned atcertain intervals and airports, allowing one aircraft to continue the flight leg and the otherone to travel back to the home base for a planned service check. When and where to place aswap, just as the assignment of the individual aircraft to a specific flight segment, so calledtail assignment, is decided by when maintenance is due for the aircraft. For TUIfly Nordicthe accumulated flight hours far succeed the amount of cyclesand/or calendar dates for whichan aircraft maintenance check is needed. The maintenance team therefore decides the swapsand aircraft assignments based on this parameter.

The maintenance team at TUIfly decides the tail assigning of the aircrafts, and relays itto Germany where the operations center is based. For the timebeing the operations team inGermany operates the program for tail assignment (Chapter 3will elaborate further on whythe tail assignment is not directly handled by the maintenance team). Although acquisitionof the software is currently in progress.


2.6.2 Slots and traffic rights

One of the major factors when planning an air traffic scheduleare slots. Slots are airport-baseddefined timeframes where landings and take-offs are permitted. These are related to the capacity ofthe airport, and thus for each airport certain amount of slots are negotiated for to enable arrival anddeparture for the holiday destinations as well as in Scandinavia at departure/arrival. Availabilityof slots depend on size and location of the airports, i.e. amount of gates, runways (the length ofthe runway can also be a factor, depending on size of aircraftand necessary minimum take-offdistance) and transport facilities.

Slots retain a high market value and are considered businessassets. Arrival and departure timesof a designated slot can have great impact on ticket sales. Clearly owning slots on popular timesof the day will attract more travelers and thus produce more profit. Owning attractive slots alsoprovides a tradable asset for the airline and a competitive advantage against competing airlines.

Slots are also ahigh risk parameter, desired slots are something most airlines have to negotiatefor. Slots are held by the “grandfather clause”, i.e. slots remain in the possession of an airlineindefinitely provided annual usage. Slot conferences take place twice a year, one conference foreach season. Slot conferences are held at a relatively late stage. By this time, the traffic schedulefor the season in question has been, for most part, released,priced and open for ticket sales via thetour operator, therefore it implies arisk factor.

While planning the traffic layout, historical data from earlier seasons play a large role in howto plan for the upcoming season. This data retains a record ofall the slots that are opened for theairline at each destination while also providing a staring point on how to build the traffic program.

2.6.3 Resource planning

Parallel to the initiation of a new long term traffic plan, theresources needed for the season arecalculated by the Planning control function. This a collaboration between those building the longterm traffic schedule and crew rotations and those handling finance. Because of the mutable natureof the long haul flights, this process will at an early stage help build and reshape the traffic programby evaluating simple scenarios that are for the most part prepared initially on paper.

Usually scenarios are built covering a two week period, thisis a reasonable time period thattakes into account all the aircraft rotations that occur before a period repeats itself.MS Excel

based tools are available for evaluating whether or not a traffic program is viable. For instance,these tools can help decide whether a rotation on a flight leg is within legal parameters from a crewperspective, which is something that is discussed long before the schedule even reaches the stagewhere crew rotations are planned.

Figure 2.6 depicts the step-by-step process describing thestages of resource dimensioning. Itis easy to see that resource planning is influenced by severalfactors, some of which are directly


Figure 2.6 Step-by-step flow chart for the resource calculations process. Illustration: ClementBerguerand.

manageable and some of which can clearly vary, such as sick-leaves for crew though statistics canhelp for dimensioning. Dimensioning of standby crew is of great importance, since a sick crewmember needs to be temporarily filled in for, meaning this will affect the available crew resourceswhich will also affect costs. Figure 2.7 shows a timeline of the resource planning process for theflight deck (pilots).

After the first stage of scenario building and rough resourcecalculating while planning thetraffic schedule, the needs and requirements for a season arelaid out and planned in two stages. Inthe first stage, available resources i.e. cabin crew and pilots, are evaluated in order to determinewhether action should be taken (i.e. if reduction or recruitment is necessary) or not. Pilot biddingwill take place at this stage, bidding concerning allocation of base, vacation days, according toseniority etc. Following this is recognizing training needs, whether pilots and cabin crew need tobe trained for a new airplane model or if new requirements need to be taken into account.

2.7. HANDLING 21

The second stage is implementing the first one in an optimal way, such as scheduling re-traininginto the transition periods (shoulderperiods) between seasons where a decline in production oc-curs. As is shown in figure 2.3.

Figure 2.7 Example of a current timeline for flight deck planning. Illustration: ClementBerguerand

There is a trade-off between robustness and flexibility whenplanning for an air-traffic schedule. Itis preferable of course that both are obtained, but this is rarely the case. To allocate or distributeresources in an efficient way, it is necessary to have the air-traffic schedule ready and the crewrotation schedules planned. This estimates that take more information into account are possible,and thus a more flexible and robust schedule is reached.

2.7 Handling

All activities that occur on ground, that is to say activities that take place at the airport from themoment of touch down to take off, are gathered and categorized ashandling. These activitiesinclude passenger check in, boarding at gate, luggage loading, refueling, deicing, cleaning andcatering of the aircraft. Much more can be said about the logistics that go into planning groundhandling, but this will not be covered here, though it is a dimensioning parameter.

Speed and precision in the sequence of handling tasks performed is key to an efficient andsmooth rotation. Handling times need to be taken into account when planning aircraft rotations

2.8. CREW PAIRING 22

as the arrival and departure times at a destination (also called turn-around time) can’t be plannedneither to far apart, because of loss in efficiency, nor to short, to allow that everything that needs tobe performed on ground is done properly.

Handling is rather specific for charter flights, compared to regular flights. While turn-aroundtimes for regular flights can range from 20−45 min, charter flights will demand a lot more, herethe turn-around times, depending on aircraft type, begin at70 min. This is mainly due to what isoffered in terms of charter service (amount of seats offered, pre-packing of tax-free items etc.) andthe amount of passengers to board.

Large amounts of people produce more waste, and bring on morerevenue as they make useof the tax-free havens offered on board flights, as such they are provided withpre-packs(mer-chandize, bought onboard or pre-ordered, that are distributed on the journey). Everything needs tobe offloaded and fresh supplies need to be reloaded, which requires time. Cleaning and cateringpersonal need time to prepare the aircraft for the next groupof passengers on route to their holidaydestinations.

Charter airlines utilize the aircraft at their disposal "tothe limit". Handling times are pushed tothe minimum today, and it is ground handling that suffers when an air traffic schedule is interruptedby delays, some large enough to make that minimum turn-around time window look very small.The effects of this delay will undoubtedly push cleaning andcrew to perform their duties in a muchless time. Pushing tight time limits like this often resultsin the flight being delayed, while alsocreating a snowball effect of delays. The consequences are often dealt with very fast, but if a flightis sufficiently delayed, the need might arise for a substitution charter to be hired, resulting in highexpenses, though the cheapest recovery alternative is always chosen.

As such, the importance of handling in the long term traffic planning stage is crucial. Planningfor the right turn around times at an early stage is complicated since it greatly impacts the efficiencyand robustness of the flight schedule. (The ability to counteract an arising disturbance in thehandling times could have a massive effect economically.)

Today, TUIfly Nordic handles the ground work by hiring third party suppliers, meaning thereis no in-house catering business or cleaning crew at each airport. It is common practice that workis contracted and dealt with remotely, where quality is determined from what is demanded in anagreement and the costs of such.

Section 2.10 will explain the financial sides of the planningprocess and why some of thesecosts are fixed while others are variable.

2.8 Crew Pairing

Optimization of crew pairings is frequently covered in scientific literature, given that significantreductions can be generated solving the crew pairing problem optimally. Crew pairing costs are


only exceeded by fixed aircraft costs and fuel costs. G. Desaulniers et al [2] state that “The magni-tude of these savings for major airlines is exemplified by thefact that a one percent decrease in thetotal crew costs often amount to tens of million [sic] of dollars per year in additional profit”. Thisis statement is also true for smaller airlines but with a smaller magnitude in profits. G. Desaulnierset al describe an implementation of a new solution method forthe crew pairing problem applied todata from Air France. This is an improved method from S. Lavoie et al [1] from 1988.

When creating a feasible traffic schedule, crew rotation feasibility is essential. Since there arenearly endless constraints for how the crew are allowed to work, these constraints will undoubtedlyoppose the ones set for the aircraft rotations. This means that to an extent when scheduling the air-craft rotations, the crew constraints must be met in order toachieve a flyable schedule. Accountingfor crew limitations early on when planning aircraft rotations simplifies the work of creating feasi-ble crew pairings when handed over to the pairing stage of theplanning process (see figure 2.2 onpage 11).

Planning the crew rotations is a process that takes place along side the long term traffic planningprocess. The reason for this is that traffic planning today isat large ruled by experience rather thanoptimizing mathematics. The experience yielded by the planner can incorporate far more variablesthan what would be deemed necessary when only planning aircraft rotations. Figure 2.8 showssome of the input parameters that are taken into account whencreating viable crew pairings. Mostof these parameters are difficult to evaluate using a simple mathematical algorithm (although someattempts have been made and are covered in Chapter 4), they are also much for this reason hard toprogram into a good software product.

Figure 2.8 Parameters affecting in the crew pairing process. Illustration: Mercedes Inal.

When the long term traffic plan is almost ready, it is handed over for crew pairing constructions.One of the key parameters in optimizing crew pairings is the number ofproduction daysproducedby a rotation or a duty period. Production days are as it implies, work/duty days, days which


generate expenses (as well as revenues). The optimization problem that is created is how to keepthe production days to a minimum while maximizing profit.

There are of course constraints to this statement. It is not always possible to minimize pro-duction days without suffering expenses. As will be discussed in the next section there are severallegal obligations to the crew and the work limits set for them, for example after a long haul flightor a 12 hour duty period it is required that the crew have threeconsecutive nights of rest. Thiscreates costs for the airline. But these three rest days are alegal requirement and are therefore seenas a required expense.

The difficulty becomes how to create rotations that are always within the legal constraints whenaircraft rotations are already heavily constrained by for instance defined arrival and departure times(slots). Returning to the example, when flights to a long hauldestination, with many timezonecrossings, is set at twice a week, certain gaps can occur. Thecrew coming with the flight, on thefirst slot of the week, can not fly the flight back home since theyrequire a rest period, the dutyperiod becoming too long. The solution is that the crew staysand switches with the next crewcoming in with the second arrival of the week, and man the aircraft on its return journey, (implyingthat the return journey has to be planned three nights ahead). Best case scenario states that thisexample is possible without redundant costs.

