面向鲁棒设计方案探索的工程设计耦合决策问题分类与执行

Gehendra Sharma, J. Allen, F. Mistree
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引用次数: 4

摘要

决策支持问题(dsp)用于对涉及多种权衡的设计决策进行建模。在实践中,这样的设计决策也是耦合的,也就是说,这些决策必须通过识别和处理它们对彼此施加的影响来建模。因此,我们需要对耦合决策问题进行分类,并介绍管理此类问题的不确定性的方法。耦合决策问题的分类允许决策模板的开发和执行,以影响设计并在计算机上存档与设计相关的知识。合并健壮度量允许通过管理不确定性来探索耦合决策问题的健壮设计解决方案。在本文中,我们提出了一种使用dsp进行耦合决策的分类方案,称为决策场景矩阵,并通过使用dsp解决耦合问题来说明其效用。以作为挡泥板的梁的设计为例,说明了耦合问题公式的有效性。在第一个例子中,我们确定了一个稳健设计,即确定挡泥板的尺寸,同时设计材料,认识到计算模型是不完整和不准确的。在第二个例子中,当设计决策是耦合的,即确定挡泥板的尺寸和同时选择材料时,我们确定了鲁棒设计方案。在本文中,我们的重点是说明该方法的功效,而不是结果。
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Classification and Execution of Coupled Decision Problems in Engineering Design for Exploration of Robust Design Solutions
Decision Support Problems (DSPs) are used to model design decisions involving multiple trade-offs. In practice, such design decisions are also coupled, that is, these decisions must be modelled by identifying and addressing the influence they exert on one another. Hence, we need to classify coupled decision problems and to introduce methods for managing uncertainty for such problems. Classification of coupled decision problems allows for the development and execution of decision templates to effect design and to archive design-related knowledge on a computer. Incorporating robustness metrics allows for the exploration of robust design solutions for coupled decision problems by managing uncertainty. In this paper, we present a classification scheme for coupled decisions using DSPs, called the Decision Scenario Matrix and we illustrate its utility by solving a coupled problem using DSPs. The design of a beam to be used as a fender is used to illustrate the efficacy of the formulation of coupled problems. In the first example, we determine a robust design, that is, determine the dimensions of the fender and simultaneously design the material recognizing that the computational models are incomplete and inaccurate. In the second example, we determine robust design solutions when design decisions are coupled, that is, determine the dimensions of the fender and select the material concurrently. Our focus, in this paper, is on illustrating the efficacy of the method rather than on the results.
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