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. Discrete-event simulation can produce accurate assessments of the production performance, including the contribution by the 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 AMHS performance considering the possibility of vehicle-blocking.
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. The resulting large-scale model provided reasonably accurate performance 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.
Neither model is a conventional Markov Chain 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 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.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/11557 |
| Date | 07 July 2006 |
| Creators | Nazzal, Dima |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Language | en_US |
| Detected Language | English |
| Type | Dissertation |
| Format | 791626 bytes, application/pdf |
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