Intelligent Aggressiveness: Using Forecast Multipliers, Hybrid Forecasting, Fare Adjustment, and Unconstraining Methods to Increase Revenue*
研究了航空公司使用四种智能激进杠杆（预测乘数、解除限制、混合预测和票价调整）在不同网络和需求水平下对收入的影响，发现混合预测在受限票价环境中效果最优，收入提升0.4%至6.3%。
ABSTRACT Many studies have begun the exploration of airlines using intelligent aggressiveness (IA) in unidimensional directions (e.g., forecast multipliers alone). This article uses the sophisticated passenger origin–destination simulator (PODS) to examine the revenue impact of four different IA levers—forecast multipliers, unconstraining, hybrid forecasting (HF) and fare adjustment (FA). We also explore the impacts in two different origin–destination networks. Due to the competitive nature of PODS (two or four airlines competing) and its allowance for customer choice, we are able to assess all the implications, including the impact of spill, upgrades and recapture. We find that with a single IA lever, independent of the network and demand level, in a more‐restricted fare environment, the optimal lever is almost always HF with moderate‐to‐aggressive estimates of willingness‐to‐pay, with revenue gains of 0.4–4.3% in a large global network, and gains of 1.7–4.2% in a domestic network, depending on demand level and optimization method used. We also test two additional, less‐restricted fare environments and find that revenue improvements have a wider range (0.8–6.3%) with a single lever in the larger network. Finally, we explore the impacts of allowing the competitors to use basic IA and the airline of interest to use multiple IA levers.