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Improving Revenue by System Integration and Cooperative Optimization

Improving Revenue by System Integration and Cooperative Optimization

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Improving Revenue by System Integration and Cooperative Optimization

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  1. Improving Revenue bySystem Integration andCooperative Optimization Reservations & Yield Management Study Group Annual Meeting Berlin 16 - 19 April 2002 Georg Kliewer Sven Grothklags Klaus Weber UNIVERSITY of PADERBORN

  2. Motivation - Planning Processes MarketModeling FleetAssignment Crew Pairing NetworkDesign OperationControl AircraftRotation RevenueManagement Crew Rostering

  3. Agenda • Fleet Assignment Problem • Business Process • Mathematical Model • Optimisation Algorithm • O&D Revenue Management • Current System Integration • System Integration Approaches • Conclusions

  4. Fleet Assignment - Business Process Development SchedulingActivity OperationsControl NetLineApplication NetLine/Plan NetLine/Sched NetLine/Ops weekly fully-dated • Find most Profitable Subfleet Assignment • Use a Subfleet that is Feasible for each Leg • Use the Existing Fleet and no extra Aircraft • Obey Blocktime and Groundtime Constraints Fleet Assignment

  5. Fleet Assignment - Business Process (cont)

  6. Strategic Profitability Evaluation Model • Strategic-PEM is a forecasting model that measures the network profitability contribution of each schedule scenario • The Strategic PEM forecasts are developed on an O&D basis for a representative week for a schedule period • Input Data: • average revenue for the O&Ds • market demand for the O&Ds • cost model • fleeted schedule • Output Data: • overall schedule profitability • profit information for each leg-subfleet combination

  7. Strategic Profitability Evaluation Model (cont) Profitability Evaluation Model Fleet Assignment schedule av. revenues market demand Connection Builder itineraries Market Share Model assignment unconstrained demand prelim. capacity assignment Spill & Recapture Simulated Annealing constrained passenger flow Revenue & CostEstimation objective function for fleet assignment

  8. Fleet Assignment - Mathematical Model PAD leg l xl,f=1 leg l is assigned to subfleet f FRA yv,v+: aircraft flow on the ground xl,f yv,v+ MUC v- v v+ ground arc (v-,v) flight event

  9. Fleet Assignment - Optimisation Algorithm Profitability Evaluation Model Fleet Assignment finalassignment fleet schedule restrictions Update Solution GenerateNeighbor Solution Simulated Annealing Objective Function Accept/Reject

  10. Fleet Assignment - Optimisation Algorithm • Heuristic solution method based on local search S= initial solution T = warming_up do do Snew = neighbor solution (S) cost = cost(Snew) - cost(S) if (cost < 0 or accept(cost,T) ) S = Snew until equilibrium T = update(T) until frozen 1 Equilibrium P Frozen

  11. Fleet Assignment - Results

  12. Agenda • Fleet Assignment Problem • O&D Revenue Management • Business Process • System Overview • O&D Control Parameters • Current System Integration • System Integration Approaches • Conclusions

  13. O&D Revenue Management – Business Process BusinessActivities Development Pricing Revenue Management OperationsControl Application NetLine/Plan NetLine/Sched NetLine/Price ProfitLine NetLine/Ops

  14. GDS GDS O&D Forecast Engine Forecast Building ForecastInterface DB GDS Inventory ... Demand Forecasts O&D Optimiser GDS Control Parameters OptimiserInterface DB O&D Revenue Management – System Overview Forecasts based on either • booking histories • e.g. ODIF POSbooking limits • or • market model and price elasticity model • e.g. fare buckets • or • both

  15. Œ  O&D Revenue Management – O&D Control Parameters Parameters to beused by fleet assignment process O&D control parameters depend on type of optimizer • EMSR varieties • booking class limits per leg / compartment • on various granularity levels, e.g. ODIF POS • bid prices derived from EMSR curve or approximation • fare bucket limits per leg / compartment • revenue bucket limits per leg / compartment • Dynamic programming • bid prices per leg / compartment • booking class limits derived from bid prices Reflect • demand • demand variability • estimated fares

  16. Agenda • Fleet Assignment Problem • O&D Revenue Management • Current System Integration • Cooperative Approach • System Integration Approaches • Conclusions

  17. Current System Integration - Cooperative Approach Profitability Evaluation Model Fleet Assignment Connection Builder itineraries FinalAssignment Market Share Model 1 unconstrained demand 4 Update Solution Fleet Change on a Leg Sequence capacity assignment Simulated Annealing Spill & Recapture 5 3 constrained passenger flow Accept/Reject Revenue & CostEstimation 6 2 Objective Function

