论文标题
基于增强的基于模拟的多目标优化方法，具有可重构制造系统的知识发现
An enhanced simulation-based multi-objective optimization approach with knowledge discovery for reconfigurable manufacturing systems
论文作者
论文摘要
在当今的不确定且竞争激烈的市场中，企业受到越来越缩短的产品寿命和频繁变化，可重新配置的制造系统（RMS）应用程序在制造业的成功中起着重要作用。尽管RMS提供了优势，但在利益相关者和决策者面对这些复杂系统中固有的权衡决策时，获得高效学位是一项艰巨的任务。这项研究解决了工作任务和资源分配给工作站，以及RMS中的缓冲能力分配。目的是同时最大化吞吐量，并最大程度地减少在波动生产量和容量变化下的总缓冲能力，同时考虑系统的随机行为。提出了一种基于增强的模拟的多目标优化（SMO）方法，并提出了定制的仿真和优化组件来应对上述挑战。除了呈现受数量和容量变化的最佳解决方案外，提议的方法还支持具有发现知识的决策者，以进一步了解RMS设计。特别是，这项研究提出了一种特定于问题的自定义SMO，并结合了一种新型的柔性模式挖掘方法，用于优化RMS和进行最佳后分析。在此程度上，这项研究证明了将SMO和知识发现方法应用于RMS的快速决策和生产计划的好处。
In today's uncertain and competitive market, where enterprises are subjected to increasingly shortened product life-cycles and frequent volume changes, reconfigurable manufacturing systems (RMS) applications play a significant role in the manufacturing industry's success. Despite the advantages offered by RMS, achieving a high-efficiency degree constitutes a challenging task for stakeholders and decision-makers when they face the trade-off decisions inherent in these complex systems. This study addresses work tasks and resource allocations to workstations together with buffer capacity allocation in RMS. The aim is to simultaneously maximize throughput and minimize total buffer capacity under fluctuating production volumes and capacity changes while considering the stochastic behavior of the system. An enhanced simulation-based multi-objective optimization (SMO) approach with customized simulation and optimization components is proposed to address the abovementioned challenges. Apart from presenting the optimal solutions subject to volume and capacity changes, the proposed approach support decision-makers with discovered knowledge to further understand the RMS design. In particular, this study presents a problem-specific customized SMO combined with a novel flexible pattern mining method for optimizing RMS and conducting post-optimal analyzes. To this extent, this study demonstrates the benefits of applying SMO and knowledge discovery methods for fast decision-support and production planning of RMS.
