Abstract
A large number of studies have been conducted in the area of semiconductor final test scheduling (SFTS) problems. As a specific example of the simultaneous multiple resources scheduling problem, intelligent manufacturing planning and scheduling based on meta-heuristic methods, such as the genetic algorithm (GA), simulated annealing, and particle swarm optimization, have become common tools for finding satisfactory solutions within reasonable computational times in real settings. However, only a few studies have analyzed the effects of interdependent relations during group decision-making activities. Moreover, for complex and large problems, local constraints and objectives from each managerial entity and their contributions toward global objectives cannot be effectively represented in a single model. This paper proposes a novel cooperative estimation of distribution algorithm (CEDA) to overcome these challenges. The CEDA extends a co-evolutionary framework incorporating a divide-and-conquer strategy. Numerous experiments have been conducted, and the results confirmed that CEDA outperforms hybrid GAs for several SFTS problems.
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Acknowledgments
This work was partly supported by the Grant-in-Aid for Scientific Research of Japan Society of Promotion of Science (C) (Grant No. 245102190001), the National Science Council, Taiwan (NSC101-2811-E-007-004; NSC100-2410-H-031-011-MY2; NSC100-2628-E-007-017-MY3), and the Advanced Manufacturing and Service Management Research Center of National Tsing Hua University (101N2073E1).
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Hao, XC., Wu, JZ., Chien, CF. et al. The cooperative estimation of distribution algorithm: a novel approach for semiconductor final test scheduling problems. J Intell Manuf 25, 867–879 (2014). https://doi.org/10.1007/s10845-013-0746-x
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DOI: https://doi.org/10.1007/s10845-013-0746-x