From 1999 to 2016, research coalesced around a dynamic decision framework for revenue management, integrating pricing, inventory control, and overbooking under stochastic demand. The field moved toward real-time optimization and model-based forecasting, leveraging Markov decision processes and dynamic programming to govern capacity allocation and price levels while accommodating cancellations and no-shows. The shift from static, single-price policies to continuous-time, multi-price strategies created a unified, computationally tractable approach that links forecasting with pricing and inventory decisions.