PODS Update Large Network O-D Control Results - PowerPoint PPT Presentation

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

• 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

• 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

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H1(41)

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H2(42)

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• “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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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?

• 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

• 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