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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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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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