If the example is extended, the slots for this destination are set on a Monday and another oneon a Friday, this creates a gap of four nights between flight inand flight out for the crew arrivingat the destination on the Monday and three nights for the crewarriving at home on Tuesday. Thismeans one extra night for which the crew must be accommodatedfor. This also results in anincrease of cost for the additional production day. The mostcost effective solution will be sought,it could mean flying the crew home passively and sending one back to the destination should it benecessary.

The crew pairing process at TUIfly Nordic operates currentlywithout an automated (in-buildsoftware) optimality function, thus the pairings are created using experience and patience. Thesystem supports legality constraints warning for illegal pairings, but other than that the process issimilar to the traffic planning. Figure 2.9 shows the pairingprocess, and the stages of negotiationsalong the way.

Figure 2.9 Pairing process. Illustration: Mercedes Inal.

Crew pairing scenarios lead back to the planning controllerand the traffic planners to evaluate

2.9. DUTY LIMITATIONS 25

the need for an increase or a decrease in resources, the need to coordinate training programs etc.and also to make changes in the traffic program whenever possible to minimize production dayswithout for instance compromising maintenance stops. Whenthis is finalized and a scenario isdecided upon, a full season crew pairing schedule is made, which is on a regular basis validatedand discussed with unions (and every 28 days forwarded to thecrew rostering (individual crewassignment according to pairings) team for scheduling).

2.9 Duty limitations

For safety reasons, the ICAO (International Civil Aviation Organization) restricts flight and dutytime of the crew and requires crew rest periods. Additions tothese rules are given by theEuropeanAviation Safety Agency(EU OPS 1 SUBPART Q) and at a national level for Swedish airlines(TUIfly Nordic) from the Swedish Civil Aviations Administration(since TUIfly Nordic owns aswedish AOCAircraft Operators Certificate. The hierarchy can be seen in figure 2.10.

Figure 2.10 Rules and regulations hierarchy. Illustration: Mercedes Inal.

Breaking international and/or national rules could resultin the loss of the airlines license, andare therefore reinforced by internal policies and guidelines when planning an air-traffic schedule.Additions to these are regulations and contracts/agreements regarding crew operations, limits fortheir work periods, set by both the airline and the unions forboth pilots and cabin crew.

2.10. PRICING 26

Flight Time Limitations(FTL), are important parameters for traffic planning, both of whichneed to be monitored, for evaluative purposes and for regulation as some limits leave room for in-terpretation. For example limitations are yearly, monthlyand weekly flight time limits, guaranteedminimum rest to prevent daily and cumulative fatigue.

2.10 Pricing

Individual costs concerning individual inputs and sometimes combinations of these are evaluatedfrequently throughout the long term traffic planning process. These individual costs refer to main-tenance expenses, airplane leases, catering and crew expenses among many other. Before theschedule is released it undergoes apricing process. Pricing is a financial evaluation process thatsummarizes all expenses for a seasonal air-traffic scheduleand for charter airlines is a processending when the price-list is sent to the T/O. Although a flight schedule is expensive it will still beflown if the schedule is regarded to be the best outcome for itsseason, pricing is important to thePlanning department as it must cover the total costs for eachflight leg.

The cost structure section 1.5, explained earlier that the three largest expenses are fuel, crewand maintenance. The pricing process can be separated into three parts respectively, that coverthese types of expenses. The terminology used will be explained to start with. There are two typesof expenses to be dealt with in the financial realm and these areDirect Operational Costs, so calledDOC’s, andIndirect Operational Costs, so calledIOC’s.

Direct operational costs can be considered as costs connected directly to a flight segment, e.g.costs by annual agreements; fuel, handling, landing, overflights, passenger taxes, maintenance,catering, crew DOC’s (overnight stays at hotels, allowances) other DOC’s (e.g. deicing for air-crafts) and positioning flights.

Indirect operational costs are expenses that are not directly connected with the actual flight ofone aircraft, they exist independent of flights flown, e.g. salaries for flight deck and cabin crew,salaries for other operational staff, leasing costs and including insurance fees.

Business control

Crew costs occupy a bit more than a fourth of the total expenses, as seen earlier in figure 1.2, andthus are budgeted as a separate entity that is later incorporated into the total pricing.

Creating a pricing for crew expenses is a collaboration between the resource planner and thebusiness controller managing crew outgoings, such as salaries, hotel accommodations and al-lowances. Crew costs are also divided into IOC’s and DOC’s. These costs are calculated fromthe from the amount of resources needed according to the new pairings build from a planned sea-son flight schedule. If resources available fall short of demand then hiring of additional crew will

2.10. PRICING 27

be needed, also if resources exceed demand then restructuring is needed and available crew mightbe reduced. Demand will also increase or decrease accordingto how many aircraft are available inthe fleet.

Crew IOC’s involve various costs, e.g. overtime, extra crewneed to be able to manage absencesand sick leave, hiring extra staff, compensation etc. Thesecosts are based on estimations from datagathered each previous year and then added with the crew DOC’s to the pricing drawn up to theTour Operator.

Pilot and cabin crew salaries are highly regulated expenses, by among all, seniority and unionagreements (IOC’s). Evaluation of hotel expenses and allowances from previous seasons as wellas detailed pairings analysis allow for an estimate of expenses for the coming season (DOC’s).Estimations such as these, make it possible to account for increases (or decreases) occurring fora season by for instance determining where demands for more overnight stays are and estimatingearly on where pay benefits will be available for the crew.

Crew expenses are drawn up to cover the two weeks where a seasonal peak occur, and thenmade to cover the entire season. Handling finances this way assures that the height of the sea-son, where more resources are demanded, is accounted for andtherefore financial coverage existsthroughout the entire season. Crew costs are followed closely as they are the second largest ex-pense for an airline and as such the obvious parameter to optimize in order to yield a more favorableeconomic outcome i.e. minimization of overall crew costs and therefor the total costs.

3

System processes

An overview of the systems that are currently integrated to the planning process at TUIfly Nordiccan be seen in figure 3.1. This image illustrates which of the systems belong in the same groupand which of those that are operated from the main office in Stockholm.

Figure 3.1 Systems communications and software in use for the planningprocess. Programscurrently in use at TUIfly Nordic are highlighted with a darker colour. Illustration: Mercedes Inal.

These systems have different purposes and operate with different levels of complexity, which willbe described in the following sections, starting with a systems description, which will explain theinterconnectivity of the internal systems available to thePlanning department at TUIfly Nordic.There were some difficulties finding enough information about each system, and information con-cerning this chapter is mostly based on own observations. For this reason information regarding

28

3.1. SYSTEMS DESCRIPTION 29

the following systems, IDPS and RM5, are limited to the viewsand concerns of TUIfly Nordic.

3.1 Systems description

There are currently two systems in use at TUIfly Nordic (that are directly connected to the Planningdepartment), these are the IDPS systems, which will be described in section 3.1.1 and RM5 whichwill be described in 3.1.2. Both of these systems are designed to manage airline operations andconsist of a modular interface (or applications), each module having a different purpose.

The basic set up for both systems is a main database connectedto all modules, with a liveset up where the live program progresses. It is assumed that each system, IDPS (system 1 infigure 3.2) and RM5 (system 2 in figure 3.2), runs optimally when all of their separate modules areoperated following the order for which they are designed (e.g. aircraft rotations module→ crewparing module→ rostering module→ live module). Connecting each module in this way wouldeliminate interconnectivity issues and supposedly createa straightforward workflow.

This is a set up not entirely followed at TUIfly Nordic. The twomain reason for this are, firstoff, all modules available by the different systems are not necessary nor do they entirely addressthe needs of the air-traffic Planners or the company. Secondly, since TUIfly Nordic has used RM4previously (updated later to RM5, this will be explained in section 3.1.2) and decided in favorof keeping the system after the company merger and running italong side the systems that wereintroduced by the TUI AG Group.

Figure 3.2 Systems interconnectivity and modules currently in use at TUIfly Nordic. Illustration:Marcus Karlsson, reformatted by Mercedes Inal.

The TUI AG Group decided that its in-house developed systemswere to be used by all of their af-filiate airlines. The binding module throughout the companyis Opscon, a live system and Airpas,


a financial system. TUIfly Nordic has thus adopted these IDPS modules because of this decision,and added Arsis, an air-traffic scheduling module, because it acts like an intermediate betweenOpscon and Airpas. This has resulted in the presence of two systems currently in use at TUIflyNordic. These systems do function well separately but running them together creates interconnec-tivity issues. There are however also exceptions to this in the TUI Group, for instance Corsairflyin France has an operations system of its own.

At this stage it is important to mention that all of the modules are interconnected in differentways, basically they can read information from one-another. The set up of these systems are asfollows, and can be seen in figure 3.2. The IDPS systems are concentrated to the aircraft rotationsscheduling and the financial department, while the RM5 system is used to handle the constructionand follow up of crew pairings as well as rostering (and day-to-day fleet follow ups via Opscon).The explanation to this will be given in the individual sections that come next.

As was mentioned previously, it is assumed that using a single systems modules in the chaindesigned by the developer, is preferable rather than havingseveral systems, since interconnectivityissues might arise. The reason why TUIfly Nordic is using a different set of system for crew relatedoperations is due to research made within the company, finding the needs set up by the planningdepartment not met by the IDPS module covering similar operations.

3.1.1 IDPS

The Integrated Disposition Planning and Statistics System, or simply IDPS, is an IT software tech-nology which provides a broad selection of airline operations and airline management applications.IDPS is a software in-house developed by TUI Airline Management and is currently marketed by

Figure 3.3 IDPS systems hierarchy. Illustration: Mercedes Inal

GO Center, short forGroup Operation Centre. GO Center is the core provider of flight operationsinformation, analysis, dispatch and flight support.


The function of IDPS is to manage airline operations efficiently, economically and above all assafely as possible. IDPS was developed in the seventies, thesoftware consists of a core database(fast data storage and retrieval system). There is an integrated applications suit covering all kinds oftasks within the airline operations stream such as commercial flight planning, maintenance plan-ning, tail number assignment, crew rostering, daily crewing, operations control and back officesupport. The modular set up can be seen in figure 3.3 (there aremore modules available but thesehave not been incorporated because they lack of relevance tothis project.)

These modules will be further explained in the sections to come, for short the purpose of eachmodule is as follows:

– Arsis: air-traffic scheduling,

– Opsman: crew pairings module thats not currently at use,

– Opscon: live operations operated from Germany,

– Airpas: financial handling and database,

– Aeromap: tail assignment also not currently in use.

Support packages and contracts are available in a wide variety ranging from in-house implemen-tation to tailored arrangements adapted to the needs of the individual airline. IDPS target group issaid to be small to medium size airlines.

