Advances in computational power and AI have increased interest in reinforcement learning
approaches to inventory management. This paper provides a theoretical foundation for …

Reinforcement learning (RL) has proven to be well-performed and general-purpose in the
inventory control (IC). However, further improvement of RL algorithms in the IC domain is …

We consider data-driven inventory and pricing decisions in the feature-based newsvendor
problem, where demand is influenced by both price and contextual features and is modeled …

In many data-driven decision-making problems, performance guarantees often depend
heavily on the correctness of model assumptions, which may frequently fail in practice. We …

We study the data-integrated newsvendor problem in which the random demand depends
on a set of covariates. Observing from the solutions analyzed in the existing studies, we …

Data is now unanimously considered a key firm asset for enabling better operational
decisions. However, data-driven decisions can inadvertently expose private data, leaving …

We investigate the potential of interpretable deep learning methods to enhance the accuracy
of delivery time prediction in home appliance retail sector. Our research, motivated by the …