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The agenda below is subject to change without notice.

Unless otherwise specified, all events take place at the Royal Sonesta Chicago River North, Gallery Ballroom

Monday, May 6th, 2024
Time Presentation Title Affiliation and Authors Theme
Drinks and heavy hors d'oeurves

Royal Sonesta Chicago River North, Canvas Room
AGIFORS Social Event

Tuesday, May 7th, 2024
Time Presentation Title Affiliation and Authors  Theme
8:00 - 8:45 Registration open (coffee + pastries available)    
8:45 - 9:00 Welcome from AGIFORS and United Airlines AGIFORS   
9:00 - 9:25

Enhancing Robustness to Forecast Errors in Availability Control for Airline Revenue Management

T. Gonçalves, B. Almada-Lobo
Demand Forecasting & Estimation
9:25 - 9:50 Price Endogeneity in Demand Models for Revenue Management  Embry-Riddle Aeronautical University
S. Mumbower
Demand Forecasting & Estimation
9:50 - 10:15 When good is good enough: A discrete approach to forecast calibration  Amadeus
A. Matson
Demand Forecasting & Estimation
10:15 - 10:40 Cross Elasticity in Airline Revenue Management United Airlines
A. Bhardwaj, A. Jain
Demand Forecasting & Estimation
10:40 - 11:00 MORNING BREAK     
11:00 - 11:25

Enhancing Airline RM Measurement Capabilities Through Rigorous and Disciplined Experimentation 

C. Nauerz, P. Gorgi, V. Antsibor
A/B Testing and Experimentation
11:25 - 11:50 AI-driven Pricing of Airline Seats: Evidence From a Large-scale Online Field Experiment  Lufthansa Group, PROS
A. Furtuna, G. Sarlas, R. Kumar, S. Boluki, D. Walczak
A/B Testing and Experimentation
11:50 - 12:15 Automated Benefits Estimation for Personalized Recommendations with Experimentation Engines  Sabre
R. Ratliff, B. Rundquist
A/B Testing and Experimentation
12:15 - 12:30 Sponsor Presentation - The Evolution to Offers and Orders Accelya
T. Radcliffe
Sponsor Presentation
12:30 - 1:30 LUNCH     
1:30 - 1:55 Transforming Revenue Management: Air India's Journey with Data Science and Advanced Analytics  Air India
K. Jha, A. Rawat
Applications of AI and ML in RM
1:55 - 2:20 Generative AI & LMM: Transforming Airline RM 
U. Yerushalmi, A. Cohen
Applications of AI and ML in RM
2:20 - 2:45 Revenue forecast: machine learning model vs. analytical approach 
LOT Polish Airlines
K. Macielak, M. Gorczyca, M. Łukaszewski, P. Radon, D. Śliwiński
Applications of AI and ML in RM
2:45 - 3:10 Application of Generative AI for Airline Demand Forecasting  Amadeus
T. Fiig, S. Nanty
Applications of AI and ML in RM
3:10 - 3:30 AFTERNOON BREAK     
3:30 - 3:55 Using Simulations to Improve Revenue Management Systems  Breeze Airways
S. Druzdzel
Simulations in Revenue Management
3:55 - 4:20 Calibration of networks for competitive RM simulation tools Georgia Tech
L. Garrow

Simulations in Revenue Management
4:20 - 4:50 Sponsor Presentation - Measuring Impact in Revenue Management acmetric
C. Nauerz 
Sponsor Presentation
4:50 - 5:00 Update on Anna Valicek and Ken Wang Scholar Programs AGIFORS
R. Cleaz-Savoyen
5:00END OF DAY 1  
6:30 - 9:15 Evening Social Event (Boarding 6:15pm)
Dinner Cruise on Lake Michigan - depart Navy Pier
  Social Event

Wednesday, May 8th, 2024
Time Presentation Title Affiliation and Authors Theme
8:00 - 9:00 Morning coffee    
9:00 - 9:45

Keynote Address from Scott Kirby, CEO, United Airlines

United Airlines
S. Kirby
Keynote Address
9:45 - 10:10 An Adaptive Data-Driven Approach to Air Cargo Revenue Management PROS
E. Eren
Air Cargo Revenue Management
10:10 - 10:35 Mid-term decision making in airline cargo using machine learning FLYR
A. Garg, N. Shukla
Air Cargo Revenue Management
10:35 - 10:55 MORNING BREAK     
10:55 - 11:20

Dynamic pricing for airline ancillary products with machine learning

Air France
J. Bruno, A. Winckels, S. Ghamloush
Ancillary and Offer Optimization
11:20 - 11:50 Sponsor Presentation - How data science enables the airline retailing transformation Amadeus
Y.-M. Piana, M. Wittman
Sponsor Presentation
11:50 - 12:05

Sponsor Presentation - State of the industry: The journey towards dynamic offers

T. Gregorson
Sponsor Presentation
12:05 - 1:05 LUNCH     
1:05- 1:30 Unlocking Revenue Potential in Complex Networks using Hybrid Forecasting: Collaborative Study with Amtrak  ExPretio Technologies
K. Pérez, B. Narasimhan, V. de Sousa
Network Management
1:30 - 1:55 Optimizing Airline Operations through Integrated Flight Scheduling and Revenue Management
University of Massachusetts, Dartmouth
S. Sibdari, H. Nouinou
Network Management
1:55 - 2:20 Generating optimal bid prices via reinforcement learning with batch and shape constraints FLYR
A. Gupta, N. Shukla
Bid Prices and Optimization
2:20 - 2:40 AFTERNOON BREAK     
2:40 - 3:05 Independent optimization or not United Airlines
C. Keceli, A. Jain
Bid Prices and Optimization
3:05 - 3:30 Shadow prices of demand and applications Finnair
F. Nikitin
Bid Prices and Optimization
3:30 - 3:45 Presentation award voting and conference end AGIFORS

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