planning and operating united airlines: business model and optimization enablers gregory taylor...

Planning and Operating United Airlines:Planning and Operating United Airlines:Business Model and Optimization EnablersBusiness Model and Optimization Enablers

Gregory Taylor

Senior Vice President – Planning

United Airlines

2

United’s Route Network Model

Air travel is dominated by thousands of small markets where total travel demand does not justify “point-to-point” non-stop flights

Western United States

Las Vegas (LAS)

Seattle (SEA)

Portland (PDX)

Eastern United States

Boston (BOS)

Albany (ALB)

Buffalo (BUF)

LAS

SEA

PDX

BOS

ALB

BUF

3


United has chosen a “Hub-and-spoke” model that maximizes number of markets served with given aircraft assets

ORD

LAS BOS

SEA ALB

PDX BUF

•This model provides several additional connecting options to the customers through Chicago (ORD)

•United is also able to carry local traffic between all six cities and ORD

Hub-and-spoke

4


In addition to the 59 passengers from the original three markets, 91 more passengers from six new markets were accommodated

In addition, United was able to carry 1600 passengers each-way between the six

cities and its hub, ORD

Daily local passengers volume

BOS-ORD

LAS-ORD

ALB-ORD SEA-ORD

BUF-ORD

PDX-ORD460

494

79292

99

176

Daily connecting passenger volume

BOS-PDXBOS-SEA

ALB-LAS

ALB-PDX

BUF-LAS

BUF-SEA

BOS-LAS

ALB-SEA

BUF-PDX

28

917

13

22

17

12

19

13

5

The Chicago Hub

Chicago Operating Statistics (Daily)

Number of cities served 125

Number of markets 7,800

Number of departures 1,015

Total passengers 42,300

Local passengers 22,000 (52%)

Connecting passengers 20,300 (48%)

United and United Express

6

The United System

System Operating Statistics (Daily)

Number of cities served 201

Number of markets 19,682

Number of departures 3,407

Total passengers 185,000

Aircraft 780

United and United Express

7

Overview of United’s Network Planning Automation Overview of United’s Network Planning Automation Suite - ZeusSuite - Zeus

8

United’s Scheduling Strategy

•Marketing strategy•Maintain market

share•Competitive response•Provide travel day

and time flexibility to passengers

United’s scheduling strategy balances marketing goals and operating imperatives to meet financial goals

•Market selection

– Where should we fly?

•Flight frequency/time

– How often should we fly?

– When should we depart/arrive?

•Fleet selection

– Which aircraft type should we use?

•Maximize revenue

•Minimize cost

Marketing goals

•Safety/maintenance requirements•Aircraft availability•Crew availability•Other operating restrictions

Operating imperatives

Financial goalsProfitability

9

Fine Tuning the Schedule

United changes its schedule based on passenger travel patterns

•Weekdays – higher business

demand

•Weekends – higher leisure

demand

•Business destinations – more weekday flights

•Leisure destinations – more weekend

flights

•Schedule changes based on season

United’s flight schedule•Higher leisure demand

during school vacations/holidays

•Higher leisure demand during summer

•Higher business demand during spring/fall

External factors e.g. Iraq war, SARS, etc.

10

The Zeus Suite

AIRFLITESchedule Database/Editor

Slot Administrator

Data Query & Analysis

ProfitabilityForecast

Fleet Assignment

Through Assignment

1PLAN Web Portal

Maintenance Routing

Re-fleetingModels

Level of Operations (LOOPS)

WeekendCancellation

Airline Simulation

InternationalFlouting

SIMONO&D Fleeting

Neighborhood Search

Dissemination - IDEAS

11

ZEUS Enables All Stages of Planning and Scheduling

OperationalPlanning

Mid TermPlanning

Long TermPlanning

StrategicPlanning

Process

Activities

Key Models

• Hub Planning• Fleet Plan• Acquisitions• Schedule Structure

• Markets• Frequencies• Schedule Structure• International Slots

• Fleeting• Crew Interactions• Reliability• Maintenance

• Operability•Aircraft Flows •De-peaking• Reliability• Flight Number Integrity•Weekends, Transition

• Profitability Forecast (PFM)

• Joint UA-UAX Fleet Planning

• Codeshare Optimizer

• PFM• Joint UA-UAX

Fleet Assignment

• UA Fleet Assignment

• Re-Fleeting• Routing

• Through Assignment / Routing

• Flight Number Continuity

• Exception Scheduling

• De-peaking Suite

Multi-year 365-108 days 108-80 days 80-52 daysTime*

*Time = days from schedule start date

Strategic Planning Schedule Optimization

12

Profitability Forecast Model (PFM)

