1. 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
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. Airline Planning COMPETITION COMPETITION Overview
Integrated Airline Scheduling: Considering Competition…
Overview
Airline Planning
Airline profitability is critically influenced by the airline’s ability to:
Estimate passenger 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. Schedule Design & Fleet Assignment
Integrated Airline Scheduling: Considering Competition…
Overview
Schedule Design & Fleet Assignment
Schedule design: among the most important factors that determine passengers’ choice.
Usually decomposed: Frequency planning and Timetable planning.
How many departures in each route (schedule displacement)?
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. Integrated Planning Integrated Approach PASSENGER USE Overview
Integrated Airline Scheduling: Considering Competition…
Overview
Integrated Planning
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.
Integrated Approach
FREQUENCY PLANNING
TIMETABLE DEVELOPMENT
FLEET ASSIGNMENT
PASSENGER USE

6. Motivation & Contributions
Integrated Airline Scheduling: Considering Competition…
Overview
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 logit model.
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. Market Share Passenger Demand
Integrated Airline Scheduling: Considering Competition…
Passenger Demand
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. More Attributes & Mode Competition
Integrated Airline Scheduling: Considering Competition…
Passenger Demand
More Attributes & Mode Competition
Modelling the 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. Model Estimation Passenger Demand Maximum likelihood.
Integrated Airline Scheduling: Considering Competition…
Passenger Demand
Model Estimation
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.
Parameter
Estimation
Std Error
p-value
1.5137
0.009
0.006
0.035
0.008
0.042
0.017
0.8265
0.052
0.023
0.021
0.010
0.063
0.012
0.112
1.0853
0.047
0.6583
0.073
0.019
0.129
0.031
0.9195
0.109
0.025
0.093
0.018
0.184
0.037
1.4343
0.152
0.034

11. OUTLINE
OVERVIEW
PASSENGER DEMAND
OPTIMISATION MODEL
STUDY CASE

12. Optimisation Model Optimisation Model
Integrated Airline Scheduling: Considering Competition…
Optimisation Model
Optimisation Model
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. Air Network & Passenger Demand
Integrated Airline Scheduling: Considering Competition…
Optimisation Model
Air Network & Passenger Demand
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 logit model).

14. Notations Optimisation Model Decision variables:
Integrated Airline Scheduling: Considering Competition…
Optimisation Model
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:
𝐹: set of flight legs.
Π: set of fleet types.
𝐺: set of airports.
𝑂𝐷: set of origin-destination pairs.

15. Model Formulation Optimisation Model Objective function Demand
Integrated Airline Scheduling: Considering Competition…
Optimisation Model
Model Formulation
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. Integrated Airline Scheduling: Considering Competition…
Study Case
Study Case
A simplified 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 Study Case We present two different case studies:
Integrated Airline Scheduling: Considering Competition…
Study Case
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. Base Case Scenario Study Case (%) 0.85 12.43 28.10 13.72 0.9 9.72
Integrated Airline Scheduling: Considering Competition…
Study Case
Base Case Scenario
(%)
0.85
12.43
28.10
13.72
0.9
9.72
19.45
10.34
0.95
4.05
10.54
6.69 1
3.01
6.21
3.20
1.05 10
4.37
1.1
14.86
5.99
1.15
8.64
20.54
8.09

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

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

22. Impacts of the Entry of High Speed Rail
Integrated Airline Scheduling: Considering Competition…
Study Case
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. Impacts of the Entry of High Speed Rail
Integrated Airline Scheduling: Considering Competition…
Study Case
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.
average price of travel.
time elasticity of demand.
average trip time.
constant expressed in hours.
frequency value.
schedule displacement component.

24. Average load factor predicted by the model in response to HSR entry
Integrated Airline Scheduling: Considering Competition…
Study Case
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. Model's predicted total profit variation in response to HSR entry.
Integrated Airline Scheduling: Considering Competition…
Study Case
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. A Look to the Future: Other
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.
Robust network design.
Recoverable robustness in network design problems.

29. THANK YOU
ANY QUESTIONS?
LUIS CADARSO