1. Operations Research at Continental Airlines Judy Pastor, Senior Manager, OR Continental Airlines INFORMS - Houston November 16, 2000

2. Continental Airlines OR History No centralized OR Group OR”-ish” functions throughout company Many models purchased - some black box - most unrelated to each other New Management, 1994-95 Analytical skills sought

3. Continental OR Group Late 1994, Revenue Management saw a need for a simulator and Operations Research group to use it Rev Mgt simulator specified by Continental and built by Aeronomics (Talus) early 1995 OR Manager and Analyst hired April, 1995 First mission of OR group to use simulator to experiment with OD heuristics

4. Airline OR Many departments in an airline can benefit from OR expertise Historically, airlines have been some of the largest users of OR models Five major users (others exist) –Planning (Long and Medium Term) –Scheduling (Short Term Planning) –Pricing –Revenue Management –Operations

5. Planning Fleet Planning Market Planning Real Estate (gates, terminals) Finance All areas need –forecasting –optimization –network analysis

6. Scheduling Schedule Generation –uses forecasting, discrete choice (logit) modeling, simulation, some optimization Fleet Assignment –uses forecasting, integer optimization Crew Scheduling –integer optimization (column generation) Airport Services Scheduling –integer optimization, simulation

7. Pricing Usually broken up into Domestic and International components Much less scientific than other areas Deregulation in US has led to two actions –lead a sale or a price increase –match the competition 30,000 origin-destination pairs to price domestically Fares filed twice a day

8. Revenue Management Most “micro” of all areas Decides the number of “discount” seats to sell Saves seats for last-minute, high yielding passengers Determines how much to overbook a full flight Chooses whether to sell to one connecting passenger or to two locals (OD problem)

9. Revenue Management (RM) Uses many OR techniques –forecasting –optimization (deterministic and stochastic) –simulation Constraints to RM problem received from other departments –Prices from Pricing –Number of Seats from Scheduling –Available Connections from Scheduling

10. Revenue Management (RM) Airlines sell their inventory (seats) in a variety of ways –Airline reservations service –Travel Agents –Consolidators/”Bucket Shops” –Internet (own website/Travelocity/Priceline) Seat sales controlled by Computerized Reservations Systems (CRS) –antiquated, developed before hub and spoke system –RM must work in this environment

11. Back to 1995 - Initial OR Group Both Manager and Analyst from outside company Manager, Judy Pastor –BS, Computer Science, w/ 9 yrs in oil industry –MS, Operations Research, w/ 2 years in transportation OR at UPS and 4 years in LP modeling for oil refineries –Was interested in returning to transportation applications

12. Continental OR Group Analyst –Several years of programming in oil industry –Returned to school for MS, Mathematical Sciences, Rice University –First Operations Research position.

13. Initial Conditions No formal job descriptions Mission was to understand RM practices and look for process improvements Limited analytical software –no LP optimizer or modeling language –no statistical package –no generalized simulation package. –Lotus 123, student version of LINDO, and a C compiler

14. Initial Conditions (continued) OR Group under Revenue Management At same time, RM was in transition –installation of latest version of PROS (Pax Revenue Optimization System) as DSS –change from market analysts with reservations and/or operations backgrounds to MBAs from highly quantitative and analytical programs –formal training program was in its infancy

15. First Projects Initial projects centered around using RM simulator to examine OD strategies RM simulator was good example of a black box model –output showed a total revenue and total pax boarded but gave no clue as to what caused changes from run to run. –a “pretty” user interface was being built for Phase II but was useless since this information was still not available –we wrote C programs to read “debugging” output to create multiple run comparison reports showing differences in pax acceptance with parameter changes, booking curves, EMSR curves –answering question of “WHY??” because all important

16. First Projects (continued) To aid in defining OR projects, the “Questions Group” was formed –met monthly to identify things we would like to know but do not currently such as what drives denied boarding costs? what is the true cost of a posted (full) flight? how much does poor forecasting hurt us? lots of ideas and which to tackle first?

