Planning and Operating United Airlines:Planning and Operating United Airlines:Business Model and Optimization EnablersBusiness Model and Optimization Enablers
Gregory TaylorSenior Vice President – Planning
United Airlines
2
Operating Facts
United Airlines flies 1,700 daily flights
United Express flies 1,700 daily flights
$11.6 billion passenger revenue
$0.6 billion cargo revenue
Second largest
airline in the world
58.4 million domestic
passengers
8.7 million international passengers
All numbers are for calendar year 2003
United currently has 62,000+ employees worldwide to carry customers safely, conveniently and efficiently
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Operating Facts
109 destinations in 23 countries
700+ destinations in 128 countries
United's customers enjoy access to more than 700 destinations around the world through Star Alliance, the leading global airline network
United's Mileage Plus® program, with almost 40 million enrolled members, regularly receives awards from leading business travel publications
4
Operating Fleet
Airbus 319 Airbus 320
Boeing 737
Boeing 747
Boeing 757 Boeing 767
Boeing 777
BAE 146
Beech craft 1900
Canadair Dornier 328
EMB 120
Jetstream 41
United currently uses 532 aircraft to support its worldwide operations
United Airlines United Express
United Express carriers currently use 200+ aircraft in their operations
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Large Hubs in Five Major Cities
6
United is the Largest International Carrier
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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
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United’s Route Network Model
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
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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
BOS-ORD
LAS-ORD
ALB-ORD SEA-ORD
BUF-ORD
PDX-ORD460
494
79 292
99
176
Daily connecting passenger volume
BOS-PDXBOS-SEA
ALB-LAS
ALB-PDX
BUF-LASBUF-SEA
BOS-LAS
ALB-SEA
BUF-PDX
28
917
13
22
17
12
19
13
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The Chicago Hub
Chicago 2003 Operating Statistics
Number of cities served 125
Number of markets 7800
Number of departures 360,377
Total passengers 15,450,424
Local passengers 8,034,220 (52%)
Connecting passengers 7,416,204 (48%)
United and United Express
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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
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Passenger Segmentation Strategy
Higher
Lower
F
A
R
E
S
•Business travelersFrequent schedulesLast minute
availabilityFull serviceGlobal accessRecognition
•Leisure travelersLow faresQuality service
Low
High
Pric
e se
nsiti
ve
Low
HighW
illin
gnes
s to
com
mit
in a
dvan
ce
And
sch
edul
e fle
xibi
lity
13
Business
LeisureSale 14
14
7
3
0
No. of advance purchase days
Trav
el re
stric
tions
95
110
187
334
Fares
17
13
17
26
DemandHigh
56 passengers paying an average fare of $238; total revenue $13,328
69 passengers paying an average fare of $75; total revenue $5,175Sale 7
60
79
28
24
125 passengers paying an average fare of $148; total revenue $18,503
Capacity Control Problem: UA881 on Sep 16 2004
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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)
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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
Operations Research at United AirlinesOperations Research at United Airlines
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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
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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 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 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.
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Overview of United’s Network Planning Automation Overview of United’s Network Planning Automation Suite - ZeusSuite - Zeus
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ZEUS Enables All Stages of Planning and Scheduling
OperationalPlanning
Mid TermPlanning
Long TermPlanning
StrategicPlanningProcess
Activities
Key Models
• Hub Planning• Fleet Plan• Acquisitions• Schedule Structure
• Markets• Frequencies• 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
> 180 days 180-108 days 108-80 days 80-52 daysTime*
*Time = days from schedule start date
Strategic Planning Schedule Optimization
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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
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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
Competitive Schedules
(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
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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
Methodology and Key Capabilities
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
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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
Methodology and Key Capabilities
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.
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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
Methodology and Key Capabilities
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
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Hub De-peaking Suite
An Integer-Programming optimizer determines the flight re-timings from the baseline schedule
Objective is to minimize revenue loss while satisfying de-peaking and gating constraints
Methodology and Key Capabilities
UA Schedule
PFMDemand
Forecasts
De-peaking and GatingRestrictions
De-peakedSchedule
Inputs Outputs
ObjectiveFine-tune United’s schedule to meet airport capacity requirements with minimal revenue impact
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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.
Methodology and Key Capabilities
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
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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
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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
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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
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• 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
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Demand Forecasting System
Model Technology• Exponential smoothing based forecasting method
utilizes most relevant historical data
Methodology and Key Capabilities
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
Inputs Outputs
UA schedule
Path level booking and cancel data
User adjustments
Special events calendar
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
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Passenger Valuation System
• Establish the fare value proxy for O&D using• Weighted average of historical usage• Current selling fares for future travel periods• User adjustments
Methodology and Key Capabilities
• Fares are updated every two weeks, to reflect accurate information on future fares
• Fares can be established based on• Day of week• Connection type• Departure date range• Point of sale
• O&D fare forecasts
Inputs Outputs
Current fares for future travel
periods
Historical usageof fare products
User Adjustments
Objective Forecast the expected value of future passenger demand
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Optimization System
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
Methodology and Key Capabilities
Key Capabilities• Space planning model distinguishes between true no-
shows and revenue standbys• Overbooking dials to throttle bookings
• Flight bucket authorization levels
• Displacement costs
Inputs Outputs
UA schedule
Path level demand, cancel
forecasts
O&D fare forecasts
No-show forecasts
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
Availability Processing
• 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
Methodology and Key Capabilities
• Virtual nesting leads to dynamic mapping of paths to buckets
• O&D availability of inventory
• Accept/reject decisions of booking requests
Inputs Outputs
Flight bucket level
authorizations
Displacement costs for all future
flights
UA schedule
Objective: Evaluate availability requests based on path value and bucket availability
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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
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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
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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
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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. Airport Congestion
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
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Irregular Operations Management at United
Operations Data Store
Pilot Apps
AircraftReassignment
Flight AttendantRecovery
PassengerRecovery
ResourceRecovery
ArrivalSequencing
Delay VsCancelsOptimized 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?
44
Irregular Operations Management at united
Operations Data Store
Pilot Apps
AircraftReassignment
Flight AttendantRecovery
PassengerRecovery
ResourceRecovery
ArrivalSequencing
Delay VsCancelsOptimized 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
45
Irregular Operations Management at united
Operations Data Store
ArrivalSequencing
Optimized Re-sequencing
of Arrivals at ORD
SkyPath
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
OpsGlobalSolver
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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
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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