The IDPS systems used by TUIfly Nordic will be explained belowstarting with the air-trafficscheduling tool, Arsis.

Arsis

The main air traffic planning tool used is an aircraft rotations planning software calledArsis, seefigure 3.4. Arsis like IDPS functions as a database, which means that the program is meant toorganize, store and retrieve large amounts of data within its operational use. Arsis also has severalinbuilt modules such asscenarioand live (see figure 3.3), and these are the only two modulescurrently utilized by TUIfly Nordic.

Arsis does not function as a database and has therefore no history retaining possibilities i.e.statistics, the modules utilized within the program do not communicate, meaning that a scenariocreated in thescenario-module can not be transferred to thelive-module. This creates unnecessaryworkload as the planner has to manually rewrite the tested scenario into thelive-module. The resultis an inefficient work environment which can cause hang ups and delays throughout the planningprocess.

The importance of this system is that it is connected to its partner programsAeromap, which isused for maintenance purposes such as tail assignment, andOpscon, which handles the live feedof the ongoing traffic program. These two are currently operated from Germany.


Figure 3.4 Screenshot of Arsis interface.

There is an ongoing project that aims to bring the control of Aeromap in to the hands of thetechnicians and maintenance team in Arlanda, Stockholm since they hold first hand knowledge ofthe airplanes. Negotiations are currently in progress.

Opscon

Figure 3.5 shows the Opscon interface. The schedule is updated by GO Center in Germany everythree days, and is, from that point, on out of the Planners control. Changes that occur within thesethree days are relayed by the Planners over to GO Center who transfers them into Opscon.

Even though Opscon is operated from GO Center in Germany, thelive operations are closelymonitored by TUIfly Nordic. The screenshot illustrates a colour system in Opscon that simplifiesoperational follow-up (dull-greenfor flights flown on time andyellowfor flights that are delayed),


Figure 3.5 Screenshot of Opscon interface.

there is also a marked timeline (vertical green line) showing the momentary progress of the sched-ule, see illustration for more colour explanations. The purpose of Opscon, besides controlling thelive progress of the air-traffic, is to deliver journey logs,ACARS (Aircraft Communications Ad-dressing and Reporting System) and the movements of the aircraft to Airpas on an over night basis,which later sorts this information.

Airpas

The main function of the Airpas system is to calculate directoperational costs and revenues, han-dling the distribution of indirect costs and invoice checking, see figure 3.6. Airpas is a powerfulfinancial tool that handles most business units that are involved in the airline sector, such as groundhandling, fuel expenditure, catering/inflight sales, crewand maintenance (standard as well as indi-rect costs) and also administration, insurances etc.


Figure 3.6 Screenshot of Airpas interface.

Airpas allows for calculations and produces scenarios (forsignature and pricing purposes), budget,planned and actual figures. Airpas is therefore also a good report tool, that is able to retain histor-ical data to generate forecasts for future estimates, whichare very useful for the pricing process.The data is received and updated over night as flights are processed by Opscon. The program iscompatible with Excel, and is therefore able to produce large report sheets that are easy to rework.

3.1.2 Sabre Rocade Suite

Sabre®Rocade airline operations suite: this system is internally referred to asRM5 (short forResource Manager version 5). RM5 is developed bySabre: Airline solutionsa company thatprofessionalizes in airline operations. RM5 is a multi purpose system mainly used for scheduling,crew pairing and crew follow up at TUIfly Nordic.


Positive aspects of RM5 are the report generating abilitiesthat the system and modules offer,and some in-built optimization modules (all of which don’t always work perfectly but it is a workin progress and the RM5 developers are trying to improve these modules to better suit the wishesof their clients). RM5 is a module based system just as IDPS, see figure 3.7.

Figure 3.7 RM5 systems module overview and order of connectivity. Illustration: MercedesInal.

This image illustrates the order of the systems according tohow they are designed to operate (i.e.ARP → PAR → ASG → DCO ROC). Each module representing each step of the planningprocess up until the advanced stages and going live (daily operation). Each module has its ownsub-modules that have specific purposes that assist the mainmodule (e.g. optimization tools, crewportals).

All of the modules seen in figure 3.7 are currently available for TUIfly Nordic, but all modulesare not in active use (license-for-use agreements are needed). The following paragraphs will givea short description of each module seen in figure 3.7. Some information on the modules of RM5can be found in booklets offered by Sabre Airline Solutions [10].

ARP is the RM5 equivalent to Arsis. It is an air-traffic scheduling software, with slot memoryand report writing possibilities. When the air-traffic program is created in Arsis it is transferredto ARP, via so called*.ssimfiles. This is how the air-traffic schedule enters the RM5 system, andfollows the process as described by figure 3.2. It is preferred by the Planners at TUIfly Nordic that


the scheduling process begins in ARP, creating a continuousflow system-wise, and transferringthe ready schedule from ARP into Arsis, since the only purpose of Arsis is the connection it holdsto Opscon and Airpas. This is for the time being not possible as Arsis does not support file import.

PAR is the crew pairing module (equivalent to Opsman in IDPS). Crew pairings are createdwhile planning the aircraft rotations to ascertain whetheror not legality holds. PAR works similarto Arsis, work is mainly manual without access to optimalityfunctions as of today, resulting in adependency of the Planners experience.

Figure 3.8 Screenshot of the RM5 module: PAR interface.

PAR contains the necessary legalities involved in creatinglegal work days/periods with the nec-essary amount of rest periods demanded by regulations involved in the airline business (see sec-tion 2.9). In PAR, it is possible to create several scenariosthat can be compared and evaluated inorder to determine the best outcome. The PAR interface can beseen in figure 3.8.


CTO Crew Trip Optimizeris the in-built optimization tool for PAR. This is currentlyin pro-cess of being activated at TUIfly Nordic. Evaluations are in progress. Due to lack of relevance tothis project no coverage has been made of CTO (also known asRaptorin the older RM4 version).The purpose of activating it is however connected to the problem statement, there is a need at TU-Ifly Nordic to assist the Planners with optimization tools inorder to determine whether computergenerated solutions might provide a better scheduling/pairing solution than the manual work doneby the Planners and their gathered experience.

ASG short forAssignis the crew assignment module. This stage is also known asrostering.Assigning each individual crew member work duties according to the crew pairings, and all otheractivities (training, meetings) but also vacation and daysoff, making a 28 day schedule for eachemployee.

ARCON stands for Auto-Rostering, which is the CTO equivalent to the assign module. Itis an optimization tool; optimization in the areas of crew pairings, crew assignment and rosteringare heavily researched areas, some mathematical models have been developed to the extent thatthey can be effectively implemented in software (for further reading see Chapter 4). ARCON iscurrently in use at BLX for cabin crew rostering.

DCO Daily Crew Operationsis the crew tracking module. This module keeps track of day-to-day activities of the crew. Ready rosters are uploaded into DCO and monitored combined withdaily air-traffic schedule activities conveyed via the ROC module.

CWP Crew Web Portalis an interface for the crew, one where they can check schedulechanges, confirm their check ins and check outs, leave notes and briefings.

Goody Bag is a system developed by Rainmaker, a company with close business ties toSabre Airline Solutions. This relationship simplifies system connectivity with RM5. Goody Bagis a recently activated system at TUIfly Nordic, designed to simplify hotel bookings for crew.The basic idea is to automatize booking so that hotels can connect to a remote client and confirmamount of bookings according to the air-traffic schedule.

ROC Rocade Operations Controlis the RM5 equivalent to Opscon in IDPS. This is the liveair-traffic schedule tracking, day-to-day operations. Opscon is connected to ROC in terms of con-veying the daily operations to RM5 via DCO module.

4

Airline Optimization

The airline business generates profits in the billions, and that only incorporates the charter airlines,expanding the field into regular air traffic and cargo flights the economic impact rises by nearexponential factors. As mentioned in the introduction eventhe smallest improvements to anyaspect of the airline process can result in substantial costreductions.

This thesis will review the charter airline business from a mathematical point of view, from acurrent position at TUIfly Nordic. The contribution will be two folds, first a more comprehensivereal-world view will be given of the difficulties governing the traffic scheduling process from thedesign stage all the way to the execution stage. The purpose is to determine the objectives and con-straints that arise along the way and through this determinethe risk factors that need to be evaluatedin order to reach an optimal air-traffic schedule. This involves several problems that will briefly beexplained by mathematical models. The focus will lie on the aircraft rotations scheduling, which inthe case of TUIfly Nordic has become the heart of the traffic scheduling process. Secondly, an opti-mization model will be assessed for modeling certain key performance indicators. Since managingthe airline traffic means facing several opposing constraints, a solution is presented appropriatinga multi-objective programming model for the optimal allocation of decision variables, allowingTUIfly Nordic to evaluate and asses risk factors that determine the efficiency and productivity aswell as the economical impact of traffic schedule scenarios.

Methods to optimize problems of a commercial nature was firstimplemented in the airline in-dustry, making this a widely researched area. Heuristics combined with advanced mathematicalalgorithms and the advancement in computer hardware and software technology has made it pos-sible to solve large-scale, sophisticated airline optimization problems for the past 60 years. Theliterature available on airline optimization is vast. A simple overview of the advances and a briefsummary of the operational factors governing the airline business ranging from a managerial pointof view to traffic scheduling is given by J. L. Snowdon and G. Paleologo [11].

The field of airline optimization is largely limited to regular flights, rarely covering the charterbusiness. Charter airlines compared to regular airlines, are often categorized as small to medium

38

4.1. SCHEDULE PLANNING 39

size airlines based on their production and fleet size, two factors that matter greatly in the processof planning an air-traffic schedule. The planning processesvary greatly from airline to airline, eachhave their own structure and approach to the steps describedin the following sections. This thesiswill look into the process of TUIfly Nordic in order to evaluate the optimality of their process.

4.1 Schedule planning

There are several constraints to shaping an airline schedule, such as crew duty hours, aircraftmaintenance protocols, passenger flows, ground base resources and arrival/departure time win-dows. Collaborating all of these objectives into a feasibleflight schedule is one of the industryworlds most complex issues. Most often, the process is partitioned and optimized using heuristicmodels that describe a solvable/programmable model. The complexity of the problem lies in fac-tors that can not be expressed using a simple model, there arecompany/union regulations, safetyregulations, security concerns, market controlled parameters such as volume, density and elasticityof demand.

There has never been a single optimization model that has addressed or even formulated the en-tirety of the scheduling process. Attempts have only been made to solve two or more combinationat a time in a single model. An overview of the impact operations research has had on the airlineindustry is given by M. Clarke and B. Smith [12]. The basic steps of the air-traffic scheduling canbe seen summarized as in the flow chart in figure 4.1.

