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

• A QHH algorithm is proposed for solving the SFTSP with makespan criterion. • An efficient encoding and decoding pair is presented to generate feasible schedules. • Eight simple heuristic rules are designed to construct a set of LLHs. • Q-learning algorithm is employed as a high-level strategy to intelligently select the LLHs. • The performance of the proposed QHH is evaluated on a set of benchmark scenarios. 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. [ABSTRACT FROM AUTHOR]