17. Getting Started Much latitude was given to define work processes and projects Assignments were unstructured and exploratory. Do whatever needs to be done Sad fact of matter about OR jobs - the data is never the way you want it, needs to be cleaned up, etc. “Grunt” work often required, especially in the absence of tools Modeling not always major part of job Entry level OR Analysts can be disappointed with this

18. OR Analysts should be able to accept frustration require extreme flexibility and ability to change in mid-stream able to find the value in coming up against a dead-end (sometimes an opening comes up later) must never be satisfied until they understand why a system works as it does

19. Home Alone In the first two years, two OR analysts came and went Ads in the paper generated hundreds of resumes, most responding to either “Operations” or “Research” but not “Operations Research” Time alone gave me the time to acquire software, build usable tools, document processes, learn more about Continental and the airline industry, etc. Created a vision for the group of one that could provide OR techniques to RM and other parts of company, tying together the many black boxes for a common purpose

20. Building the Group Was able to build group with a variety of people –from inside and outside the company –with strong analytical skills –with strong communication skills Strong communication skills very important to get the message out

21. OR Responsibilities Understand all vendor supplied systems in place now –forecasting in PROS seasonality calculations clustering of market groups Learn about new features and be able to explain them. Make recommendations as to their use.

22. OR Responsibilities Develop new techniques/systems to improve processes and enhance revenue –used statistical analysis to aid in identifying causes of frequently late arriving flights – participated in design and delivery of Enterprise Wide Data Warehouse eliminating the data “silos” built by each department

23. OR Responsibilities Stay abreast of latest techniques/research done in Operations Research, especially in area of Revenue Management –participation in INFORMS –membership in AGIFORS (Airline Group of the International Forum of OR Scientists) –software user groups

24. Current Major Projects Demand Driven Dispatch –“Flagship” Project of OR –Combines aspects of Scheduling (Fleet Assignment) and RM –Algorithm and System developed by OR Overbooking Improvement using DW data O and D Forecasting

25. Demand Driven Dispatch Aircraft types (and caps) are assigned to routes by a Fleet Assignment Model (FAM) –Input to FAM is based on an estimate of average demand –Objective is to maximize revenue, minimize costs, and normalize operations –A consistent fleet assignment throughout the week to the same flight M-F is seen as advantageous to the operation

26. Demand Driven Dispatch RM knows that demand varies from one DOW (day of week) to another Fleet assignment is “pretty” optimal overall, but suboptimal on a flight by flight basis D 3 (Demand Driven Dispatch) group –examines the schedule –finds sets of flights that are easy to swap –queries the RM forecasts for those flights – prescribes swaps to maximize revenue

27. Overbooking Improvement Determined by “no show” factor –normal no show is 10-15% –Latin flights can have up to 50% no show Empty seats are perishable inventory –after plane takes off, those selling opportunities are gone Can more detailed data about pax help? –Data Mining techniques, forecasting, DSS

28. O and D Forecasting CRSs are “leg based” –connecting pax book onto two legs –revenue of 1 cnx pax < 2 local pax (in general) 2200 flights a day/10 fare classes = 22,000 leg based forecasts per day * 330 days in a booking cycle = 660,000 for departure day 30,000 OD itineraries * 3 paths/itinerary * 10 fare classes * 330 = 297,000,000

29. O and D Forecasting We currently do leg forecasting and optimization with an O and D heuristic to handle connecting itinerary requests Theoretically, a network based solution would give us substantially better revenue But, network solutions are based on many forecasts all with some type of error associated with them

30. O and D Forecasting Other challenges –using O and D optimization within constraints of leg based CRS –small numbers problem –constantly changing network/schedule/environment –is there a compromise that can get us most of the way to the “optimal”?

31. Other Issues OR must assist in the management decision –develop the DSS in-house or –purchase from vendor? –how will it be integrated into business process? –how will the technology be transferred? Currently OR is under RM, but integrated departmental solutions are the holy grail Career path for OR Professionals

32. Who You Gonna Call? “Ghost Busters!” - Operations Research group has the skills to understand the ramifications of different optimizations and build bridges between them, if possible.

33. Ultimate Goal We want Continental Airlines to have THE BEST LITTLE OR HOUSE IN TEXAS !!