Air Cargo Network Revenue Management
研究了航空货运网络收益管理的启发式方法，基于动态规划提出线性规划、近似动态规划和分解方法，数值实验表明分解法在单航段和网络问题中均能获得更高预期净收益。
In this paper we discuss heuristics for network air cargo revenue management. We start from a dynamic programming formulation of air cargo network revenue management. Since the curse of dimensionality makes this problem intractable, we suggest several methods, based on linear programming, approximate dynamic programming, and decomposition to obtain both upper bounds and heuristics. We prove relationships between the upper bounds. Furthermore, we analyze the performance of both the bounds as well as the heuristics in a numerical study. In this numerical study, we find that a dynamic programming decomposition yields the tightest bounds. The heuristic based on the decomposition dominates other approaches by giving higher expected net revenues both when applied on the single-leg and on the network cargo problem.