A Data-driven Single-Period Newsvendor Problem Based on XGBoost Algorithm
Solving single-period newsvendor problem usually assumes that the demand distribution follows a particular form,predicts demand from historical data,then solves optimization models.Although this assump-tion simplifies the analysis,it does not reflect the true distribution of demand over time and inevitably passes on historical estimated prediction errors to the optimization process.To solve this problem,a method that combines estimation and optimization is proposed.This method adopts the form of data-driven weights to deal with the demand uncertainty related to features and introduces XGBoost algorithm to solve single-period newsvendor problem with non-stationary demand.After observing the features that affect demand,the method directly determines the optimal inventory decision.This paper's contribution to the field of operations manage-ment mainly includes twofold:1)It integrates a scalable end-to-end tree boosting system called XGBoost into a single-period newsvendor problem.2)The integrated estimation optimization algorithm is applied to a real-world data set under different target service levels and training sample sizes,and is compared with several standard methods for studying newsvendor problems.The results show that this method can reduce inventory costs by at least about 5％.
newsvendormachine learningsample average approximationquantile regressionoptimization
万方数据
维普
