Planning and Operating United Airlines: Business Model and Optimization Enablers

1. Planning and Operating United Airlines:Business Model and Optimization Enablers Gregory Taylor Senior Vice President – Planning United Airlines

2. Operating Facts United Airlines flies 1,700 daily flights Secondlargest airline in the world United currently has 62,000+ employees worldwide to carry customers safely, conveniently and efficiently $11.6 billion passenger revenue $0.6 billion cargo revenue 58.4 million domestic passengers 8.7 million international passengers United Express flies 1,700 daily flights All numbers are for calendar year 2003

3. 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

4. Airbus 319 Boeing 777 Airbus 320 Boeing 737 Boeing 747 Boeing 757 Boeing 767 Operating Fleet United currently uses 532 aircraft to support its worldwide operations United Express carriers currently use 200+ aircraft in their operations Jetstream 41 BAE 146 EMB 120 Beech craft 1900 Dornier 328 Canadair United Express United Airlines

5. Large Hubs in Five Major Cities

6. United is the Largest International Carrier

7. 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 Eastern United States Boston (BOS) Albany (ALB) Buffalo (BUF) Western United States Las Vegas (LAS) Seattle (SEA) Portland (PDX) LAS BOS SEA ALB PDX BUF

8. 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 SEA ORD 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

9. 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

10. The Chicago Hub Chicago 2003 Operating Statistics United and United Express 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%)

11. 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?

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

13. Capacity Control Problem: UA881 on Sep 16 2004 334 26 0 56 passengers paying an average fare of $238; total revenue $13,328 187 17 3 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

14. (1 Seat) (1 Seat) (1 Seat) What is O&D Control ? SFO ORD LGA LAX Itinerary FareDemand LGA-ORD $100 5 ORD-LAX $100 2 ORD-SFO $100 1 LGA-ORD-LAX $150 5 LGA-ORD-SFO $225 1

15. (1 Seat) (1 Seat) (1 Seat) O&D Control Yields Better Revenue SFO ORD LGA LAX Leg Based ORION Itinerary FareDemand 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 1 $300 $325

16. Operations Research at United Airlines

17. 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 The Group 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 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

18. Enterprise Optimization – Business Areas Revenue Management Aircraft Scheduling • 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 ChainManagement • 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. • 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.

19. Overview of United’s Network Planning Automation Suite - Zeus

20. > 180 days 180-108 days 108-80 days 80-52 days ZEUS Enables All Stages of Planning and Scheduling Schedule Optimization Strategic Planning Process Strategic Long Term Mid Term Operational Planning Planning Planning Planning Time* • 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 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

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

22. 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 Outputs Inputs Methodology and Key Capabilities 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 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

23. 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 Outputs Inputs Methodology and Key Capabilities 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

24. 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. Outputs Inputs Methodology and Key Capabilities 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

25. Exception Scheduling Model • Objective • Optimize exceptions on weekends to improve profitability while adhering to operational constraints Outputs 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 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

26. Hub De-peaking Suite • Objective • Fine-tune United’s schedule to meet airport capacity requirements with minimal revenue impact Outputs Inputs Methodology and Key Capabilities UA Schedule 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 De-peaked Schedule PFM Demand Forecasts De-peaking and Gating Restrictions

27. Schedule Improver (Simon) • Objective • Simon determines the optimal schedule to fly from a given base schedule and a large superset of potential flight opportunities. Outputs 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. 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

28. Revenue Management Automation Suite

29. 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. Yield Management Objective: Given a schedule and estimated demand/fares, optimally allocate the seat inventory on each flight to ensure revenue-maximizing passenger mix Pricing Objective: Set the fares to maximize revenue across customer segments and to effectively compete in the market place

30. 1980s 1990 - 1995 1996 - 2000 2001 - 2003 2004 and Beyond 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 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

31. 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) AU Levels Displacement Costs Optimization controls Path level demand & no-show forecast bookings cancellations schedule change departure data adjustments Demand Forecasting Travel Agents United Res. Online Agencies Aircraft Scheduling schedule

32. 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

33. 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 Outputs Inputs Methodology and Key Capabilities • Model Technology • Exponential smoothing based forecasting method utilizes most relevant historical data UA schedule Path level booking and cancel data • Future path class point of sale booking forecasts • Cancellation rates of current and future bookings • Types of Forecast Models • Rejected Demand • Seasonality • Special events – Used for targeted periods • Groups • No-shows Special events calendar User adjustments

34. Passenger Valuation System Objective • Forecast the expected value of future passenger demand Outputs Methodology and Key Capabilities Inputs • Establish the fare value proxy for O&D using • Weighted average of historical usage • Current selling fares for future travel periods • User adjustments Current fares for future travel periods • O&D fare forecasts Historical usage of fare products • 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 User Adjustments

35. 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 Outputs Inputs Methodology and Key Capabilities UA schedule • 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 Path level demand, cancel forecasts • Flight bucket authorization levels • Displacement costs No-show forecasts • Key Capabilities • Space planning model distinguishes between true no-shows and revenue standbys • Overbooking dials to throttle bookings O&D fare forecasts

36. Availability Processing Objective: • Evaluate availability requests based on path value and bucket availability Outputs Inputs 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 Flight bucket level authorizations • O&D availability of inventory • Accept/reject decisions of booking requests Displacement costs for all future flights • Virtual nesting leads to dynamic mapping of paths to buckets UA schedule

37. 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 Advanced Availability Processing Challenges and Opportunities • 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

38. Day of Operations Automation Suite

39. Customer Service Gate Agents Baggage Handlers Airport Employees Passengers Overestimating Need  Costly, Idle employees Underestimating Need  Long lines, dissatisfied customers Output Input Demand & Schedule How many employees? Their respective assignments Considerations Multiple start times Overtime/Parttime Employees call in sick IRROPS (Bad Weather) OR-Based Assignment Model Airport Manpower Assignment Models How many employees do we need at the airport for daily Operations?

40. Output Input Demand Fuel cost Crew Cost # minutes to fly Block Time Forecasting 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 Statistical Forecasting Techniques

41. 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 • Airport Congestion 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

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

43. The Future for OperationsThe Operations Holy Grail:Can there be one Global application that can make ALL these decisions?

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

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

47. 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

48. 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