Recently there has been a surge of interest in operations research (OR) and the machine
learning (ML) community in combining prediction algorithms and optimization techniques to …

We are concerned with the interaction and integration between demand forecasting and
inventory control, in the context of supply chain operations. The majority of the literature is …

We investigate a data-driven multiperiod inventory replenishment problem with uncertain
demand and vendor lead time (VLT) with accessibility to a large quantity of historical data …

Problem definition: Is deep reinforcement learning (DRL) effective at solving inventory
problems? Academic/practical relevance: Given that DRL has successfully been applied in …

Problem definition: The beer game is widely used in supply chain management classes to
demonstrate the bullwhip effect and the importance of supply chain coordination. The game …

Retailers that offer perishable items are required to make ordering decisions for hundreds of
products on a daily basis. This task is non-trivial because the risk of ordering too much or too …

Recently there has been a surge of interest in operations research (OR) and the machine
learning (ML) community in combining prediction algorithms and optimization techniques to …

This work provides a Deep Reinforcement Learning approach to solving a periodic review
inventory control system with stochastic vendor lead times, lost sales, correlated demand …

Inventory control aims to meet customer demands at a given service level while minimizing
cost. As a result of market volatility, customer demand is generally changing, and ignoring …

In this book... Nicolas Vandeput hacks his way through the maze of quantitative supply chain
optimizations. This book illustrates how the quantitative optimization of 21st century supply …