Institute for Systems Research

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Approximate Policy Iteration for Semiconductor Fab-Level Decision Making - a Case Study
(2000) He, Ying; Bhatnagar, Shalabh; Fu, Michael C.; Marcus, Steven I.; Marcus, Steven I.; ISR
In this paper, we propose an approximate policy iteration (API) algorithm for asemiconductor fab-level decision making problem. This problem is formulated as adiscounted cost Markov Decision Process (MDP), and we have applied exact policy iterationto solve a simple example in prior work. However, the overwhelmingcomputational requirements of exact policy iteration prevent its application forlarger problems. Approximate policy iteration overcomes this obstacle by approximating thecost-to-go using function approximation. Numerical simulation on the same example showsthat the proposed API algorithm leads to a policy with cost close to that of the optimalpolicy.
Simulation-Based Approach for Semiconductor Fab-Level Decision Making - Implementation Issues
(2000) He, Ying; Fu, Michael C.; Marcus, Steven I.; Marcus, Steven I.; ISR
In this paper, we discuss implementation issues of applying a simulation-based approach to asemiconductor fab-level decision making problem. The fab-level decision making problem isformulated as a Markov Decision Process (MDP). We intend to use a simulation-based approach sinceit can break the "curse of dimensionality" and the "curse of modeling" for an MDP with largestate and control spaces. We focus on how to parameterize the state space and the control space.