Capacity Uncertainty in Airline Revenue Management: Models, Algorithms, and Computations
针对航班容量在订票期内可能变化的问题，提出首个整合容量不确定性的航段收益管理模型，用随机场景表示容量更新，给出混合整数规划和组合求解方法，并通过数值实验验证效果。
Most airline revenue optimization models assume capacity to be fixed by fleet assignment, and thus treat it as deterministic. However, empirical data show that on 40% of flights, capacity is updated at least once within the booking horizon. Capacity updates can be caused by fleet-assignment reoptimizations or by short-term operational problems. This paper proposes a first model to integrate the resulting capacity uncertainty in the leg-based airline revenue management process. While assuming deterministic demand, the proposed model includes stochastic scenarios to represent potential capacity updates. To derive optimal inventory controls, we provide both a mixed-integer program and a combinatorial solution approach, and discuss efficient ways of optimizing the special case of a single capacity update. We also explore effects of denied boarding cost and the model’s relationship to the static overbooking problem. We numerically evaluate the model on empirically calibrated demand instances and benchmark it on the established deterministic approach and an upper bound based on perfect hindsight. In addition, we show that the combinatorial solution approach reduces the computational effort. Finally, we compare the static overbooking approach derived from the capacity uncertainty model to existing approaches based on the expected marginal seat revenue (EMSR).