Semiconductor manufacturing is one of the most complex existing manufacturing systems. It requires constant improvement to meet demands and expectations. This dissertation studies semiconductor manufacturing under three main topics, preventive maintenance scheduling, lot size management and AMHS scheduling. We first provide an optimization based decomposition algorithm and a heuristic algorithm to solve preventive maintenance scheduling problem along with direct optimization. Then, we develop an analytic tool to investigate and find optimal lot sizes to run in a manufacturing environment to minimize cycle time. Finally, we propose an optimization based AMHS scheduling algorithm and compare its performance to a myopic algorithm. / text

This MS thesis studies the effect of uncertainty in the demand of finished products, supply of raw materials, and maintenance resources availability on the maintenance schedule of a manufacturing facility. A simulation model is formulated in order to realistically model manufacturing systems of various complexities, consisting of multiple interacting machines that degrade and fail over time, and are repaired using imperfectly available maintenance crews and resources. A design of experiments (DOE) based sensitivity study is conducted to find the system parameters that mostly affected the maintenance decisions and corresponding profits. / text

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