Stephanie Introne
(Advisor: Prof. Dimitri Mavris)
will defend a doctoral thesis entitled,
A Methodology for Forecasting and Mitigating Risk in Cislunar Mission Planning Using Real Options
On
Monday, October 28 at 9:00 a.m.
Collaborative Visualization Environment (CoVE)
Weber Space Science and Technology Building (SST III)
And
Abstract
Space mission planning is made extremely difficult by the nature of space itself: it is a hostile environment for human exploration, requiring large budgets and advanced technologies. Space exploration is also a dangerous endeavor; crewed missions have consistently exceeded the risk guidelines set by NASA, and the most stringent safety requirements are set on the launch and landing phases, which are also the phases where an accident is statistically most likely to occur. The focus of future exploration is on creating a foundation on the Moon for the eventual launch of crewed Mars missions, so the operating environment is becoming even more challenging. Additionally, mission budgets are tied to public perception and assumed risk, so there is an increased need for methodology improvements to reduce programmatic and personal risk.
Real options and analysis through decision trees presents serious potential for improving decision-making methods. Real options is an analysis strategy that borrows principles from economics and financial markets to model how the value of a project changes over time, where the project exists in an uncertain future. A major advantage of real options is the built-in flexibility: there are opportunities at decision nodes to abandon a project, increase investment, or change the structure. It was hypothesized that applying real options to space mission planning would enable methodology improvements and meet these stated needs
There are limitations in the application of real options that have prevented its widespread use for this type of problem in the past, including that the decision trees that need to be developed for this kind of approach include too many nodes for a decision-maker to fully consider all of their options, many of these mission planning methods create silos around individual metrics of success which lead to the need for iterative designs and repeated sequential analysis, there are an intractable number of possible failures so there is a need to determine the areas that are most critical for analysis and mitigation, and traditional planning methods are driven by Subject Matter Expert (SME) expertise but do not contain the necessary structure to overlay subjective information from the stakeholder and scenario definition. These limitations define the experimental gaps that are addressed through the hypotheses and results.
The overarching research hypothesis aims to develop a methodology that uses real options to locate satisficing architectures more efficiently and include input from decision-makers. The use of decision trees accelerates architecture selection because the results of the analysis display the possible outcomes for a variety of architectures and include uncertainty information about each individual architecture as well as the entire scenario in general. The primary inclusion of user input comes through the section of the methodology involving the interactions with the tabletop. The explicit purpose of the tabletop is to include this input through allowing the selection of the relevant architectures and allowing the user to modify the scenario and requirements and see how that impacts the preferred mission architectures in real time.
Committee
- Prof. Dimitri Mavris – School of Aerospace Engineering (advisor)
- Dr. Mark Whorton – Georgia Tech Research Institute/School of Aerospace Engineering
- Prof. Glenn Lightsey – School of Aerospace Engineering
- Prof. Mariel Borowitz – Sam Nunn School of International Affairs
- Dr. Michael Balchanos – School of Aerospace Engineering