Planning and Operating United Airlines:
Business Model and Optimization Enablers
Gregory Taylor
Senior Vice President – Planning
United Airlines
Operating Facts
United Airlines flies
1,700 daily flights
Second
largest
United currently has 62,000+
employees worldwide to
carry customers safely,
conveniently and efficiently
airline in the
world
$11.6 billion
passenger
revenue
58.4 million
domestic
passengers
$0.6 billion
cargo revenue
United Express flies
1,700 daily flights
8.7 million
international
passengers
2
All numbers are for calendar year 2003
Operating Facts
United's customers enjoy access to more than 700 destinations around the world
through Star Alliance, the leading global airline network
700+ destinations
in 128 countries
109 destinations
in 23 countries
United's Mileage Plus®
program, with almost 40 million
enrolled members, regularly
receives awards from leading
business travel publications
3
Operating Fleet
United currently uses 532 aircraft to
support its worldwide operations
Boeing 737
Airbus 319
United Express carriers currently
use 200+ aircraft in their operations
Jetstream 41
Airbus 320
EMB 120
Boeing 757
Beech craft 1900
Boeing 767
Canadair
Boeing 777
BAE 146
Dornier 328
Boeing 747
United Airlines
United Express
4
Large Hubs in Five Major Cities
5
United is the Largest International Carrier
6
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
Eastern United States
Las Vegas
(LAS)
Boston
(BOS)
Seattle
(SEA)
Albany
(ALB)
Portland
(PDX)
Buffalo
(BUF)
LAS
BOS
SEA
ALB
PDX
BUF
7
United’s Route Network Model
United has chosen a “Hub-and-spoke” model that maximizes number of markets served
with given aircraft assets
LAS
BOS
ALB
ORD
SEA
PDX
BUF
Hub-and-spoke
• 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
8
United’s Route Network Model
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
Daily connecting passenger volume
ALB-ORD
BUF-SEA
BUF-LAS
13
79
SEA-ORD
292
22
BOS-LAS
BUF-ORD
99
28
ALB-PDX
17
ALB-SEA
19
ALB-LAS
BUF-PDX
13
12
LAS-ORD
460
PDX-ORD
176
BOS-SEA
9
BOS-PDX
17
BOS-ORD
494
9
The Chicago Hub
Chicago 2003 Operating Statistics
United and United Express
Number of cities served
125
Number of markets
7800
Number of departures
Total passengers
360,377
15,450,424
Local passengers
8,034,220 (52%)
Connecting passengers
7,416,204 (48%)
10
United’s Scheduling Strategy
United’s scheduling strategy balances marketing goals and operating imperatives to
meet financial goals
Marketing goals
•
•
•
•
Marketing strategy
Maintain market share
Competitive response
Provide travel day and
time flexibility to
passengers
• Market selection
– Where should we
fly?
• Flight frequency/time
– How often should
we fly?
Operating imperatives
•
•
•
•
Safety/maintenance
requirements
Aircraft availability
Crew availability
Other operating
restrictions
Profitability
Financial goals
•
•
Maximize revenue
Minimize cost
– When should we
depart/arrive?
• Fleet selection
– Which aircraft
type should we
use?
11
Passenger Segmentation Strategy
Low
Low
Higher
 Full service
 Global access
 Recognition
Price sensitive
 Last minute availability
• Leisure travelers
 Low fares
And schedule flexibility
 Frequent schedules
Willingness to commit in advance
• Business travelers
F
A
R
E
S
 Quality service
High
High
Lower
12
Capacity Control Problem: UA881 on Sep 16 2004
334
0
Business
7
14
Travel restrictions
3
26
187
17
110
13
95
17
Sale 7
79
24
Sale 14
60
28
Fares
Demand
56 passengers
paying an average
fare of $238; total
revenue $13,328
125 passengers
paying an average
fare of $148; total
revenue $18,503
69 passengers
paying an average
fare of $75; total
revenue $5,175
Leisure
High
No. of advance
purchase days
13
What is O&D Control ?
