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.

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