Market airline class mac revenue management rm2003
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Market/Airline/Class (MAC) Revenue Management RM2003. Hopperstad May 03. Issues. Model structure Background: PODS Functional form Some results Potential real-world application Lines of inquiry. Airline RM modeling assumptions a short (public) history.

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Market airline class mac revenue management rm2003

Market/Airline/Class (MAC) Revenue ManagementRM2003

Hopperstad

May 03


Issues

Issues

  • Model structure

  • Background: PODS

  • Functional form

  • Some results

  • Potential real-world application

  • Lines of inquiry


Airline rm modeling assumptions a short public history

Airline RM modeling assumptionsa short (public) history

  • 80’s – leg/fare class demand independence

     6 to 8% revenue gains over no RM

  • 90’s – path (passenger itinerary)/class demand independence

     1 to 2% revenue gains over leg/class RM

  • Current – excursions into path demand independence

     ½% revenue gain over path/class RM


Airline rm modeling assumptions

Airline RM modeling assumptions

  • Yet, anyone who has ever taken an air trip knows that flights are picked on a market basis

    • trading-off airlines, paths, fares and fare class restrictions

  • Thus, an ultimate RM system must be market-based

  • However, market-based RM is a giant step

    • it is proposed here that a small next step is to assume independent market/airline/class demand


Background pods passenger origin destination simulator

Background: PODSpassenger origin/destination simulator

  • PODS is a full-scale simulation in the sense that:

    • passengers by type (business/leisure) generated by their

      • max willing-to-pay (WTP)

      • favorite/unfavorite airlines & the disutility attributed to unfavorite airlines

      • decision window & the disutility assigned to paths outside their window

      • disutility assigned to stops/connects

      • disutility assigned to fare class restrictions

    • passengers assigned to best (minimum fare + disutilities) available path with a fare meeting their max WTP threshold

    • RM demand forecasts based on historical bookings


Background pods

Background: PODS

  • Leg/class baseline: Expected Marginal Seat Revenue (EMSR)

  • Three path/class RM systems available in the current version of PODS

    • NetBP

    • ProBP

    • DAVN


Background pods1

Background: PODS

  • EMSR processes (virtual) classes on leg in fare class order

    • solves for the forecast demand and average fare for the aggregate of all higher classes

    • obtains a protection level of the aggregate against the class

    • sets the booking limit for the class (and all lower classes) as the remaining capacity – protection level


Background pods2

Background: PODS

  • NetBP solves for leg bidprices (shadow price) using a network flow LP equivalent

    • path/class is marked as available if the fare is greater than the sum of the bidprices of the associated legs


Background pods3

Background: PODS

  • ProBP solves for leg bidprices by iterative proration

    • prorate path/class fare by ratio of bidprices of associated legs

    • for each leg order the prorated fares and solve a leg bidprice using standard (EMSR) methodology and re-prorate

    • path/class is marked as available if the fare is greater than the sum of the bidprices of the associated legs


Background pods4

Background: PODS

  • DAVN uses the bidprices from NetBP as displacement costs and then for each leg

    • reduces path/class fare by the displacement from other leg(s)

    • creates (demand equalized) virtual classes

    • uses standard (EMSR) leg/class optimizer to set availability


Architecture

Architecture

  • Embed NetBP/ProBP/DAVN in a MAC shell rather than develop a new optimizer (for now)

  • Use current PODS forecasters and detruncators

    • pickup and regression forecasting

    • pickup, booking curve and projection detruncation

    • aggregate path/class observations into MAC observations

  • Assumption: all spill is contained within a MAC until all paths (of index airline) are closed for the class


Optimizers

allocate MAC forecasts to associated path/classes

solve for leg bidprices

re-allocate spill from newly closed path/classes to open path/classes

close path/classes with fares less than sum of bidprices for the associated legs*

any new path/classes closed?

yes

no

quit

*Rule: no path/class can be re-opened

Optimizers

  • Bidprice engine (NetBP, ProBP)


