IEEE Trans Cybern. 2017 Sep;47(9):2951-2965. doi: 10.1109/TCYB.2016.2562674. Epub 2016 May 19.
Automated design of dispatching rules for production systems has been an interesting research topic over the last several years. Machine learning, especially genetic programming (GP), has been a powerful approach to dealing with this design problem. However, intensive computational requirements, accuracy and interpretability are still its limitations. This paper aims at developing a new surrogate assisted GP to help improving the quality of the evolved rules without significant computational costs. The experiments have verified the effectiveness and efficiency of the proposed algorithms as compared to those in the literature. Furthermore, new simplification and visualisation approaches have also been developed to improve the interpretability of the evolved rules. These approaches have shown great potentials and proved to be a critical part of the automated design system.
自动化生产系统调度规则设计是近年来一个颇有趣味的研究课题。机器学习，尤其是遗传编程（GP），是解决这一设计问题的有力手段。然而，其计算要求高、准确性和可解释性仍受到限制。本文旨在开发一种新的代理辅助 GP 方法，帮助在不显著增加计算成本的情况下提高进化规则的质量。实验验证了所提出的算法与文献中算法相比的有效性和效率。此外，还开发了新的简化和可视化方法来提高进化规则的可解释性。这些方法显示出了巨大的潜力，并被证明是自动化设计系统的关键部分。
