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PODS Update Large Network O-D Control Results. Peter Belobaba and Seonah Lee Massachusetts Institute of Technology AGIFORS RM STUDY GROUP MEETING New York City March 22-24, 2000. Outline. Description of New Large PODS Network Standardization of RM and O-D Method Parameters

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pods update large network o d control results

PODS UpdateLarge Network O-D Control Results

Peter Belobaba and Seonah Lee

Massachusetts Institute of Technology

AGIFORS RM STUDY GROUP MEETING

New York City

March 22-24, 2000

outline
Outline
  • Description of New Large PODS Network
  • Standardization of RM and O-D Method Parameters
    • DAVN parameters – re-optimization, virtual bucket definition
    • Re-optimizing rate for bid price methods (HBP and PROBP)
  • Results: O-D Revenue Gain Comparisons
    • Impacts of Average Load Factors and Distributions
  • Overview of Additional PODS Studies
    • Use of Path-Based (ODF) Forecasts in Leg/Bucket RM
    • Introduction of Cancellation and No-Show Behaviors
    • Recovery of RM Methods from Sudden Demand Shocks
characteristics of large network
Characteristics of Large Network
  • 40 spoke cities with 2 hubs, one for each airline
  • 20 spoke cities on each side, located by geographical coordinates of actual US cities
  • Distance -- 125 to 1514 miles to the hub from spoke cities
  • Unidirectional -- West to east flow of traffic
  • Inter-hub services -- one for each direction, for each bank, for each airline
  • 3 banks starting at 10:30, 14:00, 17:30 for each airline hub
  • 252 flight legs, 482 O-D markets, 4 fare types per market
geographical layout
Geographical Layout

1

H1(41)

2

21

3

4

5

25

6

23

24

27

26

7

31

28

30

8

29

32

33

22

9

11

34

35

38

10

12

14

15

13

16

H2(42)

