Below are some descriptions of MS faculty research projects. On the other hand, the generality of the MDP framework makes it attractive for tackling sequential decision-making problems under uncertainty, so finding good solution methods for MDPs is a very important research area. Recently, simulation-based methods have emerged as one alternative for tackling these types of problems. We propose two new simulation-based approaches that offer potential benefits not found in previously proposed methods.
Sequential optimization is accelerated by simultaneously considering effects on the parameters.
This approach has proven effective in overcoming the difficulty of applying optimization via simulation for stochastic problems with a large number of parameters, but this would be its first application to MDPs. In the first thrust, we have developed a finite-horizon transient MDP model that integrates product life cycle dynamics in order to provide decision support on such critical operational issues as when to add additional capacity by purchasing tools and how to allocate existing capacity, e.g., in order to determine when to convert from one type of production to another. The goal of the second thrust is to integrate modeling at the process level with operations at the fab level. Unlike other types of manufacturing systems such as job shops, which have more variable processing times, the main sources of operational uncertainty in semiconductor fabs are the downtimes of the machines, and so preventive maintenance is crucial for efficient operations. What has been largely ignored until recently in these analyses are policies that take into account information from the state of the system, in terms of, for example, production planning data or process control data.