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Luis Cadarso (1) , Vikrant Vaze (2) , Cynthia Barnhart (3) , Ángel Marín (1)

Integrated Airline Scheduling: Considering Competition Effects. Luis Cadarso (1) , Vikrant Vaze (2) , Cynthia Barnhart (3) , Ángel Marín (1) (1) Technical University of Madrid (2) Philips Research North- America (3) Massachusetts Institute of Technology.

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Luis Cadarso (1) , Vikrant Vaze (2) , Cynthia Barnhart (3) , Ángel Marín (1)

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  1. Integrated Airline Scheduling: Considering Competition Effects Luis Cadarso(1), VikrantVaze(2), Cynthia Barnhart(3), Ángel Marín(1) (1)TechnicalUniversity of Madrid (2)Philips Research North-America (3)Massachusetts Institute of Technology 2013 ICSO-HAROSA International Workshop on Simulation-Optimization & Internet Computing July 10-12, 2013, Barcelona, SPAIN

  2. OUTLINE OVERVIEW PASSENGER DEMAND OPTIMISATION MODEL STUDY CASE

  3. Overview Integrated Airline Scheduling: Considering Competition… AirlinePlanning • Airline profitability is critically influenced by the airline’s ability to: • Estimatepassenger demands. • Construct profitable flight schedules (the airline schedule planning process). FLEET PLANNING FREQUENCY PLANNING ROUTE EVALUATION COMPETITION TIMETABLE DEVELOPMENT SCHEDULE DEVELOPMENT FLEET ASSIGNMENT CREW SCHEDULING AIRCRAFT ROUTING COMPETITION REVENUE MANAGEMENT

  4. Overview Integrated Airline Scheduling: Considering Competition… Schedule Design & FleetAssignment • Schedule design: among the most important factors that determine passengers’ choice. • Usually decomposed: Frequency planning and Timetable planning. • How many departures in each route (scheduledisplacement)? • When to depart? • Fleet assignment: cost minimising (or profit maximising) assignment of fleets to scheduled flights. • Given: flight schedule; number of aircraft by fleet type; restrictions (flows, noise, maintenance, etc.); costs, potential revenue of flights.

  5. Overview Integrated Airline Scheduling: Considering Competition… IntegratedPlanning • Current practice: sequential solving. • Integrated models: • better feedback; • better choice of schedule and capacity. • Joint optimization and planning is a big challenge: • little data. Difficult to obtain consistent and detailed demand/cost data; • models that capture competitive behaviors. IntegratedApproach FREQUENCY PLANNING TIMETABLE DEVELOPMENT FLEET ASSIGNMENT PASSENGER USE

  6. Overview Integrated Airline Scheduling: Considering Competition… Motivation & Contributions • Traditionally: projected demand fixed. However, it depends on: mode, airline, fare, frequency and the market characteristics. • Modes: High speed rail (medium-haul markets)and Air (Legacy and Low Cost Airlines). • Estimation of demands associated with schedules using a nested logitmodel. • Generation of schedules and fleet assignments using an integrated schedule design and fleet assignment model: • passenger demand competition; • and impacts of schedule decisions on passenger demand.

  7. OUTLINE OVERVIEW PASSENGER DEMAND OPTIMISATION MODEL STUDY CASE

  8. PassengerDemand Integrated Airline Scheduling: Considering Competition… Market Share • Airlines compete for passengers: frequency and departure schedule; price charged; quality of service. • Discrete Choice Modelling: commonly used to model customer behavior. • Four elements (Decision maker, Alternatives, Attributes, Decision rule). • Multinomial logit models frequently used (itinerary choice, recapture rate, etc.).

  9. PassengerDemand Integrated Airline Scheduling: Considering Competition… More Attributes & ModeCompetition • Modellingthe market share as simply a function of the frequency share is not enough(fares, travel times and passenger types). • High Speed Rails have become an important competitor. • The choice can be modeled using a nested multinomial logit model. • Two levels: mode (air vs. rail) and operator. 9

  10. PassengerDemand Integrated Airline Scheduling: Considering Competition… ModelEstimation • Maximum likelihood. • 728 sets of data: market share, frequencies, itineraries, travel time and price. • Significant at the 0.95 confidence level using a classic Student’s t-test.

  11. OUTLINE OVERVIEW PASSENGER DEMAND OPTIMISATION MODEL STUDY CASE

  12. Optimisation Model Integrated Airline Scheduling: Considering Competition… OptimisationModel • We are integrating several sub-problems: • frequency planning; • approximate timetable development ; • and fleet assignment; • all while considering passenger demand variation with schedule. • Aim: determine the frequency by fleet type and by time period of the day for all the flights given airport capacities, fleet sizes, average fares, unconstrained demands and competitors’ schedules.

