An Improved Gray Wolf Optimizer for Wafer Probing Scheduling Problem

This paper studied the problem of wafer probing scheduling in wafer fabrication plants. As the final step in the front-end process of semiconductor manufacturing, wafer probing plays a critical role in wafer production. The wafer probing scheduling problem is modeled as a flexible job shop scheduling problem, considering resource constraints and batch processing. Firstly, a mixed-integer programming model is constructed to minimize the makespan. Then, an improved gray wolf optimizer is proposed to address the wafer probing scheduling problem. This improved gray wolf optimizer incorporates three improvement strategies within the framework of the original gray wolf algorithm: (1) an opposition-based learning approach is utilized to enhance the quality of the initial population; (2) leader agents mutation strategy is developed for local search; (3) a parameter adaptive adjustment is introduced to balance local and global search. In addition, an active decoding framework based on prior knowledge is designed to accelerate the convergence of the improved gray wolf optimizer. Finally, the effectiveness of the proposed improved gray wolf optimizer is verified on 15 instances of varying scales, and the results demonstrate that the proposed improved gray wolf optimizer outperforms other state-of-the-art algorithms.