Airline planing and marketing

8/7/2019 Airline planing and marketing

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Optimization inAirline Planning and

Marketing

Institute for Mathematics and ItsApplications

November 2002

Barry C. Smith


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Overview

Airline Planning and Marketing

Landscape

Applications of Optimization Modeling

Planning and Marketing Integration

Unsolved and Under-solved Problems

Future Outlook


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Airlines Make Money Only When TheyMatch Supply and Demand


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Major US domestic carriers: Operate 5000 flights per day Serve over 10,000 markets Offer over 4,000,000 fares

Schedules change twice each week

On a typical day, a major carrier will change100,000 fares

Airlines offer their products for sale more than one year inadvance

The total number of products requiring definition and control isapproximately 500,000,000

This number is increasing due to the proliferation of distributionchannels and customer-specific controls

The Problem is Large and Dynamic


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Enterprise Planning Product Planning

Tactics and

Operations

Effective Planning and Marketing is aContinuous Process


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DecisionsDecisions

There Should be Continuity

Route StructureRoute Structure FleetFleet MaintenanceMaintenance

BasesBases

Crew BasesCrew Bases FacilitiesFacilities

ScheduleSchedule Fleet AssignmentFleet Assignment Pricing PoliciesPricing Policies

PricePrice RestrictionsRestrictions AvailabilityAvailability

TimeTimeHorizonHorizon

18 Months +18 Months + 18 Months 18 Months 1 Months1 Months

3 months 3 months DepartureDeparture

ObjectiveObjective Maximize NPVMaximize NPVof Future Profitsof Future Profits

Maximize NPVMaximize NPVof Future Profitsof Future Profits

Maximize NPVMaximize NPVof Future Profitsof Future Profits

ConstraintsConstraints FinancialFinancialResourcesResources

RegulationRegulation

Route StructureRoute Structure FleetFleet MaintenanceMaintenance Crew BasesCrew Bases FacilitiesFacilities

ScheduleSchedule Pricing PoliciesPricing Policies


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Significant Optimization Applications

Tactics and OperationsYield Management

Product Planning

Fleet Assignment


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Yield Management Objectives

Sell the right seat

To the right passenger

At the right price


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YM is Essential to Airline Profitability

Annual benefit of Yield Management to amajor airlines is 3% 6% of total revenue

A major airlines revenue benefits from yieldmanagement exceed $500,000,000 per year

Applying this rate to the industry ($300billion/year) yields potential benefits of $15billion per year

The possibilities for even the most

sophisticated carriers go well beyond what isachieved today


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YM Controls

Overbooking

Revenue Mix

Discount allocation

Traffic flow

Groups


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Yield Management Evolution

High Value Low Value

1970s:ClassCode

Rev +4%3 MM

1960s:

Overbooking

Revenue+2%,300k

Full Fare

DeepDiscount

Value of Last Seat

Class

Code

Origin-Destination Market


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Revenue Mix Problem Flight Leg

Stop selling Current (low-value) products when:

Profit (Current) < Profit (high-value) * P (Sell out)

Sell to Current Customer

Hold for

Higher-ValueCustomer

Sell outHigh-Value Profit

Unsold Product

Current Profit

$0


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1970s:ClassCode

Rev +4%3 MM

1980s:ODRev +5%

30 MM

1960s:

Overbooking

Revenue+2%,300k

Full Fare

DeepDiscount

Value of Last Seat

Class

Code


1990s: BidPrice

Rev +6%

1 MM


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Revenue Mix Problem Network

0

),(

:Subject to

Re*Max

=

sAllocation

CapacityPax

DemandAllocationfPax

vPax

ODF

FlightODF

ODFODFODF

ODF

ODFODF


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Passengers = f (Allocation, Demand)

= 0

> 0

Mean Demand

Pass

enger

sCarried

Allocation


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Revenue Mix Approaches

Deterministic Leg Allocations (wrong)

Stochastic LegAllocations (BA, MIT)

Deterministic Network Allocations (wrong)

Stochastic Network Bid Price (AA)

Deterministic Network

EMSR

VN Allocations (MIT)

Stochastic NetworkADP on Leg Bid price(Columbia)

ADP on Network Real-time evaluation (GIT)


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2000s:Mult.