  13. Optimisation Model Integrated Airline Scheduling: Considering Competition… Air Network & PassengerDemand • Air network: airports and all feasible airway alternatives. • Slot availability is known for each airline. • Flight leg (departure airport-time period and arrival airport-time period). • Aggregated network (Different discretization: congested vs. non-congested). • Unconstrained demand: origin, destination and desired departure time. • Fixed and known. • Demand captured: competition effects (nested logitmodel).

  14. Optimisation Model Integrated Airline Scheduling: Considering Competition… Notations • Decision variables: • : frequency of flight f with fleet type π. • : number of planes of fleet type π on the ground at the beginning in airport g. • : number of passengers in itinerary i. • : frequency value in od pair offered by airline a. • Parameters: • : fleet size. • : cost of assigning fleet type π to flight leg f. • : revenue per passenger in market w. • Sets: • : set of flightlegs. • : set of fleettypes. • : set of airports. • : set of origin-destinationpairs.

  15. Optimisation Model Integrated Airline Scheduling: Considering Competition… ModelFormulation Objective function Demand Flight capacity Frequencies Aircraft count Block time + Slot availability, symmetry, minimum service, fleet size, etc.

  16. OUTLINE OVERVIEW PASSENGER DEMAND OPTIMISATION MODEL STUDY CASE

  17. Study Case Integrated Airline Scheduling: Considering Competition… Study Case • Asimplified version of IBERIAS’s air network: the Spanish network. • It is a pure hub-and-spoke network with 23 different airports. • Planning period: 7 days. • The planning must be periodic. • Three different fleet types.

  18. Study Case Integrated Airline Scheduling: Considering Competition… Study Case • We present two different case studies: • Base case scenario: a measure of how the model solutions fit to the reality. Sensitivity analysis in the maximum allowable load factor: • to validate the presented approach; • and to verify the robustness of the model formulation. • New scenario: the entry of high speed rail in a market (stimulation of demand in the market).

  19. Study Case Integrated Airline Scheduling: Considering Competition… Base Case Scenario

  20. Study Case Integrated Airline Scheduling: Considering Competition… Base Case Scenario Comparison of the frequencies per fleet type given by the model with the airline's actual schedule

  21. Study Case Integrated Airline Scheduling: Considering Competition… Base Case Scenario Percentage difference in the fleet utilization with respect to IBERIA's actual values Percentage difference in the air market share in the markets where HSR operates

  22. Study Case Integrated Airline Scheduling: Considering Competition… Impacts of the Entry of High Speed Rail • High speed rails: strong source of competition in Spain developed by the government. • High speed rails mean an important loss in the market share for the airlines. • The Spanish government is planning high speed rails to Galicia.

  23. Study Case Integrated Airline Scheduling: Considering Competition… Impacts of the Entry of High Speed Rail • Unconstrained demand given and fixed: reasonable when entry of new operators is unlikely. If such an entry occurs: demand stimulation. • Model unconstrained demand variation with the frequency (schedule displacement) and average price (no demographic changes, planning period of months). market sizing parameter (constant). price elasticity of demand. time elasticity of demand. average price of travel. constant expressed in hours. average trip time. frequency value. schedule displacement component.

  24. Study Case Integrated Airline Scheduling: Considering Competition… Impacts of the Entry of High Speed Rail Average load factor predicted by the model in response to HSR entry Model's response to HSR

  25. Study Case Integrated Airline Scheduling: Considering Competition… Impacts of the Entry of High Speed Rail • Total profit prediction reaches a constant value (increments in HSR’s frequency value have no effect on IBERIA’s profit). • Moderate ticket price reduction (10% and 20%): total profit predicted greater. • Aggressive discounts do not result in increases in profit. Model's predicted total profit variation in response to HSR entry.

  26. Integrated Airline Scheduling: Considering Competition… Conclusions • Tactical competition model for an airline-considering multi-modal competition. Integrated optimization schedule design model: • frequency planning; • approximate timetable development; • fleet assignment; • and passenger demand choice. • Nested logit model using real data provided by IBERIA. • Our integrated optimization model is able to replicate IBERIA's current decisions. • Predict response to market changes (competitors and entry of new operators). • Hence, proposed modelling approach attractive from the perspective of the operator.

  27. Integrated Airline Scheduling: Considering Competition… A Look to the Future • Departure time competition (timetable was approximate). • Price competition. • Game-theoretic models and results that provide insights into equilibrium. • Passenger demand fluctuations. • Robust plans: connecting passengers. • Integrated recovery plans.

  28. Integrated Airline Scheduling: Considering Competition… A Look to the Future: Other • Demand fluctuations arising from stochastic demands in railway planning. • Demand choice modelling in railways. • Recovery in railways: quality (schedule changes). Control the recovery length. • Recoverable robustness in railways: limited effort in the recovery. • Robustnetworkdesign. • Recoverablerobustness in networkdesignproblems.

  29. THANK YOU ANY QUESTIONS? LUIS CADARSO luis.cadarso@upm.es

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