WANG Luyao, ZHANG Xinyu, KUANG Xiong, ZHOU Jianhong
The optimization of revenue management is one of the effective ways to improve retailers' economic returns. Pricing is the engine and core technology of revenue management, which plays an important role in improving retailers' revenue. Considering the complexity of forecasting and optimization problems in the practical application of revenue management, the steps of forecasting product demand first and then optimizing revenue are usually adopted. When forecasting product demand, there are usually multiple candidate models, that is, facing the uncertainty of the model. At this time, the final model is generally determined by model selection. However, traditional model selection criteria, including Akaike information criterion(AIC) and Bayesian information criterion(BIC), usually only consider the impact of model selection on prediction accuracy, without considering how the prediction model will affect the next optimization decision objectives. This paper first proposes the focused information criterion(FIC) model selection criterion in the optimization of commodity income management, uses the FIC model selection criterion to select the product demand forecasting model, considers the structure of the optimization model, and selects the prediction model with the goal of minimizing the decision-making error rather than prediction error. The numerical simulation results show that, in most cases, compared with AIC and BIC model selection criteria, FIC model selection criteria considering decision objectives performs best. Meanwhile, the empirical research results also verify the superiority of the FIC model selection criteria considering decision objectives.