Monte Carlo Tree Search in Designing Artificial Intelligence-Enhanced Service Level Agreements

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 is unable to effectively solve complex supply chain problems. This study proposes a number of augmenting mechanisms for MCTS to be utilized in managing service level agreements. The results demonstrate that even in the case of non-stationary demand, where the majority of optimization methods reach their limitations, engaging the suggested augmentation mechanisms significantly improves the performance of MCTS