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Gregory Taylor Senior Vice President – Planning United Airlines

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1 Gregory Taylor Senior Vice President – Planning United Airlines
Planning and Operating United Airlines: Business 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 BOS SEA ALB PDX BUF

3 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 SEA ORD ALB 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

4 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 passengers each-way between the six cities and its hub, ORD

5 Chicago Operating Statistics (Daily) United and United Express
The Chicago Hub Chicago Operating Statistics (Daily) United and United Express Number of cities served Number of markets ,800 Number of departures ,015 Total passengers ,300 Local passengers ,000 (52%) Connecting passengers ,300 (48%)

6 System Operating Statistics (Daily) United and United Express
The United System System Operating Statistics (Daily) United and United Express Number of cities served Number of markets ,682 Number of departures ,407 Total passengers ,000 Aircraft

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

8 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? Profitability Flight frequency/time How often should we fly? When should we depart/arrive? Financial goals Maximize revenue Minimize cost Operating imperatives Safety/maintenance requirements Aircraft availability Crew availability Other operating restrictions Fleet selection Which aircraft type should we use?

9 Fine Tuning the Schedule
United changes its schedule based on passenger travel patterns External factors e.g. Iraq war, SARS, etc. 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

10 Schedule Database/Editor
The Zeus Suite AIRFLITE Schedule Database/Editor Slot Administrator Data Query & Analysis Profitability Forecast Fleet Assignment Through Assignment 1PLAN Web Portal Maintenance Routing Re-fleeting Models Level of Operations (LOOPS) Weekend Cancellation Airline Simulation International Flouting SIMON O&D Fleeting Neighborhood Search Dissemination - IDEAS

11 ZEUS Enables All Stages of Planning and Scheduling
Strategic Planning Schedule Optimization Process Strategic Long Term Mid Term Operational Planning Planning Planning Planning Time* Multi-year days days 80-52 days 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 Activities 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 Key Models *Time = days from schedule start date

12 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 air-carrier schedule (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 Fares (local, OD) Cost model Revenue (local, OD) PFM aids strategic decisions such as: Merger and acquisition scenarios Codeshare scenarios Equipment preference studies Hub location/buildup studies Industry fares Profitability of future schedule MAPD – Mean Absolute Percent Deviation

13 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 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 Itinerary Level demand and fare forecasts Fully fleeted schedule Aircraft Inventory By Type Aircraft Characteristics, Cost, Operational, other constraints

14 Methodology and Key Capabilities
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 Methodology and Key Capabilities Outputs OAG Schedule 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 Market List List of flights with best Codeshare Revenue Airport-pair passenger forecasts 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 Marketing Constraints

15 Exception Scheduling Model
Objective Optimize exceptions on weekends to improve profitability while adhering to operational constraints Inputs Methodology and Key Capabilities Outputs 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 Associated business process changes have resulted in independent construction of optimal weekday and weekend schedules Demand and Fare Forecasts Fully Fleeted Weekend Schedule Operational Constraints 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

16 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 Outputs 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. Simon honors a host of operational constraints including those related to maintenance, noise, and crew availability. In addition, users can specify schedule structure constraints. O&D level demand 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. Simon can intelligently determine the best pattern of flights to retain in any market Cost model

17 Revenue Management Automation Suite

18 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

19 Passenger Segmentation Strategy
Low High Price sensitive Low High Willingness to commit in advance And schedule flexibility Higher Lower F A R E S Business travelers Frequent schedules Last minute availability Full service Global access Recognition Leisure travelers Low fares Quality service

20 No. of advance purchase days
Capacity Control Problem: UA881 on Sep 334 26 56 passengers paying an average fare of $238; total revenue $13,328 3 187 17 7 110 13 Business 125 passengers paying an average fare of $148; total revenue $18,503 Travel restrictions 14 95 17 69 passengers paying an average fare of $75; total revenue $5,175 Sale 7 79 24 Leisure Sale 14 60 28 High No. of advance purchase days Fares Demand

