Q(λ) learning-based dynamic route guidance algorithm for overhead hoist transport systems in semiconductor fabs

A learning-based dynamic routing algorithm is proposed for the overhead hoist transport (OHT) systems of semiconductor fabrication facilities (fabs). An OHT system, which consists of multiple vehicles moving at high speeds on guided rails, is the primary automated material-handling system (AMHS) in a fab. Modern large-scale fabs have hundreds of vehicles moving lots between multiple processing machines. The dynamic routing method is a route guidance method that dynamically selects the best vehicle paths under given traffic conditions and congestion levels. Building on the $Q(\lambda ) $Q(λ) learning method, we develop a reinforcement learning-based dynamic routing algorithm called QLBWR(λ), which consists of a Boltzmann softmax policy and a reward function. The proposed algorithm uses real-time information to effectively guide each vehicle so that it avoids congestion and finds an efficient path. The algorithm is also designed with a low computational burden, such that the efficient route can be found for hundreds of vehicles in real time. Simulation analyses on an actual fab layout are used to compare the performance of the proposed algorithm with common static and dynamic algorithms. The results show that the proposed algorithm outperforms the benchmarking algorithms.