Supply Chain Optimization

In this article, we describe the inventory optimization problem for buy-online-pickup-in-store and ship-from-store use cases and provide a case study that shows how stock levels can be optimized in real-life settings using machine learning methods.

This article is a hands-on tutorial that describes how to develop, debug, and evaluate RL optimizers using PyTorch and RLlib. This approach can be applied to a number of supply chain problems such as stock level optimization, transportation cost minimization, and warehouse space optimization.

In this article, we explore how the supply chain optimization problem can be approached from the RL perspective that generally allows for replacing a handcrafted optimization model with a generic learning algorithm paired with a stochastic supply network simulator. We start by building a simple simulation environment that includes suppliers, factories, warehouses, and retailers, as depicted in the animation below; we then develop a deep RL model that learns how to optimize inventory and pricing decisions.