Figure 4.1 Schedule design flow chart.

There is a hierarchal order to the steps described in the flow chart, where each step produce theinput to the following step. It is possible to optimize each step and provide the result of this step to


the next, and optimize again but this is not necessarily the optimal result for the problem in total.It can be mentioned that only in mathematical terms is it easyto partition the schedule process intothe steps seen above. It is an appropriate way to divide a large problem into sub-problems. Thesesteps (sub-problems) will be described in the coming sections, but since this paper will also showa real-world view of the planning process the reader should keep in mind that these steps are onlya rough outline of the process, and that the step by step process that is described here is far fromdescribing the full picture of air-traffic scheduling.

4.1.1 Schedule design

The schedule design stage addresses market based planning parameters such the establishment of aservice plan, frequency of flight, demand forecasts for popular travel destinations and competitivemarket information. Establishing profitability and seasonal demand is important as they are twocriteria that largely impact the flight schedule design. Thecomplexity of the schedule designlies in these inherently mutable parameters not only creating a large scale problem but one whereinformation, i. e. revenues, market demands and responses from competitive airlines, are very hardto come by. Creating a feasible flight schedule that addresses the entire problem is very difficult.Support systems for this step of the schedule planning has been slow in development.

In the case of TUIfly Nordic information regarding travel demand is largely provided by theTour Operator. This data is based on statistics, destination capacities (hotels, hotel beds etc.) andestimated market incentives. This information in turn willbe developed into an initial feasible flightschedule, arrival and departure times satisfying maintenance intervals and resource constraints(available aircraft, personnel). The seasonal schedules at TUIfly Nordic are generated using theschedule design of the previous season, this becomes the backbone of the new seasons schedulewhere seasonal changes (destinations, additional flights and aircrafts etc.) are incorporated andadjusted for.

Information regarding schedule design has only been researched through overviews, these men-tion at most how sparse the information is, therefore literature regarding the subject of scheduledesign have been deemed irrelevant. Observations at TUIfly Nordic has revealed that the nature ofeach of these variables are operated and monitored by a largeamount of people in a company con-tributing to the already complex problem. There is no local hub to extract data, and some variablesalso depend on the experience that is contributed by employers that have long served the airline in-dustry. Therefore it is assumed that any model developed will not be flexible enough to incorporateall that is necessary in this stage of planning, there are simply too many constraints and conjectures.This is left to evaluating by processing past schedule designs, statistics and collectively evaluatingthe needs for coming seasons.


4.1.2 Fleet assignment

The fleet assignment model determines the size of aircraft (capacity, range) needed for each flight.The objective is to maximize profitability by assigning the right aircraft type to the right flight seg-ment. The fleet assignment complies to a large set of constraints, ranging from passenger demand,personnel on the aircraft to fuel capacity and maintenance demand. There are also operationalconstraints that have to be taken into account for when the aircraft arrives at its destination e.g.runway length (and availability), gate availability and noise limits to mention a few, all of whichcontribute to the complexity of the problem.

Airlines tend to own different models of aircraft to accommodate different distance require-ments for example, Boeing-737 (seating capacity 124–215, maximum range at maximum take ofweight 2.800–10.200 km) for shorter flights and Boeing-747 (seating capacity 452–624, maximumrange at maximum take of weight 9.800-14.800 km) for longer flights. A large airline can withinits fleet of aircraft own several aircraft models, ranging from small propeller driven craft that reachshort distance destinations, handling a handful of passengers to larger aircraft that transport hun-dreds of passengers to destinations farther away without the need for stop-overs or refueling. Asmall airline will however not afford an extravagantly large modeled fleet, they will have as manyaircraft or more commonly fewer aircraft than can meet demand. Instead they will hire extra air-craft to fully accommodate for the travel demands. This is a safety measure, for when economicaltimes are tough. Downsizing a fleet is a difficult task in any economical climate.

The fleet assignment problem has been investigated for nearly 20 years. The literature availablefor the subject is vast with several optimization theories applied, the basic fleet assignment modelhas been researched for this section only for some mathematical background. The basic modelwas best described by C. A. Hane et al. [13]: they describe thefleet assignment model (FAM) asa multi-commodity flow problem with side constraints definedon a time-space network that is aninteger program using branch-and-bound.

Figure 4.2 Fleet assignment model 4.2

The time-space network has a circular time line, see figure 4.2, representing a 24 hour period or adaily schedule for each aircraft fleet at each city. A node represents an event along the timeline,such as a flight arrival or departure. The assigned fleet to a flight is represented by a decision vari-able that connects the two nodes created by the departure andarrival. The mathematical problemcan be seen in (4.1)-(4.6).


minimize ∑j∈J

∑i∈I

ci j Xi j (4.1)

subject to: ∑i

Xi j = 1, ∀ j ∈ J (4.2)

∑d

Xidot +Yiot−t −∑d

Xiodt −Yiott+ = 0, ∀iot ∈ N (4.3)

∑j∈O(i)

Xi j + ∑o∈C

Yiotnt1 ≤ S(i), ∀i ∈ I (4.4)

Yiott+ ≥ 0, ∀

iott+

∈ N (4.5)

Xi j ∈ 0,1 , ∀i ∈ I , j ∈ J (4.6)

The following notation is required:

C = set of stations (cities) serviced by the schedule,I = set of available fleets,

S(i) = number of aircraft in each fleet fori ∈ I ,J = set of flights in the schedule,

O(i) = set of flight arcs, fori ∈ I , that contains an arbitrary early morning time (i.e.3AM, overnight),

N = set of nodes in the network, which are enumerated by the ordered triple iotconsisting of fleeti ∈ I , stationo∈C, andt =takeoff time or landing time at o,

t− = time precedingt,t+ = time followingt,

iotn = last node in a time line, or equivalently, the node that precedes 3AM,

iot1 = successor node to the last node in a time line, and decision variables:Xiodt = Xi j = 1 if fleet i is assigned to the flight leg fromo to d departing at timet, and

0 otherwise;Yiott+ = number of aircraft of fleeti ∈ I on the ground at stationo∈C from timet to t+,

The model is without any through-flights i.e one-stop, whichis an adaptation of C. A. Hane etal. [13]. Equation (4.1) minimizes the cost of assigning aircraft types to flight legs. The fleetassignment must be feasible, therefore the first constraint(4.2) states that each flight in the scheduleis assigned exactly one aircraft type. The second balance constraint (4.3), ensures that itineraries ofall aircraft types are circulations through the time-spacenetwork that can be repeated over multiplescheduling horizons, like a cycle. Equation (4.4) is a planecount constraint, saying that the totalnumber of aircraft assigned can not exceed the number available in the fleet.

Constraints that address issues such as maintenance demands, slot allocation, passenger andcrew considerations can be added to theFAM. Since many of the processes within planning anaircraft rotations schedule are interdependent it has beenattempted to combine the fleet assignment


model with several other of these processes, such as schedule design, maintenance routing and crewrotation scheduling. It is considered that the optimal solution of each of these parts alone do notcontribute to the optimal solution of the combined process.Others have developed the model toinclude time windows, the objective in mind being that improved flight connections can lead to anincrease in revenues, researchers in this area are Desaulniers et al [3] and B. Rexing [14], [15].

However as complex as the fleet assignment stage is, and how flexible the model is to accom-modate several constraints that inflict the schedule planning stage, it does not apply to the charterairline scene; why this is will be explained shortly. The real scheduling dilemma are the aircraftrotations, or the scheduling of each flight segment, or more commonly called aflight leg.

When owning a small aircraft fleet, assigning each aircraft to a specific flight segment becomesa straightforward task. It is mentioned again that the fleet assignment model can accommodateseveral constraints that are important for a flight program to be feasible or flyable. However solv-ability will be compromised when several constraints of different nature (linear, nonlinear) areadded. There are extensive legal constraints set upon a flight schedule (rules and regulations con-cerning aircraft, flight safety, flight path restrictions, crew and crew safety followed by pilot andcabin crew union and collective agreements), incorporating these into a model would not only becomplicated and time consuming task it would also result in massive cpu times if the model re-mains solvable. No research has yet been made stating the incorporation of even a fraction of theseconstraints into a mathematical model. There are however softwares that incorporate these param-eters as legality, appointing a penalty to an infeasible combination. It is also worth mentioning thatthere are not that many software available that combines thelegality demands for the flight withthose of the crew. A combination found very important from observations at TUIfly Nordic.

4.1.3 Aircraft routing

At the aircraft routing stage, each sequence of flights are assigned an aircraft individual. This is alsocalled tail assignment. As mentioned before an airline fleet can consist of several aircraft types,for a small airline it is normally between 3-4 aircraft types. The reason for keeping the numberof different types of aircraft low is discussed about in section 2.6.1. There are often a coupleof the aircraft types each in a fleet, the aircrafts are designated individual identities, allowingthe maintenance teams to keep track of each aircraft individual flight duties (i.e. block hours,maintenance intervals). The most important operational constraint to be met when determiningthe aircraft routing is maintenance. A simplified maintenance model is described by C.Barnhart etal. [16], notation and model is taken from J. L. Snowdon and G.Paleologo [11]).


minimize ∑s∈S

csxs (4.7)

subject to: ∑s∈S

xs = 1, for all flights i (4.8)

∑j∈O(v)

j∈s

xs+ ∑j∈I(v)

j∈s

xs+yO(v)−yI(v) = 0, for all stationsv (4.9)

y≥ 0 (4.10)

x∈ 0,1 (4.11)

The model, equations (4.7)-(4.11), describes a sequence ofconnected flights by one individualaircraft. The flights are defined as a string from airport a→ b → c until arriving at a final airport.The following notation is given:

s = strings,cs = associated cost of strings,S = set of all augmented strings (augmented string, the minimum time necessary

to perform maintenance attached to the end of the last flight in the string),v = node,i = flights,j = stations,

F = set of flights,I(v) = incoming links,

O(v) = outgoing links,

xs =

1 if string is selected as a route0, otherwise,

ym = is number of aircraft being served at service stationm

From observations at TUIfly Nordic, it is learned that the scheduled maintenance duty for eachaircraft individual can vary greatly from one to another, a duty depending most often on flighthours acquired. This means that the maintenance technicians handle the tail assignment. Theyalso handle the aircraft swaps so all aircraft circle through home base at appropriate maintenanceintervals. It is preferred to maintain a homogenous spread of flight hours among the individualaircraft.

