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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

Judy Pastor, Senior Manager, OR

Continental Airlines

INFORMS - Houston

November 16, 2000


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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


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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


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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


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Planning

  • Fleet Planning

  • Market Planning

  • Real Estate (gates, terminals)

  • Finance

  • All areas need

    • forecasting

    • optimization

    • network analysis


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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


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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


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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)


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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


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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


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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


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Continental OR Group

  • Analyst

    • Several years of programming in oil industry

    • Returned to school for MS, Mathematical Sciences, Rice University

    • First Operations Research position.


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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


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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


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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


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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?


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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


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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


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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


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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


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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.


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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


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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


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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


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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


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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


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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


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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


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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


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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”?


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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


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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.


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Ultimate Goal

  • We want Continental Airlines to have

THE BEST LITTLE OR HOUSE

IN TEXAS !!