A genetics-based hybrid scheduler for generating static schedules in flexible manufacturing contexts
A genetics-based hybrid scheduler for generating static schedules in flexible manufacturing contexts
复制标题
基于遗传学的混合调度程序，用于在灵活的制造环境中生成静态调度
DOI：
10.1109/21.247881
复制
发表时间：
1993
期刊：
影响因子：
--
通讯作者：
J. Zaveri
中科院分区：
文献类型：
--
作者：
C. Holsapple;V. Jacob;Ramakrishnan Pakath;J. Zaveri
现有的支持柔性制造系统（FMS）调度决策的计算机化系统在很大程度上依赖于通过机械学习获得的知识来生成调度。在少数情况下，系统还具有使用演绎或监督归纳法学习的能力。我们介绍了一种新的基于人工智能的系统，用于生成静态时间表，该系统大量使用无监督学习模块来获取必要的问题处理知识的重要部分。该调度程序采用混合调度生成策略，该策略将通过基于遗传的无监督归纳获得的知识与死记死背的知识有效地结合在一起，以有效的方式生成高质量的调度。通过对一个实际复杂的随机生成问题的一系列实验，我们证明了混合调度策略是可行的，有前途的，值得更深入的研究。>
Existing computerized systems that support scheduling decisions for flexible manufacturing systems (FMS's) rely largely on knowledge acquired through rote learning for schedule generation. In a few instances, the systems also possess some ability to learn using deduction or supervised induction. We introduce a novel AI-based system for generating static schedules that makes heavy use of an unsupervised learning module in acquiring significant portions of the requisite problem processing knowledge. This scheduler pursues a hybrid schedule generation strategy wherein it effectively combines knowledge acquired via genetics-based unsupervised induction with rote-learned knowledge in generating high-quality schedules in an efficient manner. Through a series of experiments conducted on a randomly generated problem of practical complexity, we show that the hybrid scheduler strategy is viable, promising, and, worthy of more in-depth investigations. >