Inventory management in a healthcare system needs to be compatible with its operations
and critical characteristics ensuring minimization of inventory-related cost as well as …

We consider an inventory replenishment problem with unknown and non-stationary
demand. We design a structured reinforcement learning algorithm that efficiently adapts the …

We study dual sourcing under stochastic and non-stationary demand. The non-stationarity is
modeled through Markov-modulated changes in the underlying demand distribution. The …

We study optimal trade execution strategies in financial markets with discrete order flow. The
agent has a finite liquidation horizon and must minimize price impact given a random …

We consider the production control problem of a production-inventory system with correlated
demand inter-arrival and processing times that are modeled as Markovian Arrival …

Inventory is one of the most important assets in the companies, especially in manufacturing
company. In the event of problems related to inventory, the company's business processes …

We study Bayesian quickest detection problems with sensor arrays. An underlying signal is
assumed to gradually propagate through a network of several sensors, triggering a cascade …

We consider a discrete‐time infinite‐horizon inventory system with non‐stationary demand,
full backlogging, and deterministic replenishment lead time. Demand arrives according to a …

We propose a hidden Markov model (HMM) approach to identifying on-shelf out-of-stock
(OOS) by detecting changes in sales patterns resulting from unobserved states of the shelf …

As increasingly more shop-floor data becomes available, the performance of a production
system can be improved by developing effective data-driven control methods that utilize this …