36

17

18

37

19

39

20

40

standardization of o d rm methods
Standardization of O-D RM Methods
  • “Generic” RM method parameters defined 3 years ago for smaller PODS networks (6-10 cities):
    • 4 fare classes for Base Case EMSRb Control
    • 6 virtual buckets per leg for GVN, HBP and DAVN
    • Network-wide virtual range definitions
    • Varying re-optimization rates for bid price methods
  • For new 40-city network, we updated RM methods:
    • “Standard” definitions to better reflect actual and feasible implementations of each method
standardized rm method parameters
Standardized RM Method Parameters
  • FCYM -- Fare Class Yield Management
    • 4 fare classes grouped by yields and fare restrictions
    • Leg/class demand data and forecasting
    • EMSRb limits -- Re-optimize at 16 checkpoints
  • GVN -- Greedy Virtual Nesting
    • ODFs mapped to 8 virtual buckets based on total itinerary fare values
    • Network-wide virtual ranges for all legs
    • Leg/bucket demand data and forecasting
    • EMSRb limits -- Re-optimize at 16 checkpoints
standardized rm method parameters7
Standardized RM Method Parameters
  • HBP -- Heuristic Bid Price
    • Like GVN, ODFs mapped to 8 virtual buckets based on total itinerary fare values
    • Same network-wide virtual ranges for all legs
    • Leg/bucket demand data and forecasting
    • EMSRb booking limit control for local (one-leg) itineraries -- re-optimized 16 times before departure
    • “Bid price” control for connecting requests based on current EMSR values of last seat on each leg:
      • Re-optimized daily over 63-day PODS booking period
standardized rm method parameters8
Standardized RM Method Parameters
  • DAVN -- Displacement Adjusted Virtual Nesting
    • ODFs mapped to 8 virtual buckets based on displacement adjusted “network” revenue values:
      • Network Value = ODF Fare - Displacement Cost
    • Leg Displacement Costs estimated by shadow prices of deterministic network LP optimization
      • Network re-optimized at each checkpoint (16 times)
      • Leg-specific virtual bucket range definitions
    • ODF demand forecasting (rolled up to leg/bucket)
    • EMSRb control of leg/buckets -- 16 checkpoints
standardized rm method parameters9
Standardized RM Method Parameters
  • PROBP--Probabilistic Network Bid Price
    • Nested probabilistic network convergence algorithm developed at MIT (Bratu, 1998)
    • Involves “prorating” total ODF value to legs traversed:
      • Requires ODF data demand forecasts
      • Estimates “critical EMSR operator” for each leg by accounting for complete nesting of ODF availabilities
    • Critical EMSR values used as additive bid prices for local and connecting path requests
      • Re-optimized daily over 63-day PODS booking period
summary of new rm parameters
Summary of New RM Parameters
  • Base Case Fare Class YM effectively unchanged
  • Enhancements to virtual bucket methods:
    • Number of virtual buckets increased to 8
    • More frequent network displacement optimization and leg-specific virtual re-bucketing for DAVN
    • Represents “advanced” implementations of DAVN
  • More realistic bid price re-optimization frequency:
    • Airline consensus that daily bid price updates are feasible in larger networks
    • Theoretically more frequent updates might be misleading
demand and load factors simulated
Demand and Load Factors Simulated
  • Under FCYM Base Case, simulated demand factors led to network ALFs from 70% to 87%
    • Load factor distributions compared well with system data provided by 2 airlines
  • Local traffic represents 37 to 40% of total load by flight leg, on average:
    • Varies by demand factor and RM methods used
  • Differences in load factors by connecting bank at each hub:
    • Highest for mid-day bank, lowest early in morning
alfs by hub connecting bank
ALFs by Hub Connecting Bank
  • 3 banks per day offered at each airline’s hub:
    • Range of ALFs and revenue gains for each RM method
    • Most realistic traffic characterization in PODS to date
comparison of o d revenue gains
Comparison of O-D Revenue Gains
  • Relative performance in line with smaller network:
    • Small gains for GVN, negative at higher demands
    • HBP revenue improvements over “greediness” of GVN
    • DAVN and PROBP perform best, gains of 1% or more
  • But, overall % gains of O-D methods are lower:
    • New network not designed to be “O-D friendly”
    • Each demand factor includes a range of ALFs by bank, with lower % gains for lower demand banks
    • More path choices without airline preferences or re-planning disutilities result in greater passenger shifts among paths
competitive impacts of o d methods
Competitive Impacts of O-D Methods
  • O-D control can have substantial revenue impacts on competitor:
    • Continued use of FCYM against O-D methods results in revenue losses for Airline B
    • Interesting is GVN result, where Airline B’s revenue loss is greater than Airline A’s gain
    • Still not a zero-sum game, as revenue gains of Airline A exceed revenue losses of Airline B
    • Other simulation results show both airlines can benefit from using more sophisticated O-D control
lessons from larger network
Lessons from Larger Network
  • Demand characteristics affect O-D benefits:
    • No explicit effort to design “bottleneck” legs that favor GVN
    • More realistic distribution of load factors across legs
    • Different load factors for connecting banks by time of day
    • Misleading to focus comparisons on peak connecting banks
  • Characterization of O-D methods also critical:
    • More sophisticated DAVN parameters, more realistic PROBP re-optimization frequency
    • Robustness of DAVN even with periodic re-optimization
  • O-D control has important competitive impacts
large network in pods next steps
Large Network in PODS: Next Steps
  • Alternative demand and network characteristics:
    • Proportion of local vs. connecting O-D demand
    • Load factor distributions
    • Business vs. leisure traffic mix
  • Impacts of passenger choice disutility parameters:
    • Increase re-planning costs for changing preferred times
    • Modify airline preference factors from 50/50
    • Introduce path quality options (non-stops) and disutilities
  • Less structured and more “realistic” O-D fares:
    • Not necessarily tied to O-D market distances
overview of other pods studies
Overview of Other PODS Studies
  • Path-Based (ODF) Forecasting in Leg-Based RM
  • Introduction of Cancellation and No-Show Rates
  • Impacts of Sudden Demand Shocks
  • Competitive Studies Planned and Under Way
path based forecasting in leg rm
Path-Based Forecasting in Leg RM
  • Preliminary results show potential gains from use of path-based (ODF) forecasts in leg-based RM:
    • ODF database to keep historical booking data
    • Tested simple moving average “pick-up” forecasts with “booking curve” unconstraining
    • ODF forecasts “rolled up” to leg/class or leg/bucket
  • ODF forecasts not necessarily more “accurate”:
    • Error relative to mean forecast is large due to small numbers
    • But ability to unconstrain demand by ODF path appears to contribute in large part to revenue gains
cancellation and no show rates
Cancellation and No-show Rates
  • Over past several months, we have incorporated cancellation and no-show processes into PODS:
    • “Memory-less” daily cancellation probability
    • Gaussian distributions of no-show rates at departure
    • Probabilistic overbooking model to determine AUs
  • Neither process has a large impact on revenue gains of O-D methods:
    • Relative performance of methods stays the same at similar load factors; O-D methods do slightly better at lower ALFs
  • Now testing gross vs. net booking forecast models
impacts of sudden demand shock
Impacts of Sudden Demand Shock
  • Simulated “overnight” demand shifts of +/- 20%:
    • Extreme test of robustness of each RM method to changes in actual demand vs. forecast
    • Compared percentage revenue gains of each method vs. FCYM before and after demand shock
  • After 20% sudden demand decrease:
    • GVN benefited, showing immediate revenue increase
    • DAVN and PROP suffered, due to over-forecasts by ODF
    • HBP maintained relative revenue gains
    • Relative performance stabilized after 12-14 samples
competitive studies with pods
Competitive Studies with PODS
  • Introduction of third “new entrant” airline in one or more spoke-hub local markets:
    • What are impacts on hub carrier that uses leg vs. O-D RM?
    • What are “rational” vs. “predatory” responses by hub carrier in terms of prices, capacity and RM controls?
  • System-wide reduction of aircraft capacity (6%?) by one hub airline to increase legroom:
    • Revenue and load impacts with leg-based vs. O-D RM?
    • What increase in airline preference is needed to make up for revenue losses?
summary pods rm research
Summary: PODS RM Research
  • After four years of development, PODS network is now approaching “realistic” characterization.
  • Change in recent emphasis of PODS simulations:
    • Away from O-D method “competitions”
    • Towards understanding major impacts on RM performance
  • Ability to simulate larger networks opens up even greater potential for PODS research:
    • Airline alliances and other competitive strategies
    • Impacts of pricing and schedule changes on RM methods
    • Inclusion of scheduling and fleet assignment models
pods revenue management research at mit
PODS Revenue Management Research at MIT
  • MIT PODS Consortium of 6 international airlines
  • Major accomplishments in past year:
    • Expansion of PODS network -- 40 cities, 2 airlines, multiple banks per day
    • Establishment of “implementable” O-D methods
    • Focus on sell-up models and interaction with forecasts
    • Impacts on RM method performance of forecasting, demand shocks, fare structures, cancellations
    • New competitive studies involving RM
      • Alliance RM Strategies
      • Impacts of New Entrant Airlines