A new research article co-authored by Hao Chen and Koki Ho (Georgia Tech) and Brian Gardner and Paul Grogan appears in Journal of Spacecraft and Rockets as an “article in advance” manuscript.
This paper develops a flexibility management framework for space logistics mission planning under uncertainty through decision rules and multistage stochastic programming. It aims to add built-in flexibility to space architectures in the phase of early-stage mission planning. The proposed framework integrates the decision rule formulation into a network-based space logistics optimization formulation model. It can output a series of decision rules and generate a Pareto front between the expected mission cost (i.e., initial mass in low Earth orbit) and the expected mission performance (i.e., effective crew operating time), considering the uncertainty in the environment and mission demands. The generated decision rules and the Pareto front plot can help decision makers create implementable policies immediately when uncertainty events occur during space missions. An example mission case study about space station resupply under rocket launch delay uncertainty is established to demonstrate the value of the proposed framework.
This paper evaluates perception of complexity in a novel explanatory model that relates product performance and engineering effort. Complexity is an intermediate factor with two facets: it enables desired product performance but also requires effort to achieve. Three causal mechanisms explain how exponential growth bias, excess complexity, and differential perception lead to effort overruns. Secondary data from a human subject experiment validates the existence of perception of complexity as a context-dependent factor that influences required design effort. A two-level mixed effects regression model quantifies differences in perception among 40 design groups. Results summarize how perception of complexity may contribute to effort overruns and outline future work to further validate the explanatory model and causal mechanisms.
A new research article authored by Joseph Thekinen and Paul Grogan appears in Journal of Computing and Information Science in Engineering as an accepted manuscript this month.
This paper performs an observational human subjects study to investigate how design teams use an information system to exchange, store, and synthesize information in an engineering design task. Framed through the lens of decision-based design, a surrogate design task models an aircraft design problem with 12 design parameters across four roles and six system-level functional requirements. A virtual design studio provides a browser-based interface for four participants in a 30-minute design session. Data collected across 10 design sessions provides process factors about communication patterns and outcome factors about the resulting design. Correlation analysis shows a positive relationship between design iteration and outcome performance but a negative relationship between chat messages and outcome performance. Discussion explains how advances in information exchange, storage, and synthesis can support future design activities.
Two open source repositories share Excel templates to help create oncoplot visualizations of gene mutations in patient populations and swimmer diagrams to explain patient stories.
This repository contains a simple Excel spreadsheet for creating oncoplots to illustrate genetic mutations in a patient population. It does not require or depend on any other software to use. Edit the gray-shaded cells with a designated mutation type ID (below) to identify the observed mutation (rows) for each patient (columns).
The default configuration identifies eight types of mutations (by type ID):
Missense Mutation (green)
Frame Shift Insertion (purple)
In Frame Insertion (dark red)
Frame Shift Deletion (blue)
Splice Site (orange)
Nonsense Mutation (bright red)
Multi Hit (dark blue)
In Frame Deletion (brown)
and specifies nine placeholder genes (rows) for 22 patients (columns).
A bar plot above indicates mutation type per patient and a bar plot to the right indicates mutation type per gene.
Add or remove genes by right-clicking on a row and selecting “Insert” or “Delete”. After inserting a new row, copy and paste the equations for the entire row from an adjacent row.
Add or remove patients by right-clicking on a column and selecting “Insert” or “Delete”. After inserting a new column, copy and paste the equations for the entire column from an adjacent column.
This repository contains a simple Excel spreadsheet for creating swimmer plots to illustrate patient stories. It does not require or depend on any other software to use. Edit any of the cells with blue text to customize the swimmer plots which are available in both horizontal and vertical orientations.
The default configuration allows for up to 100 data items in five categories:
Patient Information — sets the bounds for a swim lane bar (ID, History Start Date, History End Date)
“Treatment” Event — adds a red triangle to the swim lane (Patient ID and Date)
“Biopsy” Event — adds a black circle to the swim lane (requires Patient ID and Date)
“Transplant” Event — adds a green square to the swim lane (requires Patient ID and Date)
“Alive” Indicator — adds an arrow to the end of a swim lane (requires Patient ID)
If you add or remove patients, adjust the chart axis limits to ensure all are visible (left click to select patient axis, right click and select “Format Axis…”).