SFO
(1 Seat)
ORD
LAX
Itinerary
LGA
Fare Demand
LGA-ORD
$100
5
ORD-LAX
$100
2
ORD-SFO
$100
1
LGA-ORD-LAX
$150
5
LGA-ORD-SFO
$225
1
14
O&D Control Yields Better Revenue
SFO
(1 Seat)
ORD
LAX
Itinerary
LGA
Fare Demand
Leg Based
ORION
LGA-ORD
$100
5
1
0
ORD-LAX
$100
2
1
1
ORD-SFO
$100
1
1
0
LGA-ORD-LAX
$150
5
0
0
LGA-ORD-SFO
$225
1
0
$300
1
$325
15
Operations Research at United Airlines
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 Group
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
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 highleverage areas
 Rapidly develop model prototypes to
validate theories and provide quick returns
 Partner with IT professionals to build full
blown, robust production systems
17
Enterprise Optimization – Business Areas
Aircraft Scheduling
 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.
Crew Planning
 Crew Scheduling 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 Management
 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.
Supply Chain Management
 Models to balance reduction in
inventory costs while maintaining and
improving the reliability of our
operation.
Day of Operations
• Models to respond and recover from
irregular operations.
18
Overview of United’s Network Planning Automation
Suite - Zeus
19
ZEUS Enables All Stages of Planning and Scheduling
Strategic Planning
Schedule Optimization
Process
Strategic
Planning
Long Term
Planning
Mid Term
Planning
Operational
Planning
Time*
> 180 days
180-108 days
108-80 days
80-52 days
• Hub Planning
Activities
Key
Models
• Fleet Plan
• Acquisitions
• Schedule Structure
• Profitability
Forecast (PFM)
• Joint UA-UAX
Fleet Planning
• Codeshare
Optimizer
• Markets
• Frequencies
• International Slots
• Fleeting
• Crew Interactions
• Reliability
• Maintenance
• Operability
•Aircraft Flows
•De-peaking
• Reliability
• Flight Number Integrity
•Weekends, Transition
• PFM
• Joint UA-UAX
Fleet
Assignment
• UA Fleet
Assignment
• Re-Fleeting
• Routing
• Through
Assignment /
Routing
• Flight Number
Continuity
• Exception
Scheduling
• De-peaking
Suite
*Time = days from schedule start date
20
The Zeus Suite
O&D Fleeting
International
Flouting
Slot
Administrator
SIMON
Data Query &
Analysis
Airline
Simulation
Profitability
Forecast
AIRFLITE
Schedule
Database/Editor
Weekend
Cancellation
Fleet Assignment
Level of
Operations
(LOOPS)
Re-fleeting
Models
Through
Assignment
Neighborhood
Search
Maintenance
Routing
Dissemination IDEAS
1PLAN Web
Portal
21
Profitability Forecast Model (PFM)
Objective
PFM is United’s strategic network-planning tool. PFM incorporates historical cost and
fare data with itinerary-level passenger forecasts to determine schedule profitability
Inputs
Methodology and Key Capabilities
Outputs
Competitive
Schedules
(OAG)
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
Passengers
(total, local)
Industry
Demands
Cost model
Industry
fares
Fares
(local, OD)
Revenue
(local, OD)
PFM aids strategic decisions such as:
•Merger and acquisition scenarios
•Codeshare scenarios
•Equipment preference studies
•Hub location/buildup studies
Profitability
of future
schedule
MAPD – Mean Absolute Percent Deviation
22
Fleet Assignment Models
Objective
The O&D models are used to obtain the optimal fleet assignment for a flight schedule
based on itinerary based demands and market share
Inputs
Methodology and Key Capabilities
Outputs
UA Schedule
Itinerary Level
demand and fare
forecasts
Aircraft
Inventory
By Type
Aircraft
Characteristics,
Cost, Operational,
other constraints
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.
Fully fleeted
schedule
Time Windows capability creates opportunity for further
improve profitability by making small changes to
departure/arrival times
23
Codeshare Optimizer
Objective
Codeshare Optimizer is a strategic decision-making tool to determine the best set of
flights to code share based on market share and prorate agreements.
Inputs
OAG
Schedule
Market List
Airport-pair
passenger
forecasts
Marketing
Constraints
Methodology and Key Capabilities
Outputs
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
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
List of flights
with best
Codeshare
Revenue
24
Exception Scheduling Model
Objective
Optimize exceptions on weekends to improve profitability while adhering to operational
constraints
Inputs
Methodology and Key Capabilities
UA
Schedule
The model uses a Mixed Integer Linear Program to model
the weekend schedule and maximize the profitability
subject to operational and other constraints
Demand
and
Fare
Forecasts
Associated business process changes have resulted in
independent construction of optimal weekday and
weekend schedules
Operational
Constraints
Outputs
Fully
Fleeted
Weekend
Schedule
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
25
Hub De-peaking Suite
Objective
Fine-tune United’s schedule to meet airport capacity requirements with minimal
revenue impact
Inputs
Methodology and Key Capabilities
Outputs
An Integer-Programming optimizer determines the flight retimings from the baseline schedule
De-peaked
Schedule
UA
Schedule
PFM
Demand
Forecasts
Objective is to minimize revenue loss while satisfying depeaking and gating constraints
De-peaking
and Gating
Restrictions
26
Schedule Improver (Simon)
Objective
Simon determines the optimal schedule to fly from a given base schedule and a large
superset of potential flight opportunities.
