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 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 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 H1(41) H2(42) 1 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 “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 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

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 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 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 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 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 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

Revenue Gains over FCYM (Competitor uses FCYM)

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

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

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

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 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 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 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 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

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

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 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 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 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 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