4.1.4 Crew scheduling

Allocating crew optimally is often deemed the area of airline optimization where huge savings canbe made. Crew scheduling can be partitioned into three phases: crew pairing, crew assignment


and recovery from irregular operations. Crew pairings will be the focus of this project, as it isconnected to the planning operations at TUIfly Nordic, whilecrew assignment and recovery willonly be discussed briefly.

Crew pairing

The optimization of crew pairings is a subject studied for nearly half a century. The general purposeis to achieve the minimum cost set of duties that cover each flight leg concurring with currentregulations and restrictions for crew duties. Crew pairinghas been described earlier i Chapter 2and therefore only a short explanation will be given here so that the model described later can beeasily followed. The generation of crew pairings typicallybegins with a flight schedule with flightlegs and their corresponding fleet assignment. These are then decomposed for each fleet type intowork duties incorporating all crew types (pilots, flight attendants). The combination of two or moreof these duties is a paring and these range from 1-5 days length and start and end at the designatedcrew’s home base.

Legality, (i.e. ICAO rules and regulations, union agreements etc.) determines the feasibility ofcrew pairings. There are for instance limitations for number of duties and flight time in a pairing,there are also minimum rest requirements following these limitations, all of which have to be takeninto account in order to create crew pairings. European carriers must comply with ICAO, EASAand governmental regulations but the union rules can be somewhat variable, although this usuallyresults in other additional costs. Crew salaries can also beconnected to the structure of the pairingse.g. guaranteed minimum pay per duty period and overtime. Penalties are usually included intomodels in order not to create excessive costs concerning crew duties.

Creating a model that incorporates all of these limitationsis complex, it is possible to gener-ate a full set of feasible crew pairings for a problem although it is not computationally sensibleas it might take several days. It is also difficult to incorporate crew cost as these would haveto be incorporated through various different linear (per-diem charges, hotel stay) and nonlinearconstraints (flying time, time away from home base). The following literature was studied and isrecommended for further reading on the the complexities of crew pairing, G. Desaulniers et al. [2],N. Kohl et al. [6], P. Vance et al. [17], S. Lavoie et al. [1] andJ. L. Snowdon et al. [11].

A simple crew pairing model is illustrated with equations (4.12)-(4.14), followed by the nota-tion used in this model.

minimize ∑j∈P

c jx j (4.12)

subject to: ∑j∈P

ai j x j = 1, ∀i ∈ F (4.13)

x j ∈ 0,1 (4.14)


P = set of all feasible pairings,c j = cost of pairingj,

x j =

1 if pairing j is used0, otherwise,

ai j =

1 if pairing j covers flighti0, otherwise,

F = set of all flights that must be covered in the period of time under consideration,

This model is reminiscent of the fleet assignment model described in section 4.1.2, the objective isto minimize the total cost (equation (4.12)) with the constraint (equation (4.13)) ensuring that eachflight is covered once an only once (J. L. Snowdon et al. [11]).This is a partitioning problem, theflights that are needed to be covered are the rows and the available crew pairings are representedin columns. Like the FAM, additions concerning restrictions in resources such as upper and lowerbounds on crew availability and number of duties and/or dayscan be added and in this way signif-icantly reduce the set of feasible pairings (the wordfeasibleused here is not correlated with whatis a legal and optimal crew pairing).

Several approaches to solve this pairing generation model has been studied and used, some ofthese will be described in the coming sections.

Crew assignment and recovery

After pairings have been generated a crew is assigned to them, it is at this stage where the individualcrew members work duties are scheduled. These assignments or rosters are done on a monthlybasis. The objective is to minimize cost while taking crew preferences, vacation days, days off andlanguage restrictions into consideration.

It is not uncommon that unscheduled events occur e.g. bad weather, flight cancellation, delays,last-minute maintenance, illegal crew, sickness etc., there are endless unfortunate events that af-fect both flight crew and passengers. Handling of these problems are referred to as recovery fromirregular operations, or simplydisruption management. The purpose of having disruption man-agement is to minimize the costs of reassigning crew or aircraft by taking into account availableresources. There are different recovery focuses, there arethose that handle aircraft and then thereare the crew specific disruptions which are the more difficultof the two because of the many rulesand limitations that exist for crew work.

This section is intentionally kept short due to the lack of relevance it holds to the project, but forthe curious reader there are plenty of literature approaching both rostering and recovery operations,some of these have been mentioned previously in the early chapters ( [18], [6], [4]).

4.2. BACKGROUND FOR COMBINED MODEL 47

4.2 Background for combined model

To begin assembling a model that may fit the proceedings of thePlanning department at TUIflyNordic certain presets must be declared. The beginning of this chapter has described that thedifficulties of airline optimization are numerous. Combining several stages, like those describedby figure 4.1, do not always yield an optimal solution. The reason for this is partially due to howthe planning is laid out (something that varies from airlineto airline), and also due to computationalreasons. Combinatorial models tend to become computationally not trackable.

Furthermore including the basic needs of TUIfly Nordic meansthat several constraints willhave to be taken into account resulting in fewer schedule solutions. When optimizing it is com-monly held that the global solution that takes account of allconstraints is the optimal solution.Figure 4.3 illustrates that when there are several parameters but constraints are held few, then thereis a larger set of solutions to chose from.

It is important to keep in mind that mathematically optimality is defined withone solution,that is maximizing or minimizing a function with a number of constrains will yield one optimalsolution. This is also the case for a schedule, there will only be one optimal schedule as a result ofa given algorithm to optimize, although since each objective constraint is variable of several otherconstraint meaning that changing one function or adding penalties will result in a new solution,yielding in several different feasible air-traffic schedules.

Figure 4.3 Optimization using few decision variables and few constraints, resulting in a widervariety of solutions. Illustration: Mercedes Inal.

Of course when penalties or restrictions are added to the list of constraints (usually in the form ofmore constraints), see figure 4.4, possible solutions get restricted and there are fewer options tochose from. In the case of air-traffic scheduling, it is assumed that when a nearly infinite numberof restrictions are added, a solution accommodating all of these constraints become impossible. Asolution here refers to an air-traffic program that is flyableat the lowest cost.

Applying the step-by-step planning seen in figure 4.1 to TUIfly Nordic will not work. The reasonfor this is that the planning process far succeeds this type of model setup. Fleet assignment is notcomplicated for a fleet of often 12 aircraft (in the case of TUIfly Nordic), it can however be relevant

4.3. SIMULTANEOUS AIRCRAFT ROUTING AND CREW SCHEDULING 48

Figure 4.4 Optimization using many decision variables and constraints, resulting in a fewersolutions. Illustration: Mercedes Inal.

to use the FAM when there is a fleet of perhaps 50 or more aircraft, when an overview becomesdifficult to retain.

With a small fleet the setup is often simple, a Gantt chart viewwere each aircraft in the fleetis represented as a bar and processes are added to each aircraft according to a timeline (see pre-vious image of Arsis, figure 3.4). The fleet assignment model is therefore not a good place tostart, although the model can be extended to include upper and lower crew considerations, certainmaintenance constraints, noise limitations and gate availability (L. W. Clarke et al. [7]). Addingthese constraints will go a long way when creating a feasibleair-traffic schedule, but to apply thismodel schedule design data is needed (arrival and departuretimes, days, routes, etc.), data that ismore commonly available when creating daily schedules, i.e. regular flights.

Charter airlines like TUIfly Nordic have varying seasonal schedules (one for winter and onefor summer). There is also not necessarily a fix set of travel destinations, market and customerdemand will be dictating factors of travel destinations andopportunities.

4.3 Simultaneous aircraft routing and crew scheduling

Aircraft routing, is a process handled by the TUIfly Nordic maintenance team, insures that eachaircraft individual has the necessary maintenance opportunities. However, the Planners will planthe schedule according to this need, there are always swaps and built-in gaps that accommodatea maintenance slot. These slots can be planned as individualflights (a destination) or just left asgaps in the schedule. The air-traffic planning is generated following a pattern, one that repeatsafter a sufficient enough time laps (often 1.5-2 weeks). Because of this reason it is assumed thatthe aircraft routing model combined with crew scheduling, J-F. Cordeau et al [19] is a more appro-priate model to begin with. This model incorporates maintenance requirements corresponding toroutine checks every three or four days, corresponding to regular A-checks. Shorter maintenancechecks are scheduled separately, usually depending on where maintenance facilities are availableand longer maintenance checks, such as C-checks, are planned over longer period of times sincethey ground the aircraft for several weeks at a time.


The basic model as described by J-F. Cordeau et al [19] is represented below, some backgroundis needed to understand the notation of this model. This formulation assumes a dated horizon wherethe set of flight legs can vary from day to day. Assume a plannedhorizon and a setL of flight legsto be flown by a single aircraft, each flight legl ∈ L is defined by an origino and a destinationd and by fixed departure and arrival times. The model for simultaneous aircraft routing and crewscheduling is stated as follows:

minimize ∑f∈F

∑ω∈Ω f

cxωxω + ∑

k∈K∑

ω∈Ωk

cyωyω (4.15)

subject to: ∑f∈F

∑ω∈Ω f

aiωxω = 1, ∀i ∈ N (4.16)

∑k∈K

∑ω∈Ωk

aiωyω = 1, ∀i ∈ N (4.17)

∑k∈K

∑ω∈Ωk

bi jωyω − ∑

f∈F∑

ω∈Ω f

bi jωxω ≤ 0, ∀(i, j) ∈C (4.18)

∑ω∈Ω f

xω = 1, ∀ f ∈ F (4.19)

∑ω∈Ωk

yω = 1, ∀k∈ K (4.20)

xω ∈ 0,1 , ∀ f ∈ F,ω ∈ Ω f (4.21)

yω ∈ 0,1 , ∀k∈ K,ω ∈ Ωk (4.22)

(4.23)

N = set of nodes,A = set of arcs,G = (N,A) time-space network, see figure 4.5C ⊆ A set of arcs representing short connections in the networkG ,F = set of aircraft,K = set of crew,

Ω f = for every aircraftf ∈ F, set of feasible paths between nodesof andd f in Gf

Ωk = for every crewk∈ K, set of feasible paths in the networkGk

ω = path,(i, j) ∈ A represents feasible connections between two successive flight legs,

cω = cost of sending one unit flow betweenof andd f ,where costs are separated forx andy,

xω = binary variable that represents the flow on the path concerning aircraft,yω = binary variable that represents the flow on the path concerning crew,

“. . . an arc is defined between nodesi and j if the destination station of legl i is the departurestation of legl j and if the connection time between the two legs is larger thana given station


specific threshold that represents the minimum connection time when both legs are covered by theaircraft.” (J-F. Cordeau [19]). If a node is selected and an arc belongs to an aircraft pathω ∈ Ω f

the binary constantsaiω andbi j

ω will take the value 1.

