Operations Research at Continental Airlines

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

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

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

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

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

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

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

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)

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

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

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

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

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

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

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

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?

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

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

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

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

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.

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

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

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

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

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 • D3 (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

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

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

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

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”?

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

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.

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

Operations Research at Continental Airlines