We study an uncertain inventory routing problem with a finite horizon. The supplier acts as a central planner who determines replenishment quantities and also the times and routes for delivery to all retailers. We allow ambiguity in the probability distribution of each retailer's uncertain...

In this paper, we consider a classic periodic-review lost-sales inventory system with lead times, which is notoriously challenging to optimize with a wide range of real-world applications. We consider a joint learning and optimization problem in which the decision-maker does not know the demand...

Starting with historical demand trajectories of similar products and their covariate information, we develop a data-driven approach to dynamic inventory planning for new products. Specifically, we employ non-parametric machine learning methods to model the uncertain future demand and incorporate...

Problem Definition: We design an automatic decision-making system for an e-commerce platform to help manage the inventory of millions of stock keep units sold on the platform. The platform has accumulated a significant amount of contextual information, which may be useful for predicting both the...

In this paper, we study the feature-based newsvendor problem in the presence of historical demand and related demand covariates. We adopt general kernel methods to estimate the true demand distributions conditioned on the given demand features. To account for estimation errors, we propose a...

In 2020, inventory shrinkage eroded $61.7 billion profit in the U.S. retail industry. Unfortunately, fighting inventory shrinkage to protect retailers’ already slim profits is challenging due to unknown shrinkage rates and invisible inventory levels. While the latter has been studied in the...