基于自适应NSGA-II的可重构制造系统多目标优化与形式化规范

Manel Houimli, M. Khalgui, L. Kahloul
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引用次数: 2

摘要

可重构制造系统(RMSs)提供了在运行时构建具有可变结构的动态系统的新能力。RMSs是机器意外故障以及市场需求动态变化的合适解决方案。RMSs的一个关键和重要特性是它们能够在生产系统生命周期的任何时间更新机器和选择最优配置。配置的选择必须保证两个目标:优化资源和保存每次重新配置后的最佳属性。在本文中,我们提出了一种混合方法,该方法将遗传算法(GAs)和高级Petri网(HLPNs)以独特的形式结合起来,以解决前面两个目标。本文给出了该方法的形式化,并通过RMS实例分析验证了该方法的可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-objective optimization and formal specification of reconfigurable manufacturing system using adaptive NSGA-II
Reconfigurable Manufacturing Systems (RMSs) provide new abilities to build dynamic systems with changeable structure at runtime. RMSs are the suitable solution for unexpected breakdown of machines as well as the dynamic change of markets requirements. One critical and important feature in the RMSs is their ability to update machines and to select the most optimal configuration at any time of the production system life. The choice of the configuration must guarantee both objectives: optimization of resources and preservation of the best properties after every reconfiguration. In this paper, we propose a hybrid approach which combines Genetic Algorithms (GAs) and High Level Petri Nets (HLPNs) in a unique formalism to tackle with the both previous objectives. The paper presents the formalization of the approach and it demonstrates its feasibility on an RMS case study.
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