The objective of the function is to minimize the sum of all aircraft routing and crew schedulingcosts, equation (4.15). Coverage for each leg by one aircraft and one crew is covered by constraints(4.16)-(4.17). Constraint (4.18) , guarantees that when connection time is too short the crew doesnot change aircraft. Remaining constraints (4.19)-(4.20)state that each aircraft and each crew areassigned a path.

Figure 4.5 Model network [20].

This model can be supplemented by constraints from theairline crew pairing problem, (ACPP).D. C. Flórez et al. [20] describe a ACPP model that can be incorporated into the simultaneousaircraft routing and crew scheduling model. The set up of theACPP is similar to the model de-scribed by equations (4.15)-(4.22). Adding crew constraints, such as crew flow through the set oflegs, ensuring that the crew starts and ends their service ata personal base (home base) and thatcertain simple flight time limitations are kept, would only add to the model representing real-worldsituations more accurately.

The following constraints can be added, used notation will be explained. Constraint (4.24) willguarantee crew flow through the set of legsl ∈ L. The notation used through out these constraintsfor feasible paths for all crew,ω ∈ Ωk, is re-written as j ∈ N|(i, j) ∈ A.

∑ j∈N|(i, j)∈A

yki j − ∑

j∈N|( j ,i)∈A

ykji = 0, ∀i ∈ N,k= 1, . . . ,cmax (4.24)

Wherecmax is the maximum number of crews in the solution. The constraint (4.25) indicate thateach crew can leave the home base node at most once to serve thefirst leg in the pairing.

∑ j∈N|(0, j)∈A

yk0 j ≤ 0, ∀k= 1, . . . ,cmax (4.25)


A pairing has to start and end at a personal base and thereforethe same city, constraint set (4.26)ensures that this holds. Some the following notations is needed for the next few constraints,B bethe set of cities that are personnel bases;SandI are the sets of domestic and international cities,respectively;

∑(i, j)∈A|ok

j=b

yki j − ∑

( j ,0)∈A|dkj=b

ykj0 = 0, ∀i ∈ 1,2 ,k= 1, . . . ,cmax,b∈ B (4.26)

Each pairing can not exceed the maximum number of duties allowed, this is enforced by the set ofconstraints represented by equation (4.27).

∑(i, j)∈A|i>0, j>2,ti<t j

yki j ≤ dmax, ∀k= 1, . . . ,cmax (4.27)

Limit on maximum duty time is set by constraints (4.28).

tai + tg

0 − tsdmax−

(

∑(m,n)∈A|tm<ti ,tn=ti

(

tdn − tg

1 ·hn− tg2 · (1−hn)

)

·ykmn

)

+M ·yki0 ≤ M,

∀i ∈ NL,k= 1, . . . ,cmax

(4.28)

tai + tg

0 − tsdmax−

(

∑(m,n)∈A|tm<ti ,tn=ti

(

tdn − tg

1 ·hn− tg2 · (1−hn)

)

·ykmn

)

+M ·yki j ≤ M,

∀(i, j) ∈ A, i ∈ NL, t j > ti,k= 1, . . . ,cmax

The notation is described as follows (D. C. Flórez et al. [20]): “The setNL = N|0,1,2 is comprisedof nodes that represent legs from the flight schedule whereasthe setNc

L is its complement (basenodes).”

For a pairing;tspmax is the maximum service time

t f pmax is the maximum flying time

l pmax is the maximum number of landings


For each duty;dmax is the maximum number of duties in a pairingtsdmax is the maximum service time

t f dmax is the maximum flying time

ldmax is the maximum number of landings

T is defined as the minimum rest time between consecutive dutiesD is the set of days of the week.

“ For each leg associated to nodei ∈ NL, let ti, oi, di , l i , tdi , ta

i , t fi , tg

i , be the day of the week,origin city, destination city, number of landings, departure time, arrival time, flying time, andground time associated with the airport of the destination city, respectively. It is assumed thattdi = ta

i = t fi = l i = 0, for all i ∈ Nc

L.”

Furthermore;

hi =

1 if the flight represented by nodei ∈ NL begins in a national city0, otherwise,

r iq =

1 if the flight represented by nodei ∈ NL belongs to dayq∈ D,0, otherwise.

“Finally, let tgi (i ∈ Nc

L) be the debriefing time(i = 0) and briefing time at a national(i = 1) andinternational city(i = 2).”

Pairing limits and duty flying time are given by constraints sets (4.29)-(4.30) respectively.

∑(i, j)∈A

t fi ·y

ki j ≤ f t p

max, ∀k= 1, . . . ,cmax (4.29)

∑(i, j)∈A

t fi · r iq ·y

ki j ≤ f td

max, ∀q∈ D,k= 1, . . . ,cmax (4.30)

Constraints (4.31)-(4.32) limit the number of landings forpairings and duties.

∑(i, j)∈A

l i ·yki j ≤ l p

max, ∀k= 1, . . . ,cmax (4.31)

∑(i, j)∈A

l i · r iq ·yki j ≤ ld

max, ∀q∈ D,k= 1, . . . ,cmax (4.32)

4.4. SOLUTION SUGGESTION FOR AN EXTENDED SIMULTANEOUS AIRCRAFTROUTING AND CREW SCHEDULING MODEL 53

Now that all the constraints are set up, this model becomes advanced enough to generate a feasibleair-traffic schedule, however solving this model becomes a complicated task. This model will notbe solved in this report, it is not assumed impossible although not within the reach of availablemeasures and lack of time. A solution method will however be discussed next.

4.4 Solution suggestion for an extended simultaneous aircraftrouting and crew scheduling model

This section will discuss a possible solution method to a combined model of the methods describedin the previous section. The purpose of which is to render thereader a brief comprehensive lookinto the many variables that need to be taken into account in air-traffic planning and the difficultyof reaching a complete solution.

This is a rather complicated model to solve, the sets involved can be significantly large andthe recommended way to solve these is through a branch-and-bound algorithm. This is a methodthat disregards countless amounts of fruitless solutions by setting upper and lower bounds andsystematically enumerating all possibilities. Dantzig and Wolfe decomposition, a column genera-tion method that solves linear programing problems, can be used to compute relaxed linear lowerbounds. The branch-and-bound method is a two part process: first,branchingmeans, splitting thelarge sets, i.e.Ω f , Ωk into smaller sets whose combined union equals the complete sets. This stephas a tree-structure since it has a recursive nature, where the nodes are the subsets of the large sets.Secondly,boundingstep computes the upper and lower bounds for the minimum value function ofa subset of eitherΩ f andΩk.

The basic idea is to solve a restricted master problem with a set of subproblems through aniterativecolumn generationprocess. This process starts by a set of artificial variables, ensuringthat the master problem is feasible during the initial iterations. Each iteration generating newvariables, through an shortest-path problem for each network (Gf ( f ∈ F)andGk(k ∈ K)), for themaster problem. Arc costs in the networks reflect the currentvalues of the dual variables (≥ 0)associated with the constraints of the restricted master problem. When new paths are added to therestricted master problem it is re-optimized and yields a new primal solution and new values for thedual-variables. Optimal solution is reached when there areno negative-cost paths identified, andthus the column generation process stops, see Dantzig-Wolfe decomposition algorithm structure inthe box below. Similar methods to this is the Benders’ decomposition algorithm, the difference isthat this method adds new constraints and is arow generatingapproach.

It is likely that the shear amount of constraints posed by this model, will require an excessiveamount of computing time. It is believed that although this problem offers a large amount of pos-sible connections, due to the restricting constraints a feasible solution might not be reached. Whenthe problem is broken down into either decomposition method, it becomes rather simple to solvewith straightforward simplex or revised-simplex methods.The models are built on an iterative

4.4. SOLUTION SUGGESTION FOR AN EXTENDED SIMULTANEOUS AIRCRAFTROUTING AND CREW SCHEDULING MODEL 54

process that yields no possible solution if all constraintsare not satisfied. This is is assumed to bethe plausible outcome when several restricting constraints are added.

Dantzig-Wolfe decomposition algorithm (E. Kalvelagen [21])

initializationChoose initial subsets of variables.while truedoMaster problemSolve the restricted master problemπ1 := duals of coupling constraints

π(k)2 := duals of thekth convexity constraint

Sub problemsfor k=1,...,Kdo

Plugπ1 andπ(k)2 into sub-problemk

Solve sub-problemkif reduced cost (pricing)< 0 then

Add proposed optimal values to the restricted masterend if

end forif No proposals generatedthen

Stop: optimalend ifend while

5

Analysis

From the onset of this project, the objective was to define andevaluate the needs of the planningdepartment at TUIfly Nordic. This process has been conductedfirst and foremost on an obser-vational basis. What has been brought to attention and discussed are parameters by which anair-traffic schedule can be evaluated, in order to obtain a good overall view of the efficiency andstability of the program. By evaluating parameters that areimportant for the planning process,future assessment for implementations to improve the planning process is made possible (by forinstance software support). The processes at TUIfly Nordic have been closely observed and sum-marized in the previous sections. This chapter will analyzethese observations.

This thesis, has been divided into two parts, one part addressing the needs of TUIfly in termsof processes and KPI and one part that describes the need of the company in technical terms. Indoing so, a mathematical model that could describe the traffic planning process was approached,one that would take in to account the numerous constraints ofplanning an air-traffic schedule. Thisis a large undertaking, and one that can not be approached in the period of time assigned for thisproject, thus restrictions have been taken and the examplesare kept on a small scale.

The difficulty in airline optimization is that there is nevera comprehensive way to describeevents that occur in the airline sector. This is an area whereprocesses are highly interdependentand where there are too many constraints that are of an uncertain nature to account for. Largecomputational processes are at work for even the most heuristic of models for optimizing any partof the airline sector.

This project has made an attempt to address what risks are involved in the traffic planningstage, and in what ways they are connected to one another. To begin assessing the needs of TUIfly,it was essential to find out the requirements of the Planning department. In order to do so, theinternal processes have been studied and described in sections 2-3. The current structure of thesoftware processes will be described in the next section 5.1, followed by a parameter study of theinterdependency that occurs when evaluating key performance indicators.

55

5.1. SOFTWARE NEED 56

5.1 Software need

The systems network as it currently functions today and the supposed need for a solution is repre-sented in figure 5.1. The system, in the figure referred to asSystem X, represents the connectivitynecessary to accommodate the needs which have been addressed (these will be explained shortly)whilst causing no interference to the systems operating today. The basic need is one that opti-mizes and evaluates the air-traffic schedules (or scenarios) according to severalkey performanceindicators(section 5.2).

