Data-driven Scheduling for High-mix and Low-volume Production in Semiconductor Assembly and Testing

Abstract

The objective of this research is to improve scheduling decisions in high-mix low-volume (HMLV) production environments. Unique characteristics of HMLV semiconductor assembly and testing operations include: (1) Diversified Product Lines: To respond to global competition and different customer needs, manufacturers are providing diversified products to different consumers; (2) Unrelated Parallel Machines: Different machines are oftentimes procured at different capacity expansion stages. While different machines may have similar functions, the latest model oftentimes provides higher production efficiency and better quality. This leads to a production environment with parallel machines of different production characteristics; (3) Incomplete Product-machine Specific Production Data: When there are a wide variety of products produced by unrelated parallel machines, there will be a large number of possible product-machine combinations. Some of the combinations will not have enough data for accurate estimation of production characteristics, such as the required processing time information for scheduling decisions. In order to facilitate efficient and effective scheduling decisions in such HMLV production, a hierarchical prediction method is developed for mixed dataset analysis. The hierarchical prediction method generates missing parameters, such as the required processing time, for subsequent optimization of scheduling decisions. To cope with the inevitable errors in all forecast models, robust scheduling decisions are generated through a stochastic optimization framework. The framework is validated by an industry dataset collected from a leading semiconductor assembly and testing company.