TY - JOUR
T1 - Scheduling Semiconductor Testing Facility by Using Cuckoo Search Algorithm with Reinforcement Learning and Surrogate Modeling
AU - Cao, Zhengcai
AU - Lin, Chengran
AU - Zhou, Mengchu
AU - Huang, Ran
N1 - Funding Information:
Manuscript received June 19, 2018; accepted July 22, 2018. Date of publication September 13, 2018; date of current version April 5, 2019. This paper was recommended for publication by Associate Editor Y. Lu and Editor J. Li upon ievaluation of the reviewers’ comments. This work was supported in part by the National Natural Science Foundation of China under Grant 51375038 and Grant 61403018, in part by the Beijing Municipal Natural Science Foundation under Grant 4162046, and in part by the Open Project Program of the State Key Laboratory of Synthetical Automation for Process Industries under Grant PAL-N201804. This paper was presented at the 2017 IEEE Conference on Automation Science and Engineering, Xi’an, China. (Corresponding authors: MengChu Zhou; Ran Huang.) Z. Cao, C. Lin, and R. Huang are with the College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China (e-mail: giftczc@163.com; 2015200731@grad.buct.edu.cn; huangran@ mail.buct.edu.cn).
Publisher Copyright:
© 2004-2012 IEEE.
PY - 2019/4
Y1 - 2019/4
N2 - 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.
AB - 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.
KW - Cuckoo search (CS) algorithm
KW - reinforcement learning (RL)
KW - scheduling
KW - semiconductor
KW - surrogate modeling
UR - http://www.scopus.com/inward/record.url?scp=85053348988&partnerID=8YFLogxK
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U2 - 10.1109/TASE.2018.2862380
DO - 10.1109/TASE.2018.2862380
M3 - Article
AN - SCOPUS:85053348988
SN - 1545-5955
VL - 16
SP - 825
EP - 837
JO - IEEE Transactions on Automation Science and Engineering
JF - IEEE Transactions on Automation Science and Engineering
IS - 2
M1 - 8462749
ER -