Separating Design Optimization Problems Into Decision-Based Design Processes

Karimian, Peyman; Herrmann, Jeffrey W.

doi:10.1115/1.3013443

Cited by 10 publications

(15 citation statements)

References 46 publications

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“…In this paper, we follow up on the idea suggested in [39] that separation could be used to model an engineering design process. We will study separations corresponding to different types of progressive design processes.…”

Section: Modeling Design Processesmentioning

confidence: 99%

“…A separation is a type of problem decomposition that divides a design optimization problem into a set of subproblems, solves each subproblem once, and produces a feasible solution without iterative cycles [39]. The ideal separation produces an optimal solution to the original problem.…”

Section: Modeling Design Processesmentioning

confidence: 99%

“…We then model different progressive design processes using separations, a special class of problem decompositions that are significantly different from typical decomposition-based design optimization methods [39].…”

Section: Detc2009-86462mentioning

confidence: 99%

“…Previous work evaluated the quality of different separations by using optimization to solve each subproblem [39].…”

Section: Modeling Design Processesmentioning

confidence: 99%

“…Because humans are bounded rational decision-makers, we do not use optimization to solve the subproblems in the separation (as done in [39]). Instead, like Gurnani and Lewis [40], we model the designer's bounded rational design choices as a random process.…”

Section: Detc2009-86462mentioning

confidence: 99%

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Separating Design Optimization Problems for Bounded Rational Designers

Herrmann

2009

Volume 5: 35th Design Automation Conference, Parts a and B

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This paper presents a method for assessing the quality of progressive design processes that seek to maximize the profitability of the product that is being designed. The proposed approach uses separations, a type of problem decomposition, to model progressive design processes. The subproblems in the separations correspond roughly to phases in the progressive design processes. We simulate the choices of a bounded rational designer for each subproblem using different search algorithms. We consider different types and versions of these search processes in order to determine if the results are robust to the decision-making model. We use a simple twovariable problem to help describe the approach and then apply the approach to assess motor design processes. Methods for assessing the quality of engineering design processes can be used to guide improvements to engineering design processes and generate more valuable products.

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Section: Modeling Design Processesmentioning

confidence: 99%

Section: Modeling Design Processesmentioning

confidence: 99%

Section: Detc2009-86462mentioning

confidence: 99%

“…Previous work evaluated the quality of different separations by using optimization to solve each subproblem [39].…”

Section: Modeling Design Processesmentioning

confidence: 99%

Section: Detc2009-86462mentioning

confidence: 99%

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Separating Design Optimization Problems for Bounded Rational Designers

Herrmann

2009

Volume 5: 35th Design Automation Conference, Parts a and B

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Optimization problem formulation framework with application to engineering systems

Crossley

Luan

Allison

et al. 2017

Systems Engineering

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Engineering systems require considering technical, social, economic, and environmental issues on a large and interconnected scale. Addressing engineering systems problems challenges the limits of analytic methods; this paper focuses on optimization as the method. Traditional normative optimization addresses objective trade‐off issues by quantifying the relationships among decision‐maker choices and system performance. Engineering systems can present options and trade‐offs that are outside traditional analysis boundaries and often require different domain expertise. The usual response is to expand the frame of analysis, relying on expert heuristic rules of thumb to make the task manageable. However, these heuristics can create irrelevant constraints, lead to cognitive biases, or result in unnecessarily suboptimal solutions. This paper presents an initial framework to encourage explicit examination of decisions made when framing and formulating an engineering systems‐relevant optimization problem. This includes consciously determining when to keep heuristics for efficiency and when to replace them with a more normative approach. The paper provides two examples that use optimization in two different roles. The first uses an optimization problem in a simulation tool that predicts future CO2 emissions from commercial aviation for scenarios with different technology and economic inputs. The second example is based on an actively controlled wind turbine and demonstrates how a reasonable‐seeming heuristic for defining the objective can lead to a result that is suboptimal in terms of the true system performance objective. These examples demonstrate the explicit examination of the framing and formulation to achieve a good combination of normative and heuristic approaches.

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A strategy for resolving evolutionary performance coupling at the early stages of complex engineering design

Zhao

2011

Journal of Engineering Design

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Separating Design Optimization Problems Into Decision-Based Design Processes

Cited by 10 publications

References 46 publications

Separating Design Optimization Problems for Bounded Rational Designers

Separating Design Optimization Problems for Bounded Rational Designers

Optimization problem formulation framework with application to engineering systems

A strategy for resolving evolutionary performance coupling at the early stages of complex engineering design

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