Most existing literature on supply chain and inventory management consider stochastic demand processes with zero or constant lead times. While it is true that in certain niche scenarios, uncertainty in lead times can be ignored, most real-world …
Determining optimum inventory replenishment decisions is critical for retail businesses with uncertain demand. The problem becomes particularly challenging when multiple products with different lead times and cross-product constraints are considered. …
This paper evaluates the applicability of reinforcement learning (RL) to multi-product inventory management in supply chains. The novelty of this problem with respect to supply chain literature is (i) we consider concurrent inventory management of a …
Reinforcement Learning (RL) has achieved a degree of success in control applications such as online gameplay and robotics, but has rarely been used to manage operations of business-critical systems such as supply chains. A key aspect of using RL in …
Applicability of reinforcement learning (RL) algorithms to a class of problems rarely addressed in machine learning literature, involving the control of a dynamic system with high-dimensional control inputs (actions).