PFM employs advanced econometric techniques (Multinomial Logit (MNL) methodology)

•Passenger preference factors for itinerary attributes (# of stops, departure time, equipment, codeshare, etc.) are simultaneously estimated using MNL techniques

•Consistent with passenger utility-maximizing choice behavior

Methodology and Key Capabilities

air-carrier schedule

(OAG)

IndustryDemands

Industry fares

PFM aids strategic decisions such as:•Merger and acquisition scenarios•Codeshare scenarios•Equipment preference studies•Hub location/buildup studies

Cost model

Passengers (total, local)

Fares (local, OD)

Revenue (local, OD)

Profitability of future

schedule

Inputs Outputs

ObjectivePFM is United’s strategic network-planning tool. PFM incorporates historical cost and fare data with itinerary-level passenger forecasts to determine schedule profitability

MAPD – Mean Absolute Percent Deviation

13

Fleet Assignment Models

The model uses advanced Operations Research techniques to solve the entire network to determine the optimal fleet assignment.

Uses a Mixed Integer Linear Program. Maximizes UA’s profitability subject to various operational and other constraints.

Time Windows capability creates opportunity for further improve profitability by making small changes to departure/arrival times


UA Schedule

Itinerary Leveldemand and fare

forecasts

AircraftCharacteristics,

Cost, Operational, other constraints

AircraftInventoryBy Type

Fully fleetedschedule

Inputs Outputs

ObjectiveThe O&D models are used to obtain the optimal fleet assignment for a flight schedule based on itinerary based demands and market share

14

Codeshare Optimizer

Codeshare Optimizer uses a Dynamic Program-like approach to model incremental code share opportunities and PFM’s itinerary building algorithms and LOGIT methodology

The objective is to maximize incremental revenue while satisfying the flight number and other marketing constraints


OAG Schedule

Market List

Marketing Constraints

Ability to support several scenarios:•Evaluate new codeshare or expand existing codeshare•Optimize flight number usage when there is a shortage of flight numbers

•Make tactical market/flight changes during major schedule change

Airport-pair passenger forecasts

List of flightswith best

Codeshare Revenue

Inputs Outputs

ObjectiveCodeshare Optimizer is a strategic decision-making tool to determine the best set of flights to code share based on market share and prorate agreements.

15

Exception Scheduling Model

The model uses a Mixed Integer Linear Program to model the weekend schedule and maximize the profitability subject to operational and other constraints

Associated business process changes have resulted in independent construction of optimal weekday and weekend schedules


UA Schedule

Demand andFare

Forecasts

The model ensures that the weekend schedule meshes seamlessly with the surrounding weekday schedules

The model recaptures demands from canceled flights and moves the demand to neighboring flights in the market

OperationalConstraints

Fully Fleeted WeekendSchedule

Inputs Outputs

ObjectiveOptimize exceptions on weekends to improve profitability while adhering to operational constraints

16

Schedule Improver (Simon)

Given an aircraft inventory and a list of potential flights to fly, SIMON selects flight legs and assigns fleet types to flight legs in order to maximize contribution.

Simon honors a host of operational constraints including those related to maintenance, noise, and crew availability. In addition, users can specify schedule structure constraints.


Mandatory and optional

flights

O&D leveldemand

Cost model

By varying the amount of the schedule that is considered mandatory, users can control the amount of changes to an existing schedule in an incremental manner.

Simon can intelligently determine the best pattern of flights to retain in any market

O&D levelfares

OptimalSchedule

Inputs Outputs

ObjectiveSimon determines the optimal schedule to fly from a given base schedule and a large superset of potential flight opportunities.

Revenue Management Automation SuiteRevenue Management Automation Suite

18

This Section Will Focus on Yield (Inventory) Management

Yield ManagementObjective: Given a schedule and estimated demand/fares,

optimally allocate the seat inventory on each flight to

ensure revenue-maximizing passenger mix

SchedulesObjective: Develop optimal schedule network based on

market forces, estimated demand/fares, available

capacity, operational imperatives, etc.

PricingObjective: Set the fares to maximize revenue across customer segments and to effectively compete in the

market place

19

Passenger Segmentation Strategy

Higher

Lower

F

A

R

E

S

•Business travelersFrequent schedules

Last minute availability

Full service

Global access

Recognition

•Leisure travelersLow fares

Quality service

Low

High

Pri

ce s

ensi

tive

Low

HighW

illi

ng

nes

s to

co

mm

it i

n a

dva

nce

An

d s

che

du

le f

lexi

bil

ity

20

Business

Leisure

Sale 14

14

7

3

0

No. of advance purchase days

Tra

ve

l re

str

icti

on

s

95

110

187

334

Fares

17

13

17

26

Demand

High

56 passengers paying an average fare of $238; total revenue $13,328


Sale 7

60

79

28

24


Capacity Control Problem: UA881 on Sep 16 2004

21

What is O&D Control ?

