A Computational Method for Mapping the Decision Space of the Lunar Exploration Program

Paper number

IAC-06-D1.3.07

Author

Mr. Willard L. Simmons, Massachussets Institute of Technology (MIT), United States

Coauthor

Prof. Edward Crawley, Massachussets Institute of Technology (MIT), United States

Coauthor

Dr. Benjamin Koo, Taiwan, China

Year

2006

Abstract

This study uses a novel computational technique to identify, rank, and categorize key decisions and uncertainties in NASA's Lunar Exploration Program. Using a computational tool, Object-Process Network (OPN), our method is capable of dealing with hundreds of decision variables in the lunar program. These interacting decision variables can be both socio-political in nature, or involving technological expertise.  For example, one of the most impacting (high-priority) Lunar Exploration Program decisions identified by this method is ``specifying the role of international participants for lunar hardware development”.  This socio-political decision should be resolved early in the program because it is both nearly impossible to change and strongly impacts technical variables such as the design of the vehicle's sub-system interfaces.    This paper discusses both the high- and low-priority decisions and the insights that are gained from using this computational architecting technique.

There are three major elements of this approach.   First, the solution space is described in a meta-language, which models the solution space as a finite number of solution space partitions using decisions variables as the branch points.  These decisions variables are compiled into an executable meta-model, stored in a format called decision graph.  Second, the decision graph is enumerated into a finite number of feasible choices and outcomes.  Third, the graph is iteratively sorted and reduced by a combination of human interaction with the graph, simulation techniques and graph-reduction algorithms.  The sorting and reduction processes simultaneously prune away obviously poor branches of the decision space and ranks the decisions and uncertain variables in the order of most-impacting to least-impacting.

To present the results to a wide range of decision-makers, we have a flexible information visualization interface to show the decision graph in multiple formats. The current reporting interfaces include DSM, Decision-Tree, State-Transition Diagram, Equation Form, and dynamically customizable forms. These human-machine interfaces provide a bi-directional mechanism that allows different kinds of decision-makers to share and present their views and inputs via a common, synchronizable information system.

This study extends a previous method using OPN that enumerated over a thousand mission mode options for Moon and Mars Exploration Architectures.  The updated method implements new algorithms such as Reduced Ordered Binary Decision Diagrams (ROBDDs) and AND/OR Search Trees to expedite the computational tasks in reasoning about the interactive effects of decision variables.

Abstract document

IAC-06-D1.3.07.pdf

Manuscript document

IAC-06-D1.3.07.pdf (🔒 authorized access only).

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