Cooperative zone-based rebalancing of idle overhead hoist transportations using multi-agent reinforcement learning with graph representation learning
Kyuree Ahn, Jinkyoo Park
Abstract
Due to the recent advances in manufacturing systems, the semiconductor FABs have become larger, and thus, more overhead hoist transporters (OHTs) need to be operated. In this article, we propose a cooperative zone-based rebalancing algorithm to allocate idle overhead hoist vehicles in a semiconductor FAB. The proposed model is composed of two parts: (i) a state representation learning part that extracts the localized embedding of each agent using a graph neural network; and (ii) a policy learning part that makes a rebalancing action using the constructed embedding. By conducting both representation learning and policy learning in a single framework, the proposed method can train the decentralized policy for agents to rebalance OHTs cooperatively. The experiments show that the proposed method can significantly reduce the average retrieval time while reducing the OHT utilization ratio. In addition, we investigated the transferable capability of the suggested algorithm by testing the policy on unseen dynamic scenarios without further training.