Analytical approach to estimating AMHS performance in 300mm fabs

This thesis proposes a computationally effective analytical approach to automated material handling system (AMHS) performance modeling for a simple closed loop AMHS, such as is typical in supporting a 300mm wafer fab bay. In this system, due to the significant impact of vehicle blocking, a straightforward queueing network model which treats the material handling system as a central server can be very inaccurate. On the other hand, discrete-event simulation can produce accurate assessments of the production performance, including the contribution by the automated material handling systems (AMHS). However, the corresponding simulation models are both expensive and time-consuming to construct, and require long execution times to produce statistically valid estimates. These attributes render simulation ineffective as a decision support tool in the early phase of system design, where requirements and configurations are likely to change often. We propose an alternative model that estimates the MHS performance considering the possibility of vehicle-blocking. Such models are useful in the design of vehicle-based AMHS and correctly estimate the throughput capacity and move request delay of the AMHS.;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 all the vehicles on the closed-loop considering the possibility of vehicle-blocking. Steady-state analysis provides estimates of empty-vehicle flows, which are essential to accurately estimate other metrics such as the transport time and throughput capacity. The resulting large-scale model provided reasonably accurate estimates; however, it presented some computational challenges.;These computational challenges motivated the development of a second model that also analyzes the system as an extended Markov chain but with a much reduced state space because the model tracks the movement of a single vehicle in the system with additional assumptions on vehicle-blocking. This reduced-state model offers computationally fast, fairly accurate estimates of the AMHS throughput capacity.;Neither model is a conventional Markov Chain model because they combine the conventional Markov Chain analysis of the AMHS operations with additional constraints on AMHS stability and vehicle-blocking that are necessary to provide a unique solution to the steady-state behavior of the AMHS.;Based on the throughput capacity model, an analytical approach is developed to approximate the expected response time of the AMHS to move requests. The expected response times are important to measure the performance of the AMHS and for estimating the required queue capacity at each pick-up station. The derivation is not straightforward and especially complicated for multi-vehicle systems. The approximation relies on the assumption that the response time is a function of the distribution of the vehicles along the tracks and the expected length of the path from every possible location to the move request location.;The proposed analytical approach is novel because it models mutli-vehicle material handling systems considering practical issues that have not been previously addressed. Moreover, the semiconductor industry can benefit from such models because it proposes and demonstrates the capability of computationally fast and reusable analytic models that provide accurate and reliable estimates of AMHS performance necessary for the design stage.