TY - GEN
T1 - Predicting show rates in air cargo transport
AU - Brieden, Andreas
AU - Gritzmann, Peter
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/2
Y1 - 2020/2
N2 - Overbooking is an important tool for revenue optimization in airline industry both, for passenger and cargo transportation. While the former is 'binary and one-dimensional' as the passengers either show up or not, the latter is more difficult. In particular, a commodity might show up for transport but both, its actual weight and volume, might differ significantly from the values specified in the booking. A reliable prediction of the show rates is therefore instrumental for any reasonable revenue optimization in air cargo industry. The present paper presents a new mathematical optimization model for predictive analytics. The exposition focusses, on the one hand, on the theoretical background of our approach which combines statistics, diagrams, clustering and data-transformations. On the other hand, we report on the successful application on (near) real world data from air cargo industry.
AB - Overbooking is an important tool for revenue optimization in airline industry both, for passenger and cargo transportation. While the former is 'binary and one-dimensional' as the passengers either show up or not, the latter is more difficult. In particular, a commodity might show up for transport but both, its actual weight and volume, might differ significantly from the values specified in the booking. A reliable prediction of the show rates is therefore instrumental for any reasonable revenue optimization in air cargo industry. The present paper presents a new mathematical optimization model for predictive analytics. The exposition focusses, on the one hand, on the theoretical background of our approach which combines statistics, diagrams, clustering and data-transformations. On the other hand, we report on the successful application on (near) real world data from air cargo industry.
KW - Air cargo
KW - Mathematical optimization
KW - Predictive analytics
KW - Revenue management
UR - https://www.scopus.com/pages/publications/85084188813
U2 - 10.1109/AIDA-AT48540.2020.9049209
DO - 10.1109/AIDA-AT48540.2020.9049209
M3 - Conference contribution
AN - SCOPUS:85084188813
T3 - 2020 International Conference on Artificial Intelligence and Data Analytics for Air Transportation, AIDA-AT 2020
BT - 2020 International Conference on Artificial Intelligence and Data Analytics for Air Transportation, AIDA-AT 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 1st International Conference on Artificial Intelligence and Data Analytics for Air Transportation, AIDA-AT 2020
Y2 - 3 February 2020 through 4 February 2020
ER -