首页> 外文会议>ASME international design engineering technical conferences and computers and information in engineering conference;Design for manufacturing and the life cycle conference;International conference on micro- and nanosystems >FACILITATING MULTIPLE-OBJECTIVE DECISION-MAKING FOR ADVANCED MANUFACTURING: A KNOWLEDGE REPRESENTATION AND COMPUTATIONAL ACTIVE LEARNING-BASED SIMULATION FRAMEWORK
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FACILITATING MULTIPLE-OBJECTIVE DECISION-MAKING FOR ADVANCED MANUFACTURING: A KNOWLEDGE REPRESENTATION AND COMPUTATIONAL ACTIVE LEARNING-BASED SIMULATION FRAMEWORK

机译:促进高级制造的多目标决策:知识表示和基于计算主动学习的模拟框架

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Due to the rapid development of information technology and the impetus for more efficient and adaptive manufacturing processes, the concept of advanced manufacturing has become an increasingly prominent research topic across academia and industry in recent years. One critical aspect of advanced manufacturing is how to optimally cope with the complexities of multiple-objective decision-making to implement advanced manufacturing technologies with currently available enterprise resources and the realistic manufacturing conditions of a company. Generally, to successfully fulfill an advanced manufacturing plan, decision-makers must align short-term objectives with long-term strategies. In addition, the decision-making process usually has to prioritize multiple-objective goals under a considerable number of uncertainties. This requirement presents new challenges for both planning and implementing advanced manufacturing technologies, and thus calls for new approaches for to better support such tasks. This paper proposes a knowledge representation and computational active learning-based framework for dealing with complex, multiple-objective decision-making problems for advanced manufacturing under realistic conditions. Through this study, we hope to shed light on using a simulation framework for multiple-objective decision support, thereby providing an alternative for manufacturing enterprises, which could lead to an acceptable optimal decision with reasonable cost and accuracy. First, we describe the scope of an advanced manufacturing system for industrial manufacturing. Next, we introduce systematic analysis of the complexities of the decision-making to implement advanced manufacturing. Finally, we propose a simulation model for the decision-making and formulate a computational active learning-based framework to efficiently compute goal priorities for multiple-objective decision-making. We validate the framework by presenting a simulation of decision-making.
机译:由于信息技术的飞速发展和更高效,更适应制造工艺的推动,近年来,先进制造的概念已成为学术界和工业界日益突出的研究课题。先进制造的一个关键方面是如何最佳地应对多目标决策的复杂性,以利用当前可用的企业资源和公司的实际制造条件来实施先进的制造技术。通常,为了成功地执行高级制造计划,决策者必须使短期目标与长期战略保持一致。此外,决策过程通常必须在存在大量不确定性的情况下确定多目标目标的优先级。这项要求对规划和实施先进的制造技术提出了新的挑战,因此需要新的方法来更好地支持此类任务。本文提出了一种知识表示和基于计算主动学习的框架,用于处理现实条件下高级制造的复杂,多目标决策问题。通过本研究,我们希望阐明使用仿真框架进行多目标决策支持的方法,从而为制造企业提供替代方案,从而可以以合理的成本和准确性获得可接受的最佳决策。首先,我们描述了用于工业制造的先进制造系统的范围。接下来,我们介绍对实施先进制造的决策复杂性的系统分析。最后,我们为决策提供了一个仿真模型,并制定了一个基于学习主动学习的框架,可以有效地计算多目标决策的目标优先级。我们通过提出决策模拟来验证该框架。

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