  18. Current System Integration - Cooperative Approach (cont) State • Strategic PEM + Fleet Assignment NetLine/Plan • Tactical PEM + Fleet Assignment NetLine/Sched • fully-dated schedule • Basic O&D Fleet Assignment • Approximative revenue estimation • Fast evaluation loop: essential for Simulated Annealing • even faster with internal feedback by passenger flow model

  19. Agenda • Fleet Assignment Problem • O&D Revenue Management • Current System Integration • System Integration Approaches • Internal Feedback • External Feedback by O&D Revenue Management System • System View • Conclusions

  20. System Integration – Internal Feedback Profitability Evaluation Model Fleet Assignment Connection Builder FinalAssignment itineraries Market Share Model 1 3 unconstrained demand Update Solution Fleet Change on a Leg Sequence 4 capacity assignment Simulated Annealing Spill & Recapture Passenger Flow LP-Model passenger flow Accept/Reject Revenue & CostEstimation Dual Variables for Legs 2 5 Objective Function

  21. System Integration – External Feedback by O&D RMS Profitability Evaluation Model Fleet Assignment O&D Revenue Management FinalAssignment Connection Builder itineraries O&DForecast Engine Market Share Model Update Solution O&D Forecasts unconstrained demand Fleet Change on a Leg Sequence Simulated Annealing Capacity Assignment capacity assignment O&D Optimiser Spill & Recapture O&D Control Parameters passenger flow RevenueEstimation Accept/Reject CostEstimation Objective Function

  22. O&DForecast Engine O&D Forecasts Capacity Assignment O&D Optimiser System Integration – System Details O&D Revenue Management Iterative Proration (ProBP) ODIF POS buckets O&D Control Parameters

  23. System Integration – System Details (cont) O&D Revenue Management O&DForecast Engine O&D Forecasts Capacity Assignment O&D Optimiser O&D Control Parameters RevenueEstimation Estimated revenue = sum of EMSR values

  24. O&DForecast Engine vacant seats x x-1 Rt-1(x) = fi + Rt(x-1) O&D Forecasts Rt-1(x) = Rt(x) Capacity Assignment O&D Optimiser time to departure t t-1 Rt-1(x) = max[fi + Rt(x-1), Rt(x)] = max [ufi + Rt(x-u)] uÎ{0, 1} System Integration – System Details (cont) Dynamic Bid Price (DynBP) models revenue management as multistage decision process O&D Revenue Management O&D Control Parameters Bid price matrix per compartment

  25. time to deptarture current optimization point next optimization point t t-1 t-2 C 147.3 146.5 145.8 Decrease 149.5 148.7 147.9 C-1 . C-2 151.8 150.8 150.1 . remaining capacity Increase . System Integration – System Details (cont) Bid price matrix per compartment O&D Revenue Management O&DForecast Engine O&D Forecasts Capacity Assignment O&D Optimiser O&D Control Parameters RevenueEstimation Estimated revenue = sum of bid prices

  26. S = J System Integration – Discussion State Challenges • forecast uncertainty and actual fares taken into account • simple interfaces between fleet assigner and revenue management system • more accurate revenue estimation • two loop system • revenue estimation loop • fleet assignment loop • significant runtime differences • coordination of loops • capacity dependency of revenue management optimization • runtime! Approximation? • partly overlapping system functions (market model, OD-Forecast) • redundancy, requires further integration

  27. Agenda • Fleet Assignment Problem • O&D Revenue Management System • Current System Integration • System Integration Approaches • Conclusions

  28. Conclusions - Fleet Assignment in Practice Simulated Annealing-based Fleet Assignment Optimization • substantial part of commercialFleet Assignment tool Cooperative Approach • applied in Fleet Assignment Systems of ... Integrated Approaches • prototype

  29. Conclusions - Summary • Integration of Fleet Assignment and Revenue Management can improve overall revenue • Facets of the fleet assignment problem introduced:business, mathematics, simulated annealing • Substantial part: Profitability Evaluation Model (PEM) • Revenue management system introduced:particularly important for integration: control parameters • Current state of integration: Cooperative Approach with PEM (without integration of RMS) - successfully used by three airlines • System integration prototype: • based on internal passenger flow model (deterministic LP) • based on external link to O&D RMS: EMSR-based, dynamic programming good better almost perfect

  30. Conclusions - Ongoing Work, Outlook • Ongoing tests of system integration prototype • Market Model used in FA is also used in Pricing • further Integration possible • Integration of the Passenger Flow Model in Fleet Assignment • optimization approach for enhanced O&D Fleet Assignment • Usage in short-term → pre-implementation phase: • booking dependent (demand driven) fleet assignment

  31. Thank you for your attention!Any questions? UNIVERSITY of PADERBORN sven@uni-paderborn.de georg.kliewer@upb.de klaus.weber@lhsystems.com