Meta-Analysis Unconstraining Method for Two-Class Overbooking Model
Keywords:
Demand forecasting, static model, airline passenger, airline industry, stochastic model, revenue managementAbstract
Accurate demand forecasting is crucial for airline revenue management. However, it is difficult to forecast demand accurately since the historical data does not reflect the actual current demand. In order to obtain a better estimate of current demand, there are a number of unconstraining methods available. In this study, we used the meta-analysis (MA) technique applied to unconstraining data to improve the performance of the two-class overbooking model. The accuracy and expected profit are computed and compared to other methods often used, for example, the expectation-maximization (EM) method and the naïve methods (N1, N2, and N3). Our numerical study found that the MA produces a better MAPE in most situations with high accuracy for demand forecasting and the highest expected profit in all situations, which is greater than other methods, approximately 11.71% to 17.76%.
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