Inputs
Methodology and Key Capabilities
Mandatory
and optional
flights
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.
O&D level
demand
Simon honors a host of operational constraints including
those related to maintenance, noise, and crew availability.
In addition, users can specify schedule structure
constraints.
Outputs
Optimal
Schedule
O&D level
fares
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.
Cost model
Simon can intelligently determine the best pattern of flights
to retain in any market
27
Revenue Management Automation Suite
This Section Will Focus on Yield (Inventory) Management
Schedules
Objective: Develop optimal
schedule network based on
market forces, estimated
demand/fares, available
capacity, operational
imperatives, etc.
Pricing
Objective: Set the fares to
maximize revenue across
customer segments and to
effectively compete in the
market place
Yield Management
Objective: Given a schedule
and estimated demand/fares,
optimally allocate the seat
inventory on each flight to
ensure revenue-maximizing
passenger mix
29
United has been the Leader in Adopting Cutting Edge
Yield (Inventory) Management Technologies
Major Airlines
Overbooking
systems
1980s
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
1990 - 1995
1996 - 2000
Overbooking systems
Static O&D system with O&D control
Orion
Development
2001 - 2003
Orion implementation
included path based
forecast, network
optimization
and dynamic passenger
valuation
Enhancements to systems
to compete with Low Cost
Carriers
2004 and Beyond
Strategic research to
compete with Low Cost
Carriers
30
United’s Yield Management System - Orion
Pricing and
Accounting
Systems
tickets, data
published fares
rules
Orion
Passenger
Valuation
Base Fares
adjustments
RM
Planners
PV parameters
controls
Optimization
AU Levels
Displacement Costs
Inventory
System
(Apollo)
Path level demand
& no-show forecast
adjustments
Aircraft
Scheduling
Demand
Forecasting
bookings
cancellations
schedule change
departure data
Travel Agents
United Res.
Online Agencies
schedule
31
High-Level Orion Statistics
• 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
32
Demand Forecasting System
Objective
 Estimate future bookings at the path, fare class, point of sale level for all future
departure dates; Estimate the cancellation rates of existing and future bookings
Inputs
UA schedule
Methodology and Key Capabilities
Model Technology
• Exponential smoothing based forecasting method
utilizes most relevant historical data
Path level
booking and
cancel data
Special
events
calendar
User
adjustments
Outputs
Types of Forecast Models
• Rejected Demand
• Seasonality
• Special events – Used for targeted periods
• Groups
• No-shows
• Future path class
point of sale
booking forecasts
• Cancellation rates
of current and
future bookings
33
Passenger Valuation System
Objective
 Forecast the expected value of future passenger demand
Inputs
Current fares
for future travel
periods
Methodology and Key Capabilities
Outputs
• Establish the fare value proxy for O&D using
• Weighted average of historical usage
• Current selling fares for future travel periods
• User adjustments
Historical usage
of fare products
• O&D fare
forecasts
• Fares are updated every two weeks, to reflect accurate
information on future fares
User
Adjustments
• Fares can be established based on
• Day of week
• Connection type
• Departure date range
• Point of sale
34
Optimization System
Objective
 Determine optimal space planning levels based on no-show, cancellation forecasts and
upgrade potential; Estimate the displacement costs of each future flight leg
 Use displacement costs and other parameters to optimally allocate seats to buckets on each
flight leg
Inputs
UA schedule
Path level
demand,
cancel
forecasts
No-show
forecasts
O&D fare
forecasts
Methodology and Key Capabilities
Optimization Model - Displacement Adjusted Virtual
Nesting (DAVN)
• Space planning
• Overbooking model
• Upgrade potential
• LP based network optimization to determine
displacement costs
• Capacity control
• EMSR(b) method to optimally allocate seats
Outputs
• Flight bucket
authorization
levels
• Displacement
costs
Key Capabilities
• Space planning model distinguishes between true noshows and revenue standbys
• Overbooking dials to throttle bookings
35
Availability Processing
Objective:
 Evaluate availability requests based on path value and bucket availability
Inputs
Flight bucket
level
authorizations
Displacement
costs for all
future flights
Methodology and Key Capabilities
• Each booking request is broken up as one-way paths
• Each path is assigned a value based on the fare class,
point of sale and other information
• Fare Class-to-Bucket mapping is determined using the
fare value and displacement cost of the legs traversed
by the path
• Bucket availability on each leg of path is used to accept
or reject booking
• Virtual nesting leads to dynamic mapping of paths to
buckets
UA schedule
Outputs
• O&D availability of
inventory
• Accept/reject
decisions of booking
requests
Advanced Availability Processing
Challenges and Opportunities
• 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
Advanced Availability Processing
• 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 realtime application of inventory controls
• Open system architecture for faster
development
37
Day of Operations Automation Suite
Airport Manpower Assignment Models
How many employees do we need at the airport for daily Operations?
