This research explores analytical models useful in the design of vehicle-based Automated Material Handling Systems (AMHS) to support semiconductor manufacturing. The objective is to correctly estimate the throughput and move request delay. This analysis proposes a computationally effective analytical approach to multi-vehicle AMHS performance modeling for a simple closed loop. A probabilistic model is developed, based on a detailed description of AMHS operations, and the system is analyzed as an extended Markov chain. The model tracks the operations of one vehicle on the closed-loop considering the possibility of vehicle-blocking. This analysis provides the essential parameters such as the blocking probabilities in order to estimate the performance measures. A numerical example is analyzed and simulated using Automod to demonstrate and validate the queuing model.
REF proposes an analytical model using an extended Markov chain useful in the design of AMHS.
An analytical model of vehicle-based automated material handling systems in semiconductor fabs
WSC '06
Computer Science