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.

翻译：众所周知，航空货运收益管理与客运收益管理存在显著差异。预订周期短导致的固有需求波动性、货物运输的非均衡性、运力的多维性与不确定性，以及航线规划的灵活性，都是航空货运收益管理面临的挑战。本文提出了一种精心设计的数据驱动收益管理方法，以应对航空货运行业的这些特殊挑战。我们通过针对航空货运环境的仿真实验，比较了不同重量与体积竞价价格处理策略下的运行效果。结果表明，独立运行算法生成重量与体积竞价价格，并将两者相加纳入价格优化模型时，其收益表现最佳，相比其他策略可提升超过3%的收入差距。