SFO

LAX

ORD LGA

Itinerary Fare Demand

LGA-ORD $100 5

ORD-LAX $100 2

ORD-SFO $100 1

LGA-ORD-LAX $150 5

LGA-ORD-SFO $225 1

(1 Seat)

(1 Seat)

(1 Seat)

22

O&D Control Yields Better Revenue

SFO

LAX

ORD LGA

Itinerary Fare Demand

LGA-ORD $100 5

ORD-LAX $100 2

ORD-SFO $100 1

LGA-ORD-LAX $150 5

LGA-ORD-SFO $225 1

(1 Seat)

(1 Seat)

(1 Seat)

Leg Based ORION

1

1

1

0

0

$300

0

1

0

0

1

$325

23

United has been the Leader in Adopting Cutting Edge Yield (Inventory) Management Technologies

Overbooking systems

Leg based Inventory Management systems with fare class control reservation systems

AA, SAS implemented O&D systems in the 1990s. CO, LH started using O&D controls in the mid 1990s

Enhancements to systems to compete with Low Cost

Carriers

Overbooking systemsStatic O&D system with O&D control

Orion Development

Orion implementation included path based

forecast, network optimization

and dynamic passenger valuation

Strategic research to compete with Low Cost

Carriers

Major Airlines

1980s 1990 - 1995 1996 - 2000 2001 - 2003 2004 and Beyond

24

United’s Yield Management System - Orion

Travel AgentsUnited Res.Online Agencies

PassengerValuation

Optimization

DemandForecasting

Pricing andAccounting

Systems

Aircraft Scheduling

InventorySystem(Apollo)

Orion

RM Planners

tickets, datapublished faresrules

adjustments

controls

schedule

PV parameters

bookingscancellationsschedule changedeparture data

Base Fares

adjustments

Path level demand& no-show forecast

AU LevelsDisplacement Costs

25

• Flight Network Orion optimizes revenue on approximately 3,600 UA and UAX daily departures About 27,000 unique paths are flown each day by United’s customers

• Forecast and Optimization Statistics Orion produces 13 million forecasts for all 336 future departure dates All future departure dates are optimized every day Orion produces flight level controls for nearly 1.1 million flights in the future Options exist for analysts to load changes into Apollo throughout the day Passenger valuation produces new base fares every two weeks

• Hardware infrastructure A dedicated IBM supercomputer complex is utilized to run the forecasting and

optimization algorithms

High-Level Orion Statistics

26

Advanced Availability Processing

• Consumers are price conscious and conditioned to shop for travel

• Availability of internet outlets is increasing shopping activity

• Most airlines are experiencing higher look to book ratios, stretching computing capability

• Opportunity to further tailor product offering to passenger segments

• Increased inventory control capabilities

Improved channel control Customer centric RM

• Distribution capabilities

• Manages dramatic growth of availability requests and reduces processing costs

• Maintains revenue integrity through real-time application of inventory controls

• Open system architecture for faster development

Advanced Availability ProcessingChallenges and Opportunities

Day of Operations Automation SuiteDay of Operations Automation Suite

28

Airport Manpower Assignment Models

How many employees do we need at the airport for daily Operations?

Passengers

OR-BasedAssignment Model

Demand &

Schedule

How many employees?

Their respective assignments

OutputInput

Customer Service

Gate Agents

Baggage Handlers

Airport Employees

Considerations

Multiple start times

Overtime/Parttime

Employees call in sick

IRROPS (Bad Weather)

Overestimating Need Costly, Idle employeesUnderestimating Need Long lines, dissatisfied

customers

29

Block Time Forecasting Model

How many minutes should United take to fly between a City Pair?

Let’s Use JFK-LAX as an example

Block TimeForecasting

Demand

Fuel costCrew Cost

# minutes to fly

OutputInput

Initial Response to the Question above: Why doesn’t United fly the most fuel efficient route and use that time?

The range used for a 767 is anywhere between 5:10 & 5:30

Statistical Forecasting Techniques

Going Too Fast:Higher fuel costGoing Too Slow:

Higher crew costsMissed connections

Complications:Enroute Air traffic delaysFAA re-routesWeather

30

Real-time IRROPS Management Models

Q: When things go “wrong” on the day-of-operations, what is the best way to “Respond and Recover” ?

