We apply Multi-Agent Deep Reinforcement Learning (MADRL) to inventory management problems with multiple echelons and evaluate MADRL's performance to minimize the overall costs of a supply chain. We also examine whether the upfront-only information-sharing mechanism used in MADRL helps alleviate...

We study a continuous-review, two-echelon inventory system with one central warehouse, multiple local facilities, and each facility facing random demand. Local facilities replenish their stock from the central warehouse (or distribution center), which in turn places orders at an outside supplier...

We consider a monopolistic firm selling two substitutable products to a stream of sequential arrivals whose purchase decisions can be influenced by earlier purchases. Before demand realizes, the firm faces a newsvendor problem for the two products with economies of scale in production for each....

We consider the classic continuous-review N-stage serial inventory system with a homogeneous Poisson demand arrival process at the most downstream stage (Stage 1). Any shipment to each stage, regardless of its size, incurs a positive fixed setup cost and takes a positive constant leadtime. The...

Motivated by the supermarket practice of marking down perishable products daily, we study a newsvendor who sells a perishable asset over repeated periods to consumers with a given consumption valuation for the product. The market size in each period is random, following a stationary...