Channels

CRM



1970s:ClassCode

Rev +4%3 MM

1980s:ODRev +5%

30 MM

1960s:

Overbooking

Revenue+2%,300k

Full Fare

DeepDiscount

Value of Last Seat

Class

Code


1990s: BidPrice

Rev +6%

1 MM


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Fleet Assignment FAM

Fleet Assignment Models (FAM) assign aircraft types to anairline timetable in order to maximize profit

FAM is widely used in the airline industry AA and DL have reported 1% profit margin improvements

from FAM

Given a flight schedule and available fleet of aircraft,FAM maximizes operating profit subject to the followingphysical and operational constraints: Cover: Each flight in the schedule must be assigned

exactly one aircraft type

Plane Count: The total number of aircraft assignedcannot exceed the number available in the fleet Balance: Aircraft cannot appear or disappear from

the network


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Basic FAM Formulation

0}1,0{

)(,,

0

1

)(

,,,

, ,

,,,,,1,

,0,

,

,,,

=+

=

staaf

s

tsArrivalf

tsDeparturef

staafafsta

Stationssasa

Aircrafta

af

afAircrafta Flightsf

afaf

yx

circularTimestStationssAircrafta

yxxy

AircraftaPCy

Flightsfx

toSubject

CRxMax


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FAM Extensions

Time windows (US, MIT) Integration

Routing (UPF, MIT, GIT)

Crew (Gerad) Yield Management (MIT, LIS, Sabre, GIT)


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Leg Revenue Modeling Approaches

Average passenger fare: Inconsistent with yieldmanagement practices. As capacity is added,incremental passengers have lower average revenue.

Leg revenue: Modeling passenger revenue on a flightas a function only of capacity on this flight assumes

that there is no upline or downline spill These assumptions create inconsistencies with

subsequent airline marketing processes, in particularO&D yield management, and tend to bias FAMsolutions to over-use of large aircraft


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Improving Revenue Modeling in FAM

Allocations For each flight leg allocate space to each passenger path

Piecewise linear approximation for traffic/revenue oneach path

Solve the OD YM model inside of FAM

Model size explodes -- There are 150,000-500,000passenger paths in a typical problem for a major carrier

Decomposition

Solve yield management model outside of FAM

Incorporate model results into FAM


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Integration of FAM and YM

FAM

Capacity

YM

CapacityBid Price

Revenue Function Approximation:


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Revenue Function Approximation:One Leg, One Cut

Revenue

($US)

Leg Capacity (No. of Seats)

Bidprice, ($/seat)

CAPj

R0

R0 f fC A Pf R C A Pf


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OD FAM Master

CutsODi

:andBalance,Count,PlaneCover,

,0

,,

+

Flights f Aircraft aTotalafa

if

i

afAircrafta Flightsf

afTotal

RxCapR

toSubject

CxRMax

Revenue Function Approximation:


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Revenue Function Approximation:One Leg, Multiple Cuts

Revenue

($US)

Leg Capacity (No. of Seats)CAPj

R0

)(*0 fffi

fi

CAPRCAPR +


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Planning and Marketing Integration


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Enterprise PlanningEnterprise Planning Product PlanningProduct Planning

Tactics andTactics and

OperationsOperations

Ideal Planning


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Planning & Scheduling

Pricing

Yield Management

Distribution

Customers

Planning Reality

Sales


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Airline Pricing

Simple Concepts Relatively fixed seat

capacity

High fixed costs

Combination of elastic andinelastic market segments

Complex Reality Oligopoly market behavior

Multi-period repeated trial

Strategy is generallydominated by mechanics(tactics)

The pricing process is oftenunclear to airline executives


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On Tariff -- GDS

AL.com

Distressed Inventory

TA.com

Res Office

FFP Burn/Earn

CorporateTour/Cruise/Cons

Partners

Sales and Distribution:Multi-channel

Airline

Capacity

Forecast of

Demand and

Free Market

Value

Customers


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Planning & Scheduling

Pricing

Yield Management

Distribution

Customers

Bid Prices Support Integration

Sales


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Unsolved and Under-solved Problems

Opportunities Enterprise Planning

Facilities Manpower Fleet

Longitudinal Planning Alliance Optimization

Customer RelationshipManagement

Robust Planning Demand Operations Competition

Support for Labor Negotiations

Supporting Models Customer Behavior

Modeling

Simulation

Airline

Alliance

Industry

Scenario Analysis


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The Evolving Environment

Distant Past: Airlines initiated development of optimization-based systems

Recent Past: Following deregulation of the US domestic industry, airlinessupported technology development

Technical leadership shifted from airlines to academics, consultants andsoftware providers

Current: The current market conditions have reduced the ability of major UScarriers to support significant new development

Future: The marketplace for new optimization applications will bedominated by the requirements of the emerging carriers low-cost,alternative business models

Simple

Flexible

Developed outside of the carrier

Operated outside of the carrier


35/35

Optimization inAirline Planning and

Marketing

Institute for Mathematics and ItsApplications

November 2002Barry C. Smith

Airline planing and marketing

Documents