Semiconductor final testing scheduling using Q-learning based hyper-heuristic

Semiconductor final testing scheduling problem (SFTSP) has extensively been studied in advanced manufacturing and intelligent scheduling fields. This paper presents a Q-learning based hyper-heuristic (QHH) algorithm to address the SFTSP with makespan criterion. The structure of QHH employs the Q-learning algorithm as the high-level strategy to autonomously select a heuristic from a pre-designed low-level heuristic set. The selected heuristic in different stages of the optimization process is recognized as the executable action and performed on the solution space for better results. An efficient encoding and decoding pair is presented to generate feasible schedules, and a left-shift scheme is embedded into the decoding process for improving resources utilization. Additionally, the design-of-experiment method is implemented to investigate the effect of parameters setting. Both computational simulation and comparison are finally carried out on a benchmark set and the results demonstrate the effectiveness and efficiency of the proposed QHH.

Automated summary

The paper introduces a Q-learning based hyper-heuristic algorithm to address the semiconductor final testing scheduling problem, which autonomously selects low-level heuristics to optimize the solution space and improve resource utilization, demonstrating the effectiveness and efficiency of the algorithm through computational simulation and comparison on a benchmark set.