Airline Crew Scheduling ProblemAirline Crew Scheduling Problem (CSP)(CSP)

Ahmad Khayer DastjerdiAhmad Khayer Dastjerdi

81257128125712

ReviewReview

IntroductionIntroduction Scheduling ProcessScheduling Process Airline Crew Scheduling ProblemAirline Crew Scheduling Problem ModelModel Solution ApproachesSolution Approaches Case StudyCase Study

Introduction:

• Airline crew scheduling is a well-studied field in OR.

• Second cost-relevant factor after fuelfuel

• Misunderstanding and disharmonies among the crew : negative effect on the airline's customers.

• Highly competitive global market

Hackman JR(2002) Leading Teams : setting the stage for great performance,Ailine crew scheduling from planning to operation (Claud p. Medard and Nidhi Sawhney)

Dominant driving forces for airline companies :

• Cost MinimizationCost Minimization

• Customer SatisfactionCustomer Satisfaction

Other important fields: • Recruiting• Training of staff• Employee satisfaction

1)Hackman JR(2002) Leading Teams : setting the stage for great performance 2)airlinecrew scheduling from planning to operation (Claud p. Medard and Nidhi Sawhney)

Introduction (Con’d)

Scheduling Process:

-The main business of an airline is to offer and execute flights in such a way that profitprofit is maximizedmaximized

-Underlying scheduling tasks that need to be fulfilled: • Which flights are offered. • When and how often such flights should take place. • Which resources are allocated at which scheduling step. • How the schedule execution is controlled and unpredictable events are

handled.

1) Branch-and-Price Column:Generation for Solving Huge Integer Programs. Barnhart C, Cohn AM, Johnson EL, Klabjan D, Nemhauser GL, Vance PH (2003)

Block and ground

time estimation

Ground operation scheduling

Operational time rescheduling

Demand estimation

Network planning

Capacity planning

Fleet Assignment

Aircraft routing

Flight Scheduling

CrewScheduling

Physical aircraft scheduling

Branch-and-Price Column:Generation for Solving Huge Integer Programs. Barnhart C, Cohn AM, Johnson EL, Klabjan D, Nemhauser GL, Vance PH (2003)

Scheduling Process

Airline Crew Scheduling Problem (CSP):

• The largest scheduling problem• Its task is to assign all flights of a given timetable together with further activities to a limited number of crew members stationed at one or several home bases.

Airline Crew Scheduling: State-of-the-Art (BALAJI GOPALAKRISHNAN, ELLIS. L. JOHNSON 2005)A partially integrated airline crew scheduling approach with time-dependent crew capacities and multiple home bases(Yufeng Guo ,Taieb Mellouli, Leena Suhl, Markus P. Thiel 2005)

Basic definitionsBasic definitions::

A flight leg: is a non-stop air transit from a departure airport to its corresponding destination airport.

A flight duty: is a series of flight legs that can be serviced by one crew member within a workday (24 hours) .

A transit: occurs if the crew members' time-dependent location does not equal to the next scheduled location.

A pairing: which starts from and returns to the crew member’s home base. Its maximum duration is limited by a given upper bound, e.g..five working days.

Pre-scheduled activities: like vacation, simulation, training; medical examinations, etc. represent activities that a crew member has to undertake without exception.

A roster or line-of-work (LOW): represents a potential crew schedule for a dedicated crew member of the planning periods of usually two or four weeks.

Branch-and-Price Column:Generation for Solving Huge Integer Programs. Barnhart C, Cohn AM, Johnson EL, Klabjan D, Nemhauser GL, Vance PH (2003)A Network Flow Approach to Crew Scheduling based on an Analogy to a Train/Aircraft Maintenance Routing Problem. ( Mellouli 2001)

Mellouli 2001 , Barnhart et al. 2003

Roster Example

The Crew Scheduling ProblemThe Crew Scheduling Problem::

Crew Pairing Problem (CPP)Crew Pairing Problem (CPP) Crew Assignment Problem (CAP) or airline Crew Crew Assignment Problem (CAP) or airline Crew

Rostering Problem (CRP)Rostering Problem (CRP)

Airline Crew Scheduling From Planning to Operations(Claude P. Medard Nidhi Sawhney), Barnhart et al., 1999

min pPp

p xc

Pfp

px:

