Dual-Sourcing via Dynamic Programming with Monte Carlo Value Approximation

In large scale global supply chains, the inventory cost sensitivity due to supplier disruption can be high. Dual-sourcing, a inventory policy which leverages two suppliers to minimize the cost of supplier disruption, is often applied to minimize the inventory cost in the face of uncertain lead times and consumer demand. However, computing optimal policies for dual-sourcing faces challenges in modern supply chains due to the large scale of the system. We introduce Dyanmic Programming with Monte Carlo Value Approximation (DPMC), an approximate dynamic programming algorithm with polynomial time complexity which applies Monte Carlo simulation to estimate the optimal value function to address the large scale dual-sourcing problem. We show that DPMC is theoretically guaranteed to converge to the optimal policy by improving the value function approximator and/or increasing the number of Monte Carlo iterations. Via empirical simulation, we demonstrate that DPMC is competitive and often exceeds the cost-minimizing performance of other state-of-the-art dual-sourcing policies, specifically in scenarios where suppliers subject to disruption and/or fixed ordering costs.