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

Zhengcai Cao, Chengran Lin, Mengchu Zhou, Ran Huang

Research output: Contribution to journalArticlepeer-review

113 Scopus citations


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. Note to Practitioners - Scheduling of semiconductor final testing is usually solved via intelligent optimization algorithms. Nevertheless, most of them are parameter-sensitive, and thus, selecting their proper parameters is a huge challenge. In order to deal with the parameter selection issue, we propose a reinforcement learning (RL) algorithm to self-adjust their parameters. To reduce the computational burden, we propose to use surrogate modeling of the reward function in RL and determine which nests should be reserved in cuckoo search. As a result, our algorithm possesses higher robustness and can obtain a high-quality schedule than the existing algorithms for semiconductor final testing facility. In addition, it has a lower computational complexity via the proposed surrogate model, and thus, a feasible solution can be obtained in a short time for real-time scheduling. Experimental results show that the proposed method well outperforms some existing algorithms. Hence, it can be readily applied to industrial semiconductor final testing facility scheduling problems.

Original languageEnglish (US)
Article number8462749
Pages (from-to)825-837
Number of pages13
JournalIEEE Transactions on Automation Science and Engineering
Issue number2
StatePublished - Apr 2019

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering


  • Cuckoo search (CS) algorithm
  • reinforcement learning (RL)
  • scheduling
  • semiconductor
  • surrogate modeling


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