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This study offers insight into the processes of expert designers at the Jet Propulsion Laboratory (JPL) and how they make use of heuristics in the design process. A methodology for the extraction, classification, and characterization of heuristics is presented. Ten expert participants were interviewed to identify design heuristics used during early stage space mission design at JPL. In total, 101 heuristics were obtained, classified, and characterized. Through the use of post-interview surveys, participants characterized heuristics based on attributes including source/origin, applicability based on concept maturity, frequency of use, reliability, and tendency to evolve. These findings are presented, and statistically analyzed to show correlations between the participant perceptions of frequency of use, reliability, and evolution of a heuristic. Survey results and analysis aim to identify valid attributes for assessing the applicability and value of multiple heuristics for design practice in early space mission formulation. 

In designing complex systems, systems engineers strive to turn an existing situation into a situation that is most preferred. A rational decision maker would choose the alternative that maximizes the expected utility of the existing situation, but there are significant computational challenges to this approach. To overcome these challenges, most decision makers revert to heuristics. In this paper, we propose a conceptual framework for heuristics in design. A preliminary empirical study of designers for a robotics design problem was conducted to observe how participants apply heuristics. Participants having at least 2 years of experience designing robots were recruited to partake in a robotics design session in which participant were given 45 minutes to work on a design problem. A preliminary heuristics extraction method was developed, and the identified heuristics were studied to find trends within the overall set. These trends were the basis of a taxonomy of heuristics consisting of three initial classification methods: design phase, field of study, and action intent. The heuristics and classifications are presented, along with the challenges from extracting heuristics and recommendations for future work to further research design heuristics and to improve the method for extraction. 

In this paper, we introduce a research method for comparing computational design methods. This research method addresses the challenge of measuring the difference in performance of different design methods in a way that is fair and unbiased with respect to differences in modeling abstraction, accuracy and uncertainty representation. The method can be used to identify the conditions under which each design method is most beneficial. To illustrate the research method, we compare two design methods for the design of a pressure vessel: 1) an algebraic approach, based on the ASME pressure vessel code, which accounts for uncertainty implicitly through safety factors, and 2) an optimization-based, expected-utility maximization approach which accounts for uncertainty explicitly. The computational experiments initially show that under some conditions the algebraic heuristic surprisingly outperforms the optimization-based approach. Further analysis reveals that an optimization-based approach does perform best as long as the designer applies good judgment during uncertainty elicitation. An ignorant or overly confident designer is better off using safety factors.