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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

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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
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
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
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
  • Fleet Planning
  • Market Planning
  • Real Estate (gates, terminals)
  • Finance
  • All areas need
    • forecasting
    • optimization
    • network analysis
  • 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
  • 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
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
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 rm10
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
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 group12
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
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
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
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
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
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
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
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
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
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 responsibilities22
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 responsibilities23
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
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
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 dispatch26
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
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
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 forecasting29
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 forecasting30
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
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
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
Ultimate Goal
  • We want Continental Airlines to have