Data-driven Scheduling for High-mix and Low-volume Production in Semiconductor Assembly and Testing
Journal
IEEE International Conference on Automation Science and Engineering
Journal Volume
2021-August
Pages
1303-1308
Date Issued
2021
Author(s)
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. ? 2021 IEEE.
Subjects
Assembly
Design of experiments
Forecasting
Manufacture
Scheduling
Statistical tests
Stochastic models
Data driven
High-mix/low volumes
Low-volume production
Prediction methods
Processing time
Production characteristics
Production environments
Scheduling decisions
Semiconductor assembly
Unrelated parallel machines
Optimization
Type
conference paper