1 / 29

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

malo
Download Presentation

PODS Update Large Network O-D Control Results

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related