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PODS Update Large Network O-D Control Results

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

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  1. 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

  2. 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

  3. 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

  4. 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

  5. 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

  6. 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

  7. 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

  8. 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

  9. 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

  10. 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

  11. 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

  12. 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

  13. Revenue Gains over FCYM(Competitor uses FCYM)

  14. 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

  15. Revenue Gains by Connecting Bank(Network ALF=83%, Competitor uses FCYM)

  16. Competitive Impacts of O-D Methods(Network ALF=83%, Competitor uses FCYM)

  17. 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

  18. 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

  19. 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

  20. 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

  21. 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

  22. Example: Path Forecasts for Leg RM(Previous Large Network ALF=75%)

  23. 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

  24. 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

  25. 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?

  26. 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

  27. 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

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