It is well-recognized that Air Cargo revenue management is quite different
from its passenger airline counterpart. Inherent demand volatility due to short
booking horizon and lumpy shipments, multi-dimensionality and uncertainty of
capacity as well as the flexibility in routing are a few of the challenges to
be handled for Air Cargo revenue management. In this paper, we present a
data-driven revenue management approach which is well-designed to handle the
challenges associated with Air Cargo industry. We present findings from
simulations tailored to Air Cargo setting and compare different scenarios for
handling of weight and volume bid prices. Our results show that running our
algorithm independently to generate weight and volume bid prices and summing
the weight and volume bid prices into price optimization works the best by
outperforming other strategies with more than 3% revenue gap.

本文介绍了一种面向空运货物行业的数据驱动的收入管理方法，通过模拟实验得出了独立生成重量和体积出价并将其相加进行价格优化的策略相较于其他方法能够获得超过 3% 的额外收入。

基于数据驱动的航空货运收益管理

Data-Driven Revenue Management for Air Cargo

Traditional revenue management relies on long and stable historical data and
predictable demand patterns. However, meeting those requirements is not always
possible. Many industries face demand volatility on an ongoing basis, an
example would be air cargo which has much shorter booking horizon with highly
variable batch arrivals. Even for passenger airlines where revenue management
(RM) is well-established, reacting to external shocks is a well-known challenge
that requires user monitoring and manual intervention. Moreover, traditional RM
comes with strict data requirements including historical bookings and pricing
even in the absence of any bookings, spanning multiple years. For companies
that have not established a practice in RM, that type of extensive data is
usually not available. We present a data-driven approach to RM which eliminates
the need for demand forecasting and optimization techniques. We develop a
methodology to generate bid prices using historical booking data only. Our
approach is an ex-post greedy heuristic to estimate proxies for marginal
opportunity costs as a function of remaining capacity and time-to-departure
solely based on historical booking data. We utilize a neural network algorithm
to project bid price estimations into the future. We conduct an extensive
simulation study where we measure performance of our methodology compared to
that of an optimally generated bid price using dynamic programming (DP). We
also extend our simulations to measure performance of both data-driven and DP
generated bid prices under the presence of demand misspecification. Our results
show that our data-driven methodology stays near a theoretical optimum (<1%
revenue gap) for a wide-range of settings, whereas DP deviates more
significantly from the optimal as the magnitude of misspecification is
increased. This highlights the robustness of our data-driven approach.

本论文提出了一种基于历史预订数据的数据驱动式收益管理方法，使用神经网络算法来预测投标价格估计值，以应对需求波动性和数据限制等挑战。经过模拟研究发现，相比于动态编程方法，该方法在应对需求规模变动方面表现更优秀，并在广泛的设置下保持近理论最优方法（小于 1% 的收入差距）。