Customer
Service
Gate
Agents
Baggage
Handlers
Airport Employees
Passengers
Input
Demand
&
Schedule
Output
Overestimating Need  Costly, Idle employees
Underestimating Need  Long lines, dissatisfied
customers
How many employees?
Their respective
assignments
OR-Based
Assignment Model
Considerations
Multiple start times
Overtime/Parttime
Employees call in sick
IRROPS (Bad Weather)
39
Block Time Forecasting Model
How many minutes should United take to fly between a City Pair?
Initial Response to the Question above:
Why doesn’t United fly the most fuel efficient route and use that time?
Let’s Use JFK-LAX as an example
The range used for a 767 is anywhere between 5:10 & 5:30
Output
Input
Demand
Fuel cost
Crew Cost
# minutes to fly
Block Time
Forecasting
Going Too Fast:
Higher fuel cost
Going Too Slow:
Higher crew costs
Missed connections
Complications:
Enroute Air traffic delays
FAA re-routes
Weather
Statistical Forecasting Techniques
40
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 maintenance
3. Crew shortage
4. Airport Congestion
What are the choices?
1. Cancel the flight(s)
2. Delay a flight
3. Get a Spare Aircraft
4. Get Reserve Pilots/Flight attendants
Challenges:
All of this has to be done in close
to “real time”
All Resources have to be “repositioned” so that the next day
Operations can run smoothly
United has built a whole
host of math-based
Applications to assist in
these decisions
41
Irregular Operations Management at United
A “Bad” Day
at ORD
GDP
Issued
for ORD
Operations Data Store
FAA
Real-time
Information
SkyPath
30
25
20
Operations Data Warehouse
ODS
Analyze the
Impact of
Proposed
Re-ordering
Feedback
to Planning
Resource
Recovery
Analyze the
Impact of
Proposed
Cancellations
& Recovery
15
Aircraft
Reassignment
Pilot Apps
10
5
0
DynaBlock
Arrival
Sequencing
Optimized
Re-sequencing
of Arrivals at ORD
Delay Vs
Cancels
Flight Attendant
Recovery
Optimized set of
Cancellations
Passenger
Recovery
All these tools work interactively to provide the overall solution
42
The Future for Operations
The Operations Holy Grail:
Can there be one Global application that can
make ALL these decisions?
Irregular Operations Management at united
A “Bad” Day
at ORD
GDP
Issued
for ORD
Operations Data Store
FAA
Real-time
Information
SkyPath
30
25
20
Operations Data Warehouse
ODS
Analyze the
Impact of
Proposed
Re-ordering
Feedback
to Planning
Resource
Recovery
Analyze the
Impact of
Proposed
Cancellations
& Recovery
15
Aircraft
Reassignment
Pilot Apps
10
5
0
DynaBlock
Arrival
Sequencing
Optimized
Re-sequencing
of Arrivals at ORD
Delay Vs
Cancels
Flight Attendant
Recovery
Optimized set of
Cancellations
Passenger
Recovery
44
Irregular Operations Management at united
A “Bad” Day
at ORD
GDP
Issued
for ORD
Operations Data Store
FAA
25
20
Analyze the
Impact of
Proposed
Re-ordering
15
10
5
0
DynaBlock
Feedback
to Planning
Real-time
Information
SkyPath
30
Operations Data Warehouse
ODS
Arrival
Sequencing
Ops
Global
Solver
Optimized
Re-sequencing
of Arrivals at ORD
45
46
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
47
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
48