1 Ff

1,0px Pp

Model for crew pairing problemModel for crew pairing problem::

Airline Crew Scheduling: State-of-the-Art BALAJI GOPALAKRISHNANELLIS. L. JOHNSON(2005)

AnAn exampleexample::

Suppose an airline has three planes based in AtlantaSuppose an airline has three planes based in Atlanta

The second plane flies between Atlanta and New York The second plane flies between Atlanta and New York

The third plane goes on a Atlanta, New York, Memphis, AtlantaThe third plane goes on a Atlanta, New York, Memphis, Atlanta

Up to Up to 44 hours: hours: 7575% base rate% base rate

44 to to 88 hours: hours: 100100% base rate% base rate

Above Above 88 hours: hours: 200200% base rate % base rate

AABBCCDD�ٍ�ٍEEFF

Atl -- MiaAtl -- MiaMia -- AtlMia -- AtlAtl -- MiaAtl -- MiaMia -- AtlMia -- AtlAtl -- MiaAtl -- MiaMia -- AtlMia -- Atl

8:30-9:308:30-9:3010:00-11:0010:00-11:0011:30-12:3011:30-12:3013:00-13:00-14:0014:00

14:30-14:30-15:3015:30

16:00-16:00-17:0017:00

GGHHIIJJAtl -- N.YAtl -- N.Y.. N.Y. -- AtlN.Y. -- Atl Atl -- N.YAtl -- N.Y..N.Y. -- AtlN.Y. -- Atl

9:30-11:309:30-11:3012:00-14:0012:00-14:0014:30-16:3014:30-16:3017:00-19:0017:00-19:00

KKLLMMNN�ٍ�ٍOOPP

Atl -- N.YAtl -- N.Y.. N.Y -- MemN.Y -- Mem Mem -- AtlMem -- Atl Atl -- N.YAtl -- N.Y..N.Y -- MemN.Y -- MemMem -- AtlMem -- Atl

9:00-11:009:00-11:0011:30-12:3011:30-12:3011:45-14:0011:45-14:0014:30-14:30-16:3016:30

17:00-17:00-18:0018:00

18:15-18:15-19:3019:30

AB,CD,EF,GH,IJ,KLM,NOP for total cost 6.25ABCDEF,GHIJ,KLMNOP for total cost 6.00AB, CDIJ, GHEF, KLMNOP for total cost 4.75

Is there a cheaper combination Is there a cheaper combination availableavailable??

In order to be certain, we would In order to be certain, we would have to check all pairingshave to check all pairings

minks

Kk Ss

ks xc

k

pKk Ss

ks

ps nx

k

Pp

1

kSs

ksx Kk

1,0ksxKkSs k ,

Model for crew assignment problemModel for crew assignment problem::

Airline Crew Scheduling From Planning to OperationsClaude P. Medard Nidhi Sawhney,An integrated aircraft routing, crew scheduling andflight retiming model (Anne Mercier, François Soumis 2005)

SolutionSolution approachapproach::

Constructive heuristicsConstructive heuristics Mathematical ProgrammingMathematical Programming

Branch-and-BoundBranch-and-Bound

Branch-and-CutBranch-and-Cut Network-based Models Network-based Models Meta-HeuristicsMeta-Heuristics

Genetic AlgorithmsGenetic Algorithms

Simulated AnnealingSimulated Annealing

CaseCase studystudy::

(European tourist airline)(European tourist airline)

instanceinstanceManual Manual scheduleschedule(%)(%)

Autom. Autom. ScheduleSchedule(%)(%)

TotalTotal

SavingSaving(%)(%)

Hotel staysHotel stays10010076.2976.2923.7123.71

TransitTransit100100113.16113.16-13.1613.16

Hotel costHotel cost10010061.9361.9338.0738.07

Transit costTransit cost100100106.38106.38--6.386.38

Total costTotal cost10010088.5088.5011.5011.50

A partially integrated airline crew scheduling approach with time-dependent crew capacities and multiple home basesYufeng Guo ,Taieb Mellouli, Leena Suhl, Markus P. Thiel 2005

Thank you for your attentionThank you for your attention