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...

This paper constructs a model of a supply chain to examine how demand volatility is passed upstream through the chain. In particular, we seek to determine how likely it is that the chain experiences a bullwhip effect, where the variance of the upstream firm's production exceeds the variance of...

This paper constructs a model of a supply chain to examine how demand volatility is passed upstream through the chain. In particular, we seek to determine how likely it is that the chain experiences a bullwhip effect, where the variance of the upstream firms’ production exceeds the variance of...

The study sought to determine the role of vendor-managed inventory and form postponement in mitigating against the bullwhip effect in the bakery industry. The bullwhip effect is referred to as demand distortions along the upstream supply chain that is caused by variances between supply and...

This paper introduces recent developments in the analysis of inventory systems with partial observations. The states of these systems are typically conditional distributions, which evolve in infinite dimensional spaces over time. Our analysis involves introducing unnormalized probabilities to...

In this paper, we consider a stochastic inventory system of two products where a fi xed proportion of the unsatisfied ed demand for one product purchases the other product as a substitute. We formulate the optimal inventory control problem as a dynamic program; and by developing two key...

In this paper we derive useful sufficiency optimality conditions for a class of optimal control problems subject to differential inclusions and involving non-differentiable functions. A production inventory problem is solved to illustrate our results