L UIS C ADARSO (1), V IKRANT V AZE (2), C YNTHIA B ARNHART (3), Á NGEL M ARÍN (1) (1) T ECHNICAL U...

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

Integrated Airline Scheduling: Considering Competition Effects

Luis Cadarso July 2013 2

OUTLINE

1. OVERVIEW

2. PASSENGER DEMAND

3. OPTIMISATION MODEL

4. STUDY CASE


Airline PlanningOverview

FLEET PLANNING

ROUTE EVALUATION

SCHEDULE DEVELOPMENT

FREQUENCY PLANNING

TIMETABLE DEVELOPMENT

FLEET ASSIGNMENT

CREW SCHEDULINGAIRCRAFT ROUTING

REVENUE MANAGEMENT

COMPETITION

COMPETITION

Airline profitability is critically influenced by the airline’s ability to:

1. Estimate passenger demands.

2. Construct profitable flight schedules (the airline schedule planning process).

Integrated Airline Scheduling: Considering Competition…

4Luis Cadarso July 2013

Schedule design: among the most important factors that determine passengers’ choice.

o 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.

Schedule Design & Fleet Assignment Integrated Airline Scheduling: Considering Competition… Overview


Integrated PlanningIntegrated Airline Scheduling: Considering Competition… Overview

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.

FREQUENCY PLANNING

TIMETABLE DEVELOPMENT

FLEET ASSIGNMENT

Integrated Approach

PASSENGER USE


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.

Motivation & ContributionsIntegrated Airline Scheduling: Considering Competition… Overview

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).


OUTLINE

1. OVERVIEW

2. PASSENGER DEMAND


4. STUDY CASE


Market SharePassenger Demand

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.).


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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.

More Attributes & Mode Competition

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Integrated Airline Scheduling: Considering Competition… Passenger Demand


Parameter Estimation Std Error p-value1.5137 0.009 0.006-0.4186 0.035 0.008-1.5050 0.042 0.0170.8265 0.052 0.023

1.18201 0.021 0.010-0.9347 0.063 0.012-1.3251 0.112 0.0171.0853 0.047 0.0090.6583 0.073 0.019-0.8568 0.129 0.0310.9195 0.109 0.025-0.4451 0.093 0.018-0.8844 0.184 0.0371.4343 0.152 0.034

11

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rail

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rail

railasc

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.

Model EstimationIntegrated Airline Scheduling: Considering Competition… Passenger Demand


OUTLINE

1. OVERVIEW

2. PASSENGER DEMAND


4. STUDY CASE


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.

Optimisation ModelIntegrated Airline Scheduling: Considering Competition…


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).



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 flight legs.: set of fleet types.

: set of airports.

: set of origin-destination pairs.



Model Formulation

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f z od OD

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, ,g f f gk PK f DF f AFk k k

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+ Slot availability, symmetry, minimum service, fleet size, etc.

Objective function

Demand

Flight capacity

Frequencies

Aircraft count

Block time



OUTLINE

1. OVERVIEW

2. PASSENGER DEMAND


4. 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.

Study CaseStudy CaseIntegrated Airline Scheduling: Considering Competition…


Study CaseStudy 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:

o to validate the presented approach;

o 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).



(%) (%) (%)0.85 12.43 28.10 13.720.9 9.72 19.45 10.34

0.95 4.05 10.54 6.691 3.01 6.21 3.20

1.05 4.05 10 4.371.1 6.21 14.86 5.99

1.15 8.64 20.54 8.09

1MAPE 2MAPE 3MAPE

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Study Case

Base Case ScenarioIntegrated Airline Scheduling: Considering Competition…


Study Case


Comparison of the frequencies per fleet type given by the model with the airline's actual schedule


Study Case


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


Impacts of the Entry of High Speed RailStudy Case

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.



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).

Study Case

Impacts of the Entry of High Speed RailIntegrated Airline Scheduling: Considering Competition…

odw w aod a Ad M p tt sd f

wM

odp

tt

odf

sd

odsd f

market sizing parameter (constant).

average price of travel.

constant expressed in hours.

schedule displacement component.

average trip time.

frequency value.

price elasticity of demand.

time elasticity of demand.


Study Case


Model's response to HSR Average load factor predicted by the model in response to HSR entry


Study Case

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.


ConclusionsIntegrated Airline Scheduling: Considering Competition…

• Tactical competition model for an airline-considering multi-modal competition. Integrated optimization schedule design model:

o frequency planning;o approximate timetable development;o fleet assignment;o 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.


A Look to the FutureIntegrated Airline Scheduling: Considering Competition…

• 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.


A Look to the Future: OtherIntegrated Airline Scheduling: Considering Competition…

• 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.

Luis Cadarso July 2013

LUIS [email protected]

29

THANK YOUANY QUESTIONS?

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L UIS C ADARSO (1), V IKRANT V AZE (2), C YNTHIA B ARNHART (3), Á NGEL M ARÍN (1) (1) T ECHNICAL U...

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