Data-driven two-stage inventory problem

In this thesis, we consider two-stage newsvendor problem where the decision maker selling a seasonal product only uses the historical demand information in her decisions. In our setting, there are two decisions to be made: the order quantity, and a marked-down price. We decide on how many products to order for the first stage, as well as how to set a marked-down price for remaining unsold inventory in the second stage. To solve the problem considered, data-driven models which do not require any distributional assumption are provided. Specifically, we propose six data-driven methods that solve the problem hierarchically in addition to another method which finds the order quantity and the marked-down price for the remaining inventory simultaneously by using a mixed integer linear program. We generate the data from selected demand distributions and divide it into a training data and a testing data. The generated data is a function of the way that decisions were made historically. We make a definition of the relevancy level based on what decisions the data depends on. We conduct a numerical study to evaluate: (a) the effect of data relevancy, (b) the effect of training data size, (c) the performance of proposed models. We investigate the performances of proposed models in three ways: (1) comparison the best model with the worst one, (2) comparison with respective expected values, (3) comparison with respective the inverse coefficient of variation. Lastly, we measure how many times one model is the best among testing samples and compare models based on their performances.