What can go wrong?1. Bad Weather (60 days out of 360 days)2. Aircraft needs maintenance3. Crew shortage4. Runway closedowns

What are the choices?1. Cancel the flight(s)2. Delay a flight3. Get a Spare Aircraft4. Get Reserve Pilots/Flight attendants

Challenges:All of this has to be done in close to “real time”All Resources have to be “re-positioned” so that the next day Operations can run smoothly

United has built a whole host of math-based Applications to assist in these decisions

31

Irregular Operations Management at United

Operations Data Store

Pilot Apps

AircraftReassignment

Flight AttendantRecovery

PassengerRecovery

ResourceRecovery

ArrivalSequencing

Delay VsCancels

Optimized set ofCancellations

Optimized Re-sequencing

of Arrivals at ORD

SkyPath

Analyze theImpact of Proposed Cancellations & Recovery

Analyze theImpact of Proposed

Re-ordering

Operations Data WarehouseFAA ODS

Real-time Information

Feedback to Planning

GDPIssued

for ORD

A “Bad” Day at ORD

0

5

10

15

20

25

30

DynaBlock

All these tools work interactively to provide the overall solution

The Future for OperationsThe Future for Operations

The Operations Holy Grail:Can there be one Global application that can

make ALL these decisions?

33

Irregular Operations Management at united


Pilot Apps

AircraftReassignment

Flight AttendantRecovery

PassengerRecovery

ResourceRecovery

ArrivalSequencing

Delay VsCancels

Optimized set ofCancellations


of Arrivals at ORD

SkyPath

Analyze theImpact of Proposed Cancellations & Recovery


Re-ordering




GDPIssued

for ORD


0

5

10

15

20

25

30

DynaBlock

34

Irregular Operations Management at united


ArrivalSequencing


of Arrivals at ORD

SkyPath


Re-ordering




GDPIssued

for ORD


0

5

10

15

20

25

30

DynaBlock

OpsGlobalSolver

At United, we are working on building this “Global Solver”

35

Next Frontiers – A Sample

• Game theoretic models to predict and respond to competitor actions

• Multiple Criteria Decision Making

• Modeling trade-offs between key decision variables

• Data Mining

Operations Research at United AirlinesOperations Research at United Airlines

37

Experts in optimization and forecasting techniques dedicated to solving complex business problems

Approximately 45 people

Advanced degrees in Mathematics, OR, Statistics, Transportation Science, Industrial Engineering, and related fields

19 PhDs

Mix of employees from academia, the airline industry, and management consulting

Partnerships with universities

Enterprise Optimization - Overview

Mission. Provide thought leadership and ground breaking research capabilities that challenge the status quo ; partner with business units and delivery groups to create value through excellence in modeling and research.

The Activities

Solve complex business problems using math modeling, forecasting, stochastic modeling, heuristic optimization, statistical modeling, game theory modeling, artificial intelligence, data mining, and other numerical techniques

Review business processes in high-leverage areas

Rapidly develop model prototypes to validate theories and provide quick returns

Partner with IT professionals to build full blown, robust production systems

The Group

38

Profitability forecasting to make long term business plan decisions including market selection and frequency of operations.

Fleet Assignment models for fleet planning and profit maximization.

Aircraft Routing models to operationally route aircraft

Codeshare Optimization to effectively manage the growing revenue opportunity through partner airline relationships.

Enterprise Optimization – Business Areas

Aircraft Scheduling Revenue Management

Crew Planning Models to efficiently plan trips and

monthly schedules for pilots and flight attendants.

Crew Manpower Planning Models for pilots and flight attendants to manage complex decisions including staffing levels, training levels, vacation allocations and distribution of crew among geographically dispersed bases.

Revenue Optimization models focused on inventory, pricing, and yield.

O&D Demand forecasting to feed decision making in revenue optimization models.

Next Generation Revenue Management model to more effectively compete with growing airline segment of Low Cost Carriers that have a dramatically different and uniquely simplified price and inventory strategy.

Supply Chain ManagementModels to balance reduction in

inventory costs while maintaining and improving the reliability of our operation.

39

Summary

• The airline industry presents many high-value opportunities for Operations Research systems

• United has historically invested, and continues to heavily invest in state-of-the-art tools

• United has also consistently partnered with academia to develop cutting edge models

• Increasing computing power at lower cost many high value opportunities remain

planning and operating united airlines: business model and optimization enablers gregory taylor...

Documents

united airlines slide

schedule united changes

ord slide

number of markets

united express

operational planning

local passengers

schedule profitability