Scheduling Semiconductor Testing Facility by Using Cuckoo Search Algorithm With Reinforcement Learning and Surrogate Modeling

A semiconductor final testing scheduling problem with multiresource constraints is considered in this paper, which is proved to be NP-hard. To minimize the makespan for this scheduling problem, a cuckoo search algorithm with reinforcement learning (RL) and surrogate modeling is presented. A parameter control scheme is proposed to ensure the desired diversification and intensification of population on the basis of RL, which uses the proportion of beneficial mutation as feedback information according to Rechenberg's 1/5 criterion. To reduce computational complexity, a surrogate model is employed to evaluate the relative ranking of solutions. A heuristic approach based on the relative ranking of encoding value and a modular function is proposed to convert continuous solutions obtained from Lévy flight into discrete ones. The computational complexity and convergence analysis results are presented. The proposed algorithm is validated with benchmark and randomly generated cases. Various simulation experiments and comparison between the proposed algorithm and several popular methods are performed to validate its effectiveness.