标题： 一种基于仿真且具有知识发现的可重构制造系统多目标优化增强方法 显示英文标题

标题： An enhanced simulation-based multi-objective optimization approach with knowledge discovery for reconfigurable manufacturing systems

摘要： 在当今瞬息万变且竞争激烈的市场中，企业面临着日益缩短的产品生命周期和频繁的产量变化，可重构制造系统（RMS）的应用在制造业的成功中扮演着重要角色。 尽管RMS提供了诸多优势，但当利益相关者和决策者面临这些复杂系统固有的权衡决策时，实现高效率仍然是一个具有挑战性的任务。 本研究解决了可重构制造系统中工作站的工作任务与资源分配以及缓冲区容量分配问题。 目标是在生产量和产能波动的情况下同时最大化吞吐量并最小化总缓冲区容量，同时考虑系统的随机行为。 提出了一种增强型基于仿真的多目标优化（SMO）方法，该方法结合了定制化的仿真和优化组件，以应对上述挑战。 除了呈现针对产量和产能变化的最优解外，所提出的方法还为决策者提供发现的知识，以便进一步理解RMS设计。 具体而言，本研究提出了针对RMS优化和后最优分析的特定问题定制的SMO，并结合了一种新颖的柔性模式挖掘方法。 通过这种方式，本研究展示了应用SMO和知识发现方法为RMS快速决策支持和生产计划带来的好处。 显示英文摘要

摘要： 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.