Abstract

Due to the recent advancements in the manufacturing system, the semiconductor FABs have become larger, and thus, more overhead hoist transports (OHTs) need to be operated. In this paper, we propose a cooperative zone-based rebalancing algorithm (CZR) to allocate idle overhead hoist vehicles (OHTs) in a semiconductor FAB. The proposed model is composed of two parts: (1) a state representation learning part that extracts the localized embedding of each agent using graph neural network, and (2) 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.

摘要

由于制造系统的最新发展，半导体FAB变得越来越大，因此，需要操作更多的架空式提升运输机（OHT）。在本文中，我们提出了一种基于区域协作的再平衡算法（CZR），用于在半导体FAB中分配闲置的高架起重车辆（OHT）。所提出的模型由两部分组成：（1）状态表示学习部分，使用图神经网络提取每个代理的本地化嵌入；（2）策略学习部分，使用构造的嵌入进行重新平衡操作。通过在单个框架中进行表示学习和策略学习，所提出的方法可以训练分散的策略，以使代理协作地重新平衡OHT。实验表明，该方法可以显着减少平均检索时间，同时降低OHT利用率。此外，我们通过在看不见的动态情况下测试策略而无需进一步培训，就研究了建议算法的可传递性。