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.
Highlights
During the last decades, globalization has significantly fostered the growth of international air trade
In the context of our partner airline and related literature, we identify the following four questions that the Revenue Management (RM) analyst must answer during the whole booking process: 1. What is the capacity of the aircraft? 2
We presented a novel combined forecasting and packing model, developed adopting the RM perspective of the cargo department of a combination airline
Summary
Globalization has significantly fostered the growth of international air trade. This percentage translates to a value of US $5.5 trillion and annual revenues of US $50 billions for the IATA members (IATA, 2019) Notwithstanding this relevance, air cargo for combination airlines is generally considered as of secondary importance with respect to the passenger counterpart, the COVID-19 pandemic revamped cargo operations for many combination carriers. This disparity is reflected into the imbalance of academic works, especially the ones focused on big data and machine learning, addressing the passenger (Chung et al, 2020) and the cargo side. To the best of our knowledge, a novel modeling framework addressing all the phases of the air cargo booking process is developed, while explicitly considering uncertainty in both aircraft capacity and shipment dimensions.
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