21 What is O&D Control ? SFO ORD LGA LAX (1 Seat) (1 Seat) (1 Seat)
Itinerary Fare Demand LGA-ORD $ ORD-LAX $ ORD-SFO $ LGA-ORD-LAX $ LGA-ORD-SFO $

22 O&D Control Yields Better Revenue
SFO (1 Seat) (1 Seat) ORD LGA (1 Seat) LAX Itinerary Fare Demand Leg Based ORION LGA-ORD $ 1 ORD-LAX $ 1 1 ORD-SFO $ 1 LGA-ORD-LAX $ LGA-ORD-SFO $ 1 $300 $325

23 United has been the Leader in Adopting Cutting Edge Yield (Inventory) Management Technologies
Major Airlines 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 1980s 2004 and Beyond Overbooking systems Static O&D system with O&D control Orion implementation included path based forecast, network optimization and dynamic passenger valuation Strategic research to compete with Low Cost Carriers Orion Development

24 United’s Yield Management System - Orion
tickets, data published fares rules Orion Pricing and Accounting Systems Passenger Valuation Base Fares adjustments RM Planners PV parameters Inventory System (Apollo) Optimization AU Levels Displacement Costs controls Path level demand & no-show forecast adjustments bookings cancellations schedule change departure data Demand Forecasting Travel Agents United Res. Online Agencies Aircraft Scheduling schedule

25 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

26 Advanced Availability Processing
Challenges and Opportunities 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

27 Day of Operations Automation Suite

28 Airport Manpower Assignment Models
How many employees do we need at the airport for daily Operations? Passengers OR-Based Assignment Model Demand & Schedule How many employees? Their respective assignments Output Input 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 employees Underestimating Need  Long lines, dissatisfied customers

29 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 Going Too Fast: Higher fuel cost Going Too Slow: Higher crew costs Missed connections Complications: Enroute Air traffic delays FAA re-routes Weather Block Time Forecasting Demand Fuel cost Crew Cost # minutes to fly Output Input Statistical Forecasting Techniques

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? Bad Weather (60 days out of 360 days) Aircraft needs maintenance Crew shortage Runway closedowns 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 What are the choices? Cancel the flight(s) Delay a flight Get a Spare Aircraft Get Reserve Pilots/Flight attendants United has built a whole host of math-based Applications to assist in these decisions

31 Irregular Operations Management at United
A “Bad” Day at ORD Operations Data Store GDP Issued for ORD ODS Operations Data Warehouse FAA Feedback to Planning Real-time Information Resource Recovery SkyPath Aircraft Reassignment Analyze the Impact of Proposed Cancellations & Recovery Analyze the Impact of Proposed Re-ordering Pilot Apps DynaBlock Arrival Sequencing Delay Vs Cancels Flight Attendant Recovery Optimized Re-sequencing of Arrivals at ORD Optimized set of Cancellations Passenger Recovery All these tools work interactively to provide the overall solution

32 The 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
A “Bad” Day at ORD Operations Data Store GDP Issued for ORD ODS Operations Data Warehouse FAA Feedback to Planning Real-time Information Resource Recovery SkyPath Aircraft Reassignment Analyze the Impact of Proposed Cancellations & Recovery Analyze the Impact of Proposed Re-ordering Pilot Apps DynaBlock Arrival Sequencing Delay Vs Cancels Flight Attendant Recovery Optimized Re-sequencing of Arrivals at ORD Optimized set of Cancellations Passenger Recovery

34 Irregular Operations Management at united
A “Bad” Day at ORD Operations Data Store GDP Issued for ORD ODS Operations Data Warehouse FAA Feedback to Planning Real-time Information SkyPath Ops Global Solver Analyze the Impact of Proposed Re-ordering DynaBlock Arrival Sequencing Optimized Re-sequencing of Arrivals at ORD 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

36 Operations Research at United Airlines

37 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 The Activities 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 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

38 Enterprise Optimization – Business Areas
Aircraft Scheduling Revenue Management 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. 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 Management Models to balance reduction in inventory costs while maintaining and improving the reliability of our operation. 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.

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

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