利用Pareto跟踪法确定系统被动值的鲁棒性

A. Ross, D. Rhodes, D. Hastings
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引用次数: 36

摘要

在概念阶段进行设计决策时,系统设计者的一个重要作用是有效地探索备选方案的交易空间。随着系统变得越来越复杂,实现良好设计决策的形式化方法变得至关重要;这可以通过贸易空间探索范式来实现。本文演示了使用帕累托跟踪和相关度量来识别具有高度被动价值鲁棒性的跨交易空间的系统替代方案——尽管需求(属性)或上下文发生了变化,这些替代方案仍能继续向涉众交付价值。价值驱动的交易空间方法用于表示大量系统替代方案的基准性能与成本。经典帕累托集的概念被扩展到识别替代方案及其特征,这些特征导致它们在不断变化的环境中包含在帕累托集中。以近地轨道卫星为例,采用五种类型的上下文变化来演示该方法:1)属性的增加或减少;2)属性优先级的变化;3)单属性效用函数形状变化;4)多属性效用聚合函数的变化;5)增加新的决策者。这种方法证明了系统设计者在早期概念设计中提出关于评估备选方案的问题的能力。讨论并提出了应用帕累托法的建议,包括“模糊性”因素和统计度量的应用。特别是,与传统的敏感性分析的区别,以及与动态分析的联系,以发现广义价值稳健的替代品。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using Pareto Trace to determine system passive value robustness
An important role of system designers is to effectively explore the tradespace of alternatives when making design decisions during concept phase. As systems become more complex, formal methods to enable good design decisions are essential; this can be empowered through a tradespace exploration paradigm. This paper demonstrates the use of the Pareto Trace and associated metrics to identify system alternatives across tradespaces with high degrees of passive value robustness—alternatives that continue to deliver value to stakeholders in spite of changes in needs (attributes) or context. A value-driven tradespace approach is used to represent the baseline performance versus cost of a large number of system alternatives. The classical notion of Pareto Set is extended to identify alternatives and their characteristics that lead to their inclusion in Pareto Sets across changing contexts. Using a low-earth orbiting satellite case example, five types of context changes are used to demonstrate this method: 1) addition or subtraction of attributes; 2) change in the priorities of attributes; 3) change in single attribute utility function shapes; 4) change in multi-attribute utility aggregation function; and 5) addition of new decision maker. This approach demonstrates the ability for system designers to pose questions about assessment of alternatives during early conceptual design. Suggestions for application of Pareto Trace beyond the case example are discussed and presented, including application of a “fuzziness” factor and statistical measures. In particular, distinctions from traditional sensitivity analysis are made, as well as linkages to dynamic analysis for discovery of generalized value robust alternatives.
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