Abstract
The goal of this work is to develop novel methods to solve the semiconductor fab scheduling problem. The problem can be modeled as a flexible job-shop with large instances and specific constraints related to special machine and job characteristics. To investigate the problem, we develop a tool to simulate small to large-scale instances of the problem. Using the simulator, we aim to develop new dispatching strategies using genetic programming and reinforcement learning.
Keywords
- Machine Learning (ML): General
- Planning, Routing, and Scheduling (PRS): General
