(6) CubeSat Decision Advisor

Decision theory has commonly been used in applications of economics, investment strategy, game theory, medicine, the oil and gas industry, and operations engineering. Existing applications in the aerospace industry are limited to large-scale missions or design studies. This portion of my research serves as the first application of decision theory to the emerging topic of small spacecraft missions. Decision analysis methods in the area of risk management are used to identify the mission risk and/or root cause which, when mitigated, is the most efficient use of resources given the user-defined constraints of implementation cost, personnel requirements, and time to completion. The primary result of the research is a software tool specifically created for solving this problem via multi-attribute utility theory combined with decision analysis principles. The tool is purposely designed for use by a spacecraft mission designer of any background or experience level and prompts the user to enter their mission-specific data, as well as provides options for the user to select the calculations they wish to analyze.

The software tool employs a normative risk management methodology with an interactive framework by which users can examine their spacecraft mission risks. The approach relies on the fundamental principles of decision analysis through the use of decision trees, utility curves, and influence diagrams. The risk management function of the software tool queries users for their outcome preferences, their choice of mitigation techniques and the probability of success for each technique, and their cost, time, and personnel resource allocation. Together, these inputs generate the utility curves which are then used for determining the expected utility of each mitigation technique.

The seven mission risks and associated root causes, as identified by prior research, are translated into the decision analysis framework shown in Figure 1. The software tool prompts the user for their preference system and determines a joint utility function by following multi-attribute utility theory. After obtaining the user outcome preferences, the user enters their unique mission parameters for analysis through a graphical user interface (GUI) such as the one shown in Figure 2. A separate GUI exists for each mission risk, and each GUI includes all of the root causes for that mission risk along the tabs. The user selects the mitigation techniques they wish to analyze, as well as provides their estimates of success probabilities and resource allocation of cost, time, and personnel required for each mitigation technique. The user is then able to identify, based on their unique set of preferences and probabilities, the mitigation technique which will yield the maximum expected utility for a single root cause of a single mission risk. The maximum root cause utility values from all mission risks are then recorded on a summary page where the user can identify the technique(s) they wish to implement in order to reduce the mission risk(s). Images of the decision tree for the Schedule (SCH) risk and the summary page which contains the results of applying multi-attribute utility theory to each mission risk are shown in Figure 3 and Figure 4, respectively.

Check out the videos below for a visual explanation of how to use the software tool.

To request a copy of the tool, please fill out the request form. Feedback is welcome! Please supply feedback on the feedback page.

Please read the User's Guide for more information on how to use the tool as well as the mathematical foundation and decision analysis principles. The SciTech Conference paper on the CubeSat Decision Advisor is available through AIAA or on the Publications page.