Special section: New Problem Formulations for Design Under Uncertainty

Uncertain Technology Evolution and Decision Making in Design

[+] Author and Article Information
Jonathan L. Arendt

 Department of Mechanical Engineering, Texas A&M University, College Station, TX 77843arendtj@neo.tamu.edu

Daniel A. McAdams

 Department of Mechanical Engineering, Texas A&M University, College Station, TX 77843dmcadams@tamu.edu

Richard J. Malak1

 Department of Mechanical Engineering, Texas A&M University, College Station, TX 77843rmalak@tamu.edu


Corresponding author.

J. Mech. Des 134(10), 100904 (Sep 28, 2012) (11 pages) doi:10.1115/1.4007396 History: Received January 23, 2012; Revised August 08, 2012; Published September 21, 2012; Online September 28, 2012

The potential for engineering technology to evolve over time can be a critical consideration in design decisions that involve long-term commitments. Investments in manufacturing equipment, contractual relationships, and other factors can make it difficult for engineering firms to backtrack once they have chosen one technology over others. Although engineering technologies tend to improve in performance over time, competing technologies can evolve at different rates and details about how a technology might evolve are generally uncertain. In this article we present a general framework for modeling and making decisions about evolving technologies under uncertainty. In this research, the evolution of technology performance is modeled as an S-curve; the performance evolves slowly at first, quickly during heavy research and development effort, and slowly again as the performance approaches its limits. We extend the existing single-attribute S-curve model to the case of technologies with multiple performance attributes. By combining an S-curve evolutionary model for each attribute with a Pareto frontier representation of the optimal implementations of a technology at a particular point in time, we can project how the Pareto frontier will move over time as a technology evolves. Designer uncertainty about the precise shape of the S-curve model is considered through a Monte Carlo simulation of the evolutionary process. To demonstrate how designers can apply the framework, we consider the scenario of a green power generation company deciding between competing wind turbine technologies. Wind turbines, like many other technologies, are currently evolving as research and development efforts improve performance. The engineering example demonstrates how the multi-attribute technology evolution modeling technique provides designers with greater insight into critical uncertainties present in long-term decision problems.

Copyright © 2012 by American Society of Mechanical Engineers
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Figure 1

S-curve technology evolution model

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Figure 2

Evolving pareto frontiers

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Figure 3

The movement of pareto frontiers following S-curves

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Figure 4

Propagating uncertainty

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Figure 5

Comparing alternatives from payout

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Figure 6

Pareto frontiers and historical data

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Figure 7

Historical evolution

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Figure 8

Decision results

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Figure 9

Power evolution limit and standard deviation search space




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