Figure 5.1 Software need. Illustration: Marcus Karlsson additions made by ClementBerguerand.

Introducing a system like this is a complicated process and what is covered by the scope of thisproject will only explain the needs and requirements expressed by the planning department ofTUIfly Nordic. Complications are partially due to the company currently operating within twosystems. Systems that, when regarded separately, functionwithout difficulty but combined cur-rently prohibits their maximum performance and in turn doesnot entirely contribute to the benefitof the planning department. Thus, introducing a new system means one more connectivity solu-tion has to be provided without disrupting current processes. It is also not an option to start froma clean slate by using one complete system for all operations, as neither of the systems used today(RM5 and IDPS) are considered for suspension according to TUIfly Nordic (referred to the level of

5.1. SOFTWARE NEED 57

satisfaction and the investment put into the systems in operation as of today). Preferably the idealsituation would be to find a solution offered by the manufacturers of the current systems, IDPS orRM5. This would make integration simpler and is also assumedto be more cost effective.

The viewpoint of this project is one that regards the air-traffic scheduling as the focus pointof all planning operations. The pre-planning needed to generate a flyable and flexible air-trafficschedule is momentous. This is mainly the reason why airlineoptimization is so difficult, and why,after nearly 60 years of studies in the field (J. L. Snowdon et al. [11]), a more beneficial operatingsystems for the airline business has not yet been generated.

The parameters (e.g. crew rotations, maintenance planning, slots, legality, FDP (Flight DutyPeriod) ), that need to be taken into account from the very beginningof the planning process, createa situation where it is hard to retain an overview. The needs expressed by TUIfly Nordic are onesthat stem from Arsis not being useful enough in terms of generating scenarios and evaluating theseseparately. There is also an issue of module interconnectivity mentioned in the Arsis section 3.1.1where even if scenarios can be generated these can not be usedin the live module.

Presently, there is a rather limited possibility to determine whether an air-traffic schedule isefficient or stable other than by comparing changes made fromprevious season schedules. There isa need to control therisksinvolved in planning a traffic schedule while also being ableto determinethe costs behind the actions of long term planning; which means that for instance if a maintenancestop is pushed to the last minute, what profits are gained fromthe aircraft being in operation forslightly longer, what are the safety risks, is the schedule more efficient or robust that way? Thereare many questions, and this example only regarded one parameter being changed, there are severalother parameters, some presenting less risk when shifted and others like the maintenance scenarioa higher risk factor.

What becomes apparent is thatSystem Xneeds to be able to create scenarios and from thesegenerate reports assessing how the changes in KPI (Key performance indicatorssee section 5.2)affect the schedule in terms of efficiency, cost, safety, flexibility and stability, this will in turn allowthe company to create a risk assessment model. The system will have to be able to communicatewith Arsis for the execution of the preferred scenarios while also being able to communicate withRM5 to accommodate for crew processes. To further explain what System X is, and what it ismeant to perform, the next section will describe the key performance indicators that help evaluatethe air-traffic schedule over different important categories, e.g. flexibility, cost.

System Yrepresents a report tool, one that can generate reports disclosing costs for the crewrelated processes. This tool can be used to decide the price for a pairing and how to divide the costthat incur. The setup for this system was not studied as deeply, due to time constraints and due toprioritizing the need to define the purpose of System X.

5.2. KEY PERFORMANCE INDICATORS 58

5.2 Key performance indicators

Key performance indicators, are parameters by which the air-traffic schedule can be evaluated.These KPI range from the simple time table parameters such ashow many rotations an aircraftdoes over a period of time to market driven factors, e.g. arrival and departure times. These KPI arerisk factors, that can be ranked according to the effect theyhave on the air-traffic schedule.

Determining important key performance indicators can be difficult since there are so manyfor the air-traffic scheduling process. The difficulty is also due to the opposing nature of theseindicators and the interdependency among them, which will be discussed next.

At TUIfly Nordic, these KPI have been discussed for years, which is not an uncommon sit-uation in the airline business. The following key performance indictors, seen in table 5.1, wereidentified by the Planners at TUIfly Nordic during the course of this project. The table representsthe first attempt to address the needs and requirements for which System X will satisfy. Table 5.1

Traffic schedule scenarios

T1 T2 T3 T4

FlexibilityStability:

Robustness

Planning parameters∗ 60% 85% 5% 34%

Slot: #HistoricTotal 56% 35% - 6%

#FDP sensitive flights 8% 10% 67% 40%

∑i Stopi×Lengthi#Rotations 42% 50% 34% -

Cost:

#BLH×#RotationsProduction daystot

65% 30% 4% 12%

#Ferry flightsRotations 1% 60% 78% 9%

#Positioning flights (in cycle)Rotations 56% 8% 25% 10%

Price 2.3 MC 5.6 MC 1 MC 3.3 MC

Customer:Market driven

Arrival & Departure (Time).Days.

30% 50% 20% 80%

∑Total: 318 328 233 191

Table 5.1Key Performance indicators. The used values are illustrative and have no reflection onTUIfly Nordic.

illustrates how four feasible and flyable traffic scenarios are suggested and evaluated according tothe KPI in order to determine which one corresponds best to the over all need (or simply which oneis preferred). This method is also useful when presenting the impact of any changes to the traffic

5.2. KEY PERFORMANCE INDICATORS 59

schedule when conflicting opinions occur in terms of accommodating a change, this way allows allparties to get a clear view of the consequences, whether it isto the economy or the stability of theschedule. The percentile values are fictitious, they are a visual representation requested by TUIflyNordic.

Some of these indicators are simpler to identify than others. The simpler ones can be used toassess an air-traffic schedules efficiency from one season tothe next. These have been groupedinto a category namedplanning parameters, these are integer variables that are easily countable.

Planning parameters∗:#rotations/aircraft#over-night stays/slip pattern#production days/slip pattern#three pilot positioning/slip pattern#ferry flights/rotation#crew requirement/rotation#home-base stops/rotation% FDP sensitive flights(where # stands for “amount of”)

Most of these indicators are not directly measurable with current software. Some can be calculatedmanually which is tedious even for a two week scenario. A simple schedule, for a three dayperiod with three types of aircraft, is illustrated in figure5.2, this image also visualizes some of theterminology used.

Figure 5.2 Simple flight schedule example. Illustration: Mercedes Inal.

Deciding what key performance indicators to focus on is a bigstep. The next step is deciding how

5.3. RISK MANAGEMENT 60

to rank them in terms of risk, the severity and the probability of each KPI (further reading in sec-tion 5.3). Ranking these parameters is the first stage in assessing the effects of any relative changeto a flight schedule. When parameters are laid out as they are in table 5.1, other than a clear under-standing in what factors matter when building an air-trafficschedule, the table doesn’t conway anyhard facts, there are no numbers or values to base a decision upon. While the planning parameters,mentioned earlier, are straightforward and basically countable, some of the other parameters arenot as easy to fix with a number (like the market driven parameters or the FDP sensitive flights,Flight Duty Period). Deciding in what way to order these KPI will be a subject forfuture research,however a simple methodology will be suggested in the next section.

For future reference this will allow TUIfly Nordic to find a method of how to measure theseparameters efficiently and create a database of their own forstatistical analysis of schedules forcoming seasons. Furthermore, having found these KPI presented a new problem and an additionto this project,risk management. This will be discussed in the next section (5.3).

5.3 Risk management

This section will describe a risk management approach to theTUIfly Nordic problem statement.Key performance indicators are not always easy to measure asmentioned previously. When opti-mizing one parameter (e.g. arrival/departure time, maintenance times, turn around time) it tends toaffect several others, some to the positive but most often tothe negative as the parameters usuallycause conflict. After setting up important KPI at TUIfly Nordic (table 5.1), a discussion was en-gaged regarding the measurability of these indicators, andwithin what ranges values are to be setfor each parameter.

Since there are no assessments made over previous schedules, other than for increase (a per-centile calculation) in the planning parameters mentionedin the previous section, and no evaluatingmeasures in Arsis, beginning assessment of these KPI becamehard. Therefore much of this prob-lem resonates in not having the necessary tools (system support) to ascertain whether or not aschedule is optimal. And optimality is decided on hard numbers, which is also a feature missingform the software today. A simple methodology has been discussed, where each KPI connectedparameter is ranked according to the risk value they impose:

FDP Maintenance

Risk value: Risk value:1 if 0-20% 1 if 0-10%2 if 20-40% 2 if 10-30%3 if 40-60% 3 if 30-60%4 if 60-80% 4 if 60-90%5 if 80-100% 5 if 90-100%


This is just an example, again the values do not represent TUIfly Nordic, each independent param-eter must be assessed and risk values for the impact of other parameters on to it must be evaluatedso that a system of risk situations can be presented.

How the Planners set about to construct an air-traffic schedule is by most often using previousseasons schedule and rearranging it to fit demands for the coming season. The problems beginwhen trying to incorporate larger changes into an old season(i.e. new aircraft, new destinations,increase in rotations etc.) and relying on experience when doing so. It is hard to incorporate allparameters that make up for a feasible flight schedule in a mathematical model, it is even harderwhen doing so manually. The Planners have to limit themselves in order to begin implementingnew changes. At TUIfly Nordic, these limitations can be categorized into three main groups, seefigure 5.3. These three categories cover three major areas concerning crew (FTL), maintenance

Figure 5.3 Risk parameters. Illustration: Mercedes Inal.

and aircraft rotations. Seen in the illustrations are sub-parameters to each of the three major ones,these sub-parameters are all affected, to some extent, by any changes to the three major parameters.The planning department wants to determine therisk caused by a change in such parameter, e.g.cost implications, stability and efficiency.

This problem resonates in risk management. The risk assessment equation (or simply the riskequation) is a function:

Risk= f (Threat, Vulnerability, Asset). (5.1)

This is a probability function stating thatrisk is the probability that a threat will exploit a vulner-ability to cause harm to an asset(JISC infoNet [22]). Figure 5.4 (JISC infoNet [22]), illustratesa risk assessment model. This is a common model, frequently used to describe risk management.This model is adapted to the purpose of this project, statingthat after risk parameters are identified(i.e. figure 5.3), they are to be analyzed qualitatively and quantitatively in order to plan an appro-priate response followed by monitoring and controlling theeffects of the response. This means to


Figure 5.4 Risk assessment model [22].

clarify what steps are to be taken, to identify the magnitudeand severity of the risks involved inair-traffic planning, and act according to certain risk management actions.

When assessing risks, there are often statistics to go by in terms of how to define the severityof a risk. A simple model, one that is commonly associated with equation (5.1) is the followingequation (5.2), although the terminology can vary depending on the writers preference.

