Augmenting Monte Carlo Tree Search for managing service level agreements

Abstract

Monte Carlo Tree Search (MCTS) is an algorithmic technique utilized in reinforcement learning, a subfield of artificial intelligence, that combines tree-based search and random sampling for decision-making in uncertain environments. Although MCTS has been successfully used for playing complex games such as Chess and Go, without customizing the original algorithm using domain knowledge, it struggles to effectively solve complex supply chain problems. This study proposes several augmenting mechanisms for MCTS, tailored for managing service level agreements. Furthermore, we enhance the proposed solution for products/services where adjusting the base-stock level is feasible. The results demonstrate that even with non-stationary demand, where most optimization methods reach their limits, employing these augmentation mechanisms significantly improves MCTS performance.