An Effective Hybrid Rescheduling Method for Wafer Chip Precision Packaging Workshops in Complex Manufacturing...

Area of Science:

• Semiconductor Manufacturing
• Operations Research
• Artificial Intelligence

Background:

• Increasing demand for small-batch, high-variety wafer chips challenges traditional manufacturing schedules.
• Complex semiconductor manufacturing environments are prone to dynamic events (e.g., machine breakdowns, order modifications) that disrupt production.
• Existing scheduling methods struggle to adapt to these dynamic events effectively.

Purpose of the Study:

• To develop a robust rescheduling strategy for wafer chip precision packaging workshops facing dynamic events.
• To address the complexities of multiple varieties, small batches, and unpredictable disruptions in semiconductor manufacturing.
• To enhance the efficiency and stability of production scheduling in response to real-time manufacturing changes.

Main Methods:

• Established a mathematical model for the wafer chip packaging workshop rescheduling problem.
• Utilized rolling horizon technology to manage multiple dynamic events.
• Developed a hybrid algorithm combining an improved firefly optimization framework and variable neighborhood search strategy.

Main Results:

• The proposed hybrid algorithm demonstrated superior performance compared to existing advanced algorithms.
• The method proved effective and stable in handling dynamic events like machine breakdowns, emergency orders, and modifications.
• Comparative experiments and engineering case studies validated the algorithm's practical applicability and effectiveness.

Conclusions:

• The developed rescheduling approach offers a significant improvement for wafer chip packaging production.
• The hybrid optimization technique provides a stable and efficient solution for dynamic manufacturing environments.
• This research contributes to optimizing semiconductor manufacturing operations amidst increasing complexity and variability.