Iterative simulation-based optimization for parallel batch scheduling problems

The optimization of manufacturing flows in electronics and semiconductor industry becomes more and more important. This results from the high complexity of the underlying production processes. In this research a selected part of the process with high practical impact is investigated. It is a scheduling problem arising in the semiconductor frontend oxidation and diffusion area. The optimization objective is the total weighted tardiness (TWT), but also the changes in cycle time are observed. Because of comparably long processing times and parallel batch processing a high optimization potential exists at the investigated machine groups. The methods dispatching, simulation-based optimization, mixed integer programming (MIP) and variable neighbourhood search (VNS) are compared. In contrast to the traditional simulation-based optimization, where a control variable is selected before a simulation run starts and is kept during the simulation, the new iterative simulation-based optimization approach is capable to generate and optimize small local sub-problems. Afterwards the results are used in a global model. The performance of the new method is comparable to MIP and VNS approaches and is much better than dispatching.