A combined forecasting and packing model for air cargo loading: A risk-averse framework

I. Tseremoglou*, A. Bombelli, Bruno F. Santos

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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Abstract

In this paper, we present a combined forecasting and optimization decision-support tool to assist air cargo revenue management departments in the acceptance/rejection process of incoming cargo bookings. We consider the case of a combination airline and focus on the passenger aircraft belly capacity. The process is dynamic (bookings are received in a discrete fashion during the booking horizon) and uncertain (for some bookings the three dimensions are not provided, while the actual belly space available for cargo is only revealed a few hours before departure). Hence, analysts base decisions on historical data or human experience, which might yield sub-optimal or infeasible solutions due to the aforementioned uncertainties. We tackle them by proposing data-driven algorithms to predict available cargo space and shipment dimensions. A packing problem is solved sequentially once a new booking request is received, predicting shipment dimensions, if necessary, and considering the uncertainty of such prediction. The booking is accepted if it results in a feasible loading configuration where no previously accepted booking is offloaded. When applied in a deterministic context, our packing method outperformed the one used by the partner airline, increasing the loaded volume up to 20%. The framework was also tested assuming unknown shipment dimensions, comparing a risk-prone and a risk-averse strategy, with the latter accounting for uncertainty in dimension predictions and the former using mean values. While the average loaded volume decreases in the risk-averse case, the number of unplanned offloadings due to under-predicted dimensions decreases from 54% to 12% of the simulated cases, hence yielding a more robust acceptance strategy.
Original languageEnglish
Article number102579
Number of pages27
JournalTransportation Research. Part E: Logistics and Transportation Review
Volume158
DOIs
Publication statusPublished - 2022

Keywords

  • Air cargo loading
  • Deep neural networks
  • Extreme points
  • Knapsack problem
  • Prediction uncertainty
  • Unit load devices

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