Optimizers1

allocate original MAC forecasts to associated path/classes and create virtual classes using final MAC bidprices

solve for leg/virtual class availability

recalculate leg/virtual class demand

close path/classes that have been assigned

to closed virtual classes on associated legs

re-allocate spill from newly closed path/classes to open path/classes

yes

any new path/classes closed?

no

quit

Optimizers

  • Path/class availability solver (DAVN)


Additional technology

Additional technology

  • First-choice preference estimation for paths of a MAC

    • constructed from historical bookings for open paths

    • iterative procedure to account for partial observations (not all paths open for a class)

  • Assumption: second-choice, third-choice,…… preference can be calculated as normalized (removing closed paths) first-choice preference


Additional technology1

Additional technology

  • Estimation of spill-in rate from, spill-out rate to competitor(s)

    • Key idea: equilibrium

      • if the historical fraction of weighted paths open for time frame for the index airline (hfropa) and the competitor(s) (hfropc) is observed

      • and if the the current fraction of weighted paths open is observed for both the index airline and the competitor(s) (fropa, fropc)

      • then when fropc is less than hfropc, spill-in must occur

      • and when fropc is greater than hfropc, spill-out must occur

  • Fraction of competitor paths open inferred from local path/class availability (AVS messages)


Additional technology2

Additional technology

  • Competitor demand estimation

    • based on observed historical market share(which is also a function of equilibrium)

    • uses booking curves to adjust for limited (input) time horizon

  • Spill-in/spill-out defined by adjusted competitor demand and maximum spill-in rate across classes

  • Assumed that once MAC demand modified for spill to/from competitor, all spill is contained within a MAC


Some results

HUBAL 1

20 CITIES

20 CITIES

HUBAL 2

Some results

  • PODS network D

    • 2 airlines

    • 3 banks each

    • 252 legs

    • 482 markets

    • 2892 paths

    • 4 fare classes

  • Demand

    • demand factor = 1.0

    • 50/50 business/leisure


Results 1

Results 1

  • Airline 1 uses one of the path/class systems

    • without a MAC shell

    • with a MAC shell

  • Airline 2 uses the PODS standard leg/class system (EMSR)

  • Results quoted as % revenue gains compared to both airlines using EMSR


Results 11

Results 1

+MAC

+MAC

+MAC

revenue gain

NetBP

ProBP

DAVN


Results 2

Results 2

  • Airlines 1 and 2 follow a sequence of RM using DAVN

    • start with both using EMSR

    • move 1: airline 1 adopts DAVN

    • move 2: airline 2 adopts DAVN

    • move 3: airline 1 adopts DAVN + MAC

    • move 4: airline 2 adopts DAVN + MAC

  • Results quoted as % revenue gains compared to both airlines using EMSR


Results 21

Results 2

revenue gain

AL1 DAVN

AL2 DAVN

AL1 MAC

AL2 MAC


Results 3

Results 3

  • Components of MAC revenue gain

    • optimizer (NetBP, ProBP, DAVN) by itself

    • MAC without spill-in/spill-out

    • MAC spill-in/spill-out

  • Results quoted as % revenue gains compared airline 1 using EMSR


Results 31

revenue gain

NetBP

ProBP

DAVN

Results 3

Note: Mac spill gain dominated by spill-in compared to spill-out


Potential real world application of mac

Potential real-world application of MAC

  • Can’t say how difficult

  • But can propose it will provide for a new level of technical integration of RM and the rest of the airline

    • use of external path preference models to determine first-choice preference, conditional second, third,…. preference and account for the effect of schedule changes

    • use of external marketing data, econometric models, etc. to define at least components of market demand


Lines of inquiry

Lines of inquiry

  • New optimizer that integrates the MAC arguments

    • rather than embedding in a shell

  • Model vertical/diagonal buy-up

    • requires the new optimizer

  • Market-based RM

    • pessimistic unless competitor RM itself is modeled


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