Stochastic Aircraft Optimization and Decision Making using a Competitive Value-Driven Design Framework

Desai, Abdullah; Hollingsworth, Peter; Jinks, Stuart

doi:10.2514/6.2018-2869

Cited by 2 publications

(3 citation statements)

References 9 publications

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“…The method employs a sequential Monte Carlo Simulation (MCS) to generate random samples (or trajectories) of component performance based on adverse condition-dependent degradation rates and restoration models. Similarly, Desai and Hollingsworth (2018) show that by using second-order stochastic dominance it is possible to reduce the risk of incorrect decisions being taken due to the risk aversion of the engineering team. Wang and Kannah (2017) goes a step further in their attempt to quantify uncertainty by borrowing the Gini Coefficient from the field of economics to measure the inequality among values of a frequency distribution.…”

Section: The Emergence Of Uncertainty Modellingmentioning

confidence: 99%

Value Driven Design Revisited: Emerging Modelling Concepts and Applications

Bertoni

Hakki

2019

Proc. Int. Conf. Eng. Des.

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Extended product warranties, leasing, and pay-per-use schemes are few examples of how manufacturing companies are shifting their focus from selling products to offering “solutions”, combining products and services to maximise customer value. The idea of optimising a system towards the best ‘value’ is both “intuitive and sensible”, and processes such as Value Driven Design (VDD), have been proposed to supplement traditional engineering design methodologies. Yet, value-driven initiatives struggle to communicate their benefits and to demonstrate how value models can solve actual problems. The aim of this paper is to shed light on the evolution of the VDD toolbox, mapping research clusters, applications and case studies from the perspective of how much (and how well) recent contributions have answered the five top-level questions of the VDD research agenda published in 2012. The results of the systematic literature review reveal the emergence of innovative modelling constructs, methods, and tools that help VDD in taking a leap forward in supporting organizations and teams in achieving the long-sought objective of designing for 'value'.

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Section: The Emergence Of Uncertainty Modellingmentioning

confidence: 99%

Value Driven Design Revisited: Emerging Modelling Concepts and Applications

Bertoni

Hakki

2019

Proc. Int. Conf. Eng. Des.

View full text Add to dashboard Cite

show abstract

“…Instead, Jimenez and Mavris [12] determine Pareto-optimal solutions, which are the set of technologies that lie on a Pareto front of the objectives. A similar approach [13] uses stochastic dominance to remove design alternatives (i.e., technology portfolios) from the complete set, leaving decision makers with only optimal alternatives, to reduce the risk of incorrect decisions. While these approaches do retain the full information of the CDFs, it becomes harder to rank the technologies because crossing CDFs do not exhibit stochastic dominance and subjective judgment is needed to prefer one solution over another.…”

Section: Selecting Technologies In Aircraft Conceptualmentioning

confidence: 99%

“…Fig 13. Probability density functions for 2nd DOF flap technology with original (blue) distributions and for a 15% PRE −1 reduction (red, dashed).…”

mentioning

confidence: 99%