Risk= Severity∗Probability. (5.2)

This equation will be the definition of the typical risk management action model illustrated infigure 5.5. Assessing the risk concerning changes in key performance indicators is a large un-dertaking. Assessment of risks involved are coloured greatly by the Planners views and opinionsand some parameters are connected to others in such way that it is hard to assess the severity andprobability of them occurring (e.g. how does one put a value on union agreements?). Since therehasn’t been any incorporated report generating tool in the IDPS system for simple planning param-eters, at TUIfly Nordic, statistics are hard to come by. Measuring the stability and optimality of anair-traffic schedule has not been a possibility so far.

Example:The following example will illustrate how the risk management actions seen in figure 5.5 can beapplied. There are several flights that are planned with a zero margin between maximum FDPand planned FDP, these flights are FDP sensitive for long haulflights for instance, but they are stillregarded as an acceptable risk, putting them in the lower left box(1x1) of figure 5.5, however it alsofits into the upper right box(3x3), representing a high risk situation since even small disturbances(e.g. delays) might break the FDP limitations and drive a need to implement re-planning measures.So for instance the simple method of ranking can be taken a step further (for FDP only in thiscase):

5.4. MULTIOBJECTIVE OPTIMIZATION 63

Figure 5.5 Risk management actions. Illustration courtesy of Google,reworked by MercedesInal to fit TUIfly Nordic.

FDP

Risk value: Risk management action(according to figure 5.5)

1 if 0-20% (1x1)2 if 20-40% (1x2) & (2x1)3 if 40-60% (1x3) & (2x2) & (3x1)4 if 60-80% (2x3) & (3x2)5 if 80-100% (3x3)

This is only a presentation of how it is possible to go about the given problem, it will be a case forfuture studies.

5.4 Multiobjective optimization

This section will provide an discussion surrounding the complexities of creating a mathematicalmodel suitable for the risk parameters mentioned above. It was mentioned earlier that the Plannerscould group parameters that they need to keep track on into three main categories. These threecategories contain several parameters, and these three categories will sometimes cross effect. Forsimplicity these three categories can be classed as functions.


Optimizing a function with certain decision variables is generally very simple, an example isgiven in equations (5.3), by using the simplex method this will yield one optimal solution.

minimize f (x) = cn

∑j=1

x j

subject ton

∑j=1

a jx j ≤ c j (5.3)

x j ≥ 0

for all x∈ X

However when you have several functions that are interdependent of each other, like the risk pa-rameters in figure 5.3, then optimizing becomes difficult. A simple multiobjective function is givenin equation (5.4).

minimize F(x) = ( f1(x), .... fm(x))

subject tox∈ X (5.4)

The notation used is as follows,X is the decision space,Rm is the objective space, andF : X →Rm

consists ofm real-valued objective functions.

The difficulty of optimizing such a function resides partly in that it is made up of smaller func-tions, meaning each variable is in fact a function which depends on its own respective variables.The problem described in the previous sections of this chapter is such that if among a set of choices,where there is a way of valuing each choice, then these can be graphed and the optimal choice canbe picked out. This problem will be defined as in equation (5.5). The objective is to minimizeeach function, since it is for the time being assumed that small values are favorable, but it is alsoassumed that these functions have a contradicting nature, say for instance iff1(x) should be strictlydecreasing andf3(x) should be strictly increasing, which inevitably causes a conflict.

minimize f1(x)+ f2(x)+, ...,+ fm(x)

subject tog(x)≥ 0 (5.5)

x∈ X

Here the constraints are represented by a functiong(x). A geometric interpretation is provided, asthis chapter will not go further into proofs or how to solve such an optimization. The data setM,equation (5.6), represents the countless points that couldbe produced by equation (5.5).

M = ( f1(x), f2(x), .., fm(x)) | x∈ X ⊂ Rm (5.6)

Figure 5.6 is a representation of a smaller time space, whereequation (5.6) illustrates a setM whichcontains a very large number (finite) number of points inR

2. The horizontal axis displaysf2(x)


and the vertical axisf1(x).

M = ( f1(x), f2(x)) | x∈ X ⊂ R2 (5.7)

Figure 5.6 Marginal allocation example. Illustration: K. Svanberg [23].

The convex line created, in the figure represented by the blueline encircling the setM, is theeffi-cient curveand the points that lie on this curve areefficient points. These points will be theefficientsolutionsof a minimization off1(x)+ f2(x). The image shows that given a specific situation a so-lution will be decided with whichever point fits the circumstances best, this is illustrated by theisocost lines(dashed lines) in the graph.

A few methods were considered for explaining this problem mathematically, the two chosenare amarginal allocationapproach K. Svanberg [23] andpareto curves., both of which achieve thesame results. Vilfredo Pareto was an Italian economist thatis behind the term Pareto optimality, orefficiency. This is a common concept in economics, it is an allocation method of choices or goodsamong any set of which these concern.

6

Discussion

6.1 Checklist for minimum software performance

In this section a list has been produced that covers the minimum requirements that the Planningdepartment at TUIfly Nordic has put forth. New software must hold up to certain demands in orderto be worth investing in. The list addresses some key software performance demands that are notcurrently available through Arsis. These demands are not listed in any order of importance, if newsoftware should be worth evaluating then the listed demandscan be used as a checklist for furtherresearch.

1. Interconnectivity with current systems

• IDPS connectivity, Arsis compatibility

• RM5 compatibility

2. Possibility to change parameters

• Adding parameters and values in-house, without the need tocall support

3. Report generating options

• Large variety of report styles

• Diagrams/Charts

• Comparability

• Excel compatibility

4. Optimization system

• Different optimization solutions for multiple scenarios

66

6.2. CONCLUSIONS FOR THE NEEDS OF TUIFLY NORDIC 67

• Ability to separately optimize partial stages in the process i.e. maintenance, crew,price/rotation

5. Running times

• Analysis time (XX min)

• Produce reports (XX min)

6. Internal processes

• Importing information to the program that analysis’s depend on i.e. fuel costs, con-tracts, leases, cleaning.

• Implementation time, how long will it take to incorporate the system into the existingwork process

• Training for usage

7. Price

• Per license

• User fees

• First time cost i.e. start up cost, training, installation& support

8. User friendliness

9. Support

6.2 Conclusions for the needs of TUIfly Nordic

In this paper, the air-traffic planning process for TUIfly Nordic has been studied and documented.The study is based mostly on observations and these observations have been supplemented byliterature research in order to create a model that fits the scheduling processes at TUIfly Nordic,incorporating some of their current planning processes. This model tries to explain the difficultiesin solving a large scale optimization problem that describeseveral sub problems that are sometimeseasy to solve when studied separately but combined creatinga complex problem. This paper alsoprovides a background into airline optimization and explaining key variables that make up a flyableflight schedule.

It has been mentioned that charter airlines are rarely covered in literature, research often coversregular flights as it is simpler to model flights when there is adaily routine with a set frequency ora repeating schedule yearly, making the steps described in Chapter 5 possible to follow in termsof order. Literature available tend to use American airlines more often than the European ones, asthese have different rules and regulations not easily modeled.

6.3. FUTURE RESEARCH 68

The needs of TUIfly Nordic have been extensively described through out this paper and it isnot entirely simple to sum up the needs of the Planning department. It was of great importanceto determine the key performance indicators, parameters toevaluate and compare different scenar-ios of air-traffic schedules. These KPI had not been decided previously and was thus regarded asprogress and an opportunity for further research in the future. Establishing KPI also opened upnew discussions, as there has only been limited possibilities available to compare the air-trafficschedules for different seasons there was therefore also a need to determine how to set up waysto measure each of these KPI. The conclusion of this analysishas been that the basic need is onethat stems in risk management. How to rank key performance indicators and how to optimize ac-cording to these parameters while being able to generate active reports for evaluation and scenariocomparison.

There was a question as whether to implement new software, where the need was defined asa system that simplifies the building of traffic scenarios andgenerates reports while establishinga link that works between the systems currently in operation. While the system need might seembasic, it is however not a simple need to meet, there is currently no such program available. Somesystems available today areSabre®Rocadefrom Sabre Airline Solutions (worldwide large-scalesystem), Jeppesen and Navitaire all of which offer a wide range of solutions for airline operationsand recovery. For small and even medium sized airlines thesesystems are cost prohibitive leavingroom for may in-house developed solutions for airline operations and optimization.

Introducing a new system for TUIfly Nordic was found to be largely unnecessary opting forfurther research and development of the systems currently in operation. This way continuity is es-tablished throughout the planning process while also simplifying the necessary support provisionsand personal training, since there is already a support system established for the current programs.Improvements to the airline operation systems are constantly made, and renegotiations about re-visions to the license agreements in order to include support for new developments can be lookedinto.

6.3 Future research

This project only scratches the surface of the needs for TUIfly Nordic. There are a few areas whereefforts can be put into for future research that can provide an improvement to the processes today.These areas are the RM5 system and the module ARP, an in program module or an applicationthat optimizes the air-traffic schedule is in development. Furthermore, and a much larger project,is to look into the IDPS systems and Arsis, there is currentlyno option to import files into Arsis.An issue that is the main reason for why schedule building is made in Arsis today and not inARP. Had it been possible to import files into Arsis, a continuous planning process chain wouldbe accomplished through the RM5 system, requiring only a schedule input into Arsis to establishcontact with Opscon and Airpas.

6.3. FUTURE RESEARCH 69

There is a need to research how to rank the KPI according to thelevel of their relative impor-tance and the risk they impose onto a flight schedule. This canbe done partially by establishinghow they are interconnected and how they vary when penaltiesare set and partially by their ownindividual importance. Again this is something that has to be reviewed and where only extensivedata gathering could yield a possible solution.

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Appendix A

Table 1 represents the data used to produce the diagram in figure 2.3. The table contains figures thatrepresents future production and are thus sensitive material belonging to TUIfly Nordic (©TUIflyNordic).

72

BIBLIOGRAPHY 73

Legs/Aircraft

Year Month Boeing-737 Boeing-747 Boeing-757 Boeing-767

2010

January 196 221 203February 164 199 191March 199 223 179April 177 155 85May 210 129 98June 342 229 95July 358 243 101August 352 243 102September 333 218 97October 293 235 91November 251 230 96December 246 26 237 171

2011

January 258 46 222 195February 240 44 209 179March 277 38 230 169April 260 132 79May 386 92 92June 557 90 93July 661 100 98August 647 94 97September 589 86 90October 516 61 87November 411 109December 406 154

2012

January 415 190February 399 191March 424 207April 287 115

Table 1 Legs flown by aircraft type per month.

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