If you add or more events, edit the chart series (right click, select “Select data…”, click “Add” or “Remove”, similar to existing event series).
Strategy dynamics are hypothesized to be a fundamental factor that influences interactive decision-making activities among autonomous design actors. The objective of this research is to understand how strategy dynamics in characteristic engineering design problems influence cooperative behaviors and collective efficiency for pairs of design actors. Using a bi-level model of collective decision processes based on design optimization and strategy selection, we formulate a series of two-actor parameter design tasks that exhibit four strategy dynamics (harmony, coexistence, bistability, and defection), associated with low and high levels of structural fear and greed. In these tasks, actors work collectively to maximize their individual values while managing the trade-offs between aligning with or deviating from a cooperative collective strategy. Results from a human-subject design experiment indicate cognizant actors generally follow normative predictions for some strategy dynamics (harmony and coexistence) but not strictly for others (bistability and defection). Cumulative link model regression analysis shows a greed factor contributing to strategy dynamics has a stronger effect on collective efficiency and equality of individual outcomes compared to a fear factor. Results of this study establish a foundation for future work to study strategic decision-making in engineering design problems and enable new methods and processes to mitigate potential unfavorable effects of their underlying strategy dynamics through social constructs or mechanism design.
On August 17, 2020 Paul Grogan presented an abstract titled “Risk Dominance as a Decision Criterion for Collective Systems Design” for the 32nd International Conference on Design Theory and Methodology (DTM) at the 2020 ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (IDETC-CIE). Check it out below!
On August 17, 2020 Ambrosio Valencia-Romero presented an abstract titled “Structured to Succeed? Strategy Dynamics in Engineering Systems Design and Their Effect on Collective Performance” for the 32nd International Conference on Design Theory and Methodology (DTM) at the 2020 ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (IDETC-CIE). Check it out below!
On August 17, 2020 Alkim Avsar presented her paper titled “Effects of Locus of Control Personality Trait on Team Performance in Cooperative Engineering Design Tasks” for the 32nd International Conference on Design Theory and Methodology (DTM) at the 2020 ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (IDETC-CIE). Check it out below!
This paper draws on perspectives from co-design as an integrative and collaborative design activity and co-simulation as a supporting information system to advance engineering design methods for problems of societal significance. Design and implementation of the Sustainable Infrastructure Planning Game provides a prototypical co-design platform that leverages the High Level Architecture co-simulation standard. Three role players create a strategic infrastructure plan for agriculture, water, and energy sectors to meet sustainability objectives for a growing and urbaninzing population in a fictional desert nation. An observational human subject study conducts 15 co-design sessions to understand how information system features influence design outcomes. Results show co-simulation facilitates information exchange critical for discovering and addressing interdependencies across role-specific objectives and frequent data exchange is correlated with achieving joint objectives, highlighting the role of co-simulation in co-design settings. Conclusions reflect on the opportunities and challenges presented by co-simulation in co-design settings to address engineering problems for infrastructure systems and more broadly.
Systems engineering and design (SE&D) researchers increasingly tackle questions at the intersection of technical and social aspects of complex systems design. Practical challenges of access, limited observation scope, and long timescales limit empirical study of SE&D phenomena. As a result, studies are typically conducted in model world settings abstracted from the real world, such as behavioral experiments with student subjects. Model worlds must be representative of the phenomena being studied to ensure insights generalize to the real‐world settings. Currently, there is a lack of shared understanding and standards within the SE&D research community to evaluate representativeness of model worlds. This communication captures the results of ongoing efforts to build consensus on this topic: it defines the concept of model worlds, disambiguates representativeness from related concepts, and draws comparisons to other research domains. It outlines a potential path forward and calls for community participation in establishing shared standards for model world representativeness in SE&D research.
