Application of a BIP Constrained Optimization Model to Select the Optimal Sequence of Space Missions and Vehicles to Implement the Proposed International Space Exploration and Utilization Initiative of January 14, 2004
- Paper number
IAC-05-D3.1.09
- Author
Dr. George Morgenthaler, University of Colorado at Boulder, United States
- Year
2005
- Abstract
A) On January 14, 2004, President George W. Bush announced a broad invitation to all Space-faring Nations and all User Groups in Science, Industry, Robotic and Human Space Exploration and Utilization: Space tourism, Colonization, etc., to partner together in a Multi-Year, Multi-Mission, International Sustainable Space Initiative. The intention is to return to the Moon by the year 2020 and, as soon as possible thereafter, to send robotic and human missions to Mars and beyond, throughout the Solar System. The immediate response of most Space-faring Nations, Industry Investors, and User Groups (Scientists, Space Tourists, Colonizers, Explorers, etc.) was positive. However, each such group claimed that its own preferred Missions were of the ḧighest global priority.Thus the number of Missions being proposed by the USA, plus those proposed by other Nations and those proposed at International Astronautical Conferences far exceeded foreseeable Space resources and budgets, exceeding those of the US alone, and possibly even those of an International Partnership. The International Academy of Astronautics IAA’s Commission III, focused on Space Technology and Space System Development, appointed two IAA Space Study Groups to prepare Multi-Year, Multi-Mission Space Program Planning Architectures to best implement the January 14, 2004 Presidential Initiative. The paper, IAC-04-IAA.3.6.3.04 by Prof. G. Morgenthaler and Dr. Gordon Woodcock, presented at the 55th International Astronautical Conference in Vancouver, B.C., Canada, on October 6, 2004, developed a Binary Integer Programming BIP Constrained Optimization Model for selecting the most cost-effective collection of Space Missions and newly designed Space vehicles to achieve the goal of Optimally implementing the President’s Space Initiative.
B) The IAA Request: At the IAA Commission III meeting of October 3, 2004, at the Vancouver IAC, it was requested by the Chair of IAA Commission III, Mr. Hans Hoffman, that the IAC-04-IAA.3.6.3.04 BIP Constrained Optimization Model be actually applied to representative Multi-Year, Multi-Mission Space Exploration Architectures SEAs to identify and demonstrate the specific software algorithm Solvers that would enable practical applications of this Space Program Optimizing tool to competently rank representative Multi-Year, Multi-Mission Space Exploration Architectures SEAs. The Model is to select the optimal sequence of Missions from among the myriad of Missions that have been proposed, being sure that the selected optimal Mission sequences each satisfies all of the constraints of Space Program budgets, safety restrictions, technology limitations, astronaut availability, International Space Law Treaties, etc.
C) THE ADVANCED TECHNOLOGY LIFECYCLE ANALYSIS SYSTEM (ATLAS): developed by NASA, was also presented at the 55th International Astronautical Conference in Vancouver, B.C., Canada, as #IAC-04-IAA.3.6.3.01. The ATLAS provides strategic planners with a decision support tool to evaluate potential technology portfolios associated with a Space Exploration Architecture (SEA). A library of system concept models, cost models, and technology database brings together experts from various communities. Technologists provide state-of-the-art and forecasted performance, operations, and programmatic data. Concept development teams produce Excel workbooks that use the performance data and system configuration inputs to produce system mass statements, associated technology portfolios, and operations data and lifecycle costs for the SEA.This system is now ön-lineänd the description of the ATLAS Model suggests that: 1) ATLAS could be of direct help in assisting the analysts to put the Mission data into the BIP Constrained Optimization Model; and 2) ATLAS could serve as a standard to assure that all User Groups use data of comparable quality, two outstanding advantages in which ATLAS helps the BIP Model to be efficient and valid! While ATLAS has numerous analysis tools, bar graphs and technology metrics for comparing and selecting the better SEAs, it does not seem to have a global Constrained Optimizer Sub-Model that can investigate the millions of scenarios that occur when there are a truly large number of parameters and constraints. The classical BIP Branch and Cut Constrained Optimization Algorithm Solver and the Metaheuristic Solver methods to be investigated below, can handle thousands of variables and constraints. If the BIP Constrained Optimization Model turns out to be a truly practical and solvable SEA tool, it will have gained from ATLAS. In turn, ATLAS will have gained an Optimization Sub-Model from the BIP Constrained Optimization Model; and the Space Initiative Implementation will have gained from both of them; a truly WIN/WIN/WIN forward step!
D The Fukuoka Presentation: The authors are currently working to apply the above models to various Space Exploration Architectures proposed in the literature and to those presented by Dr. Ernesto Vallerani and Dr.Wes Huntress, etal., See Stepping Stones to. Marsïn the 55th IAC Program at Vancouver, B.C., Canada in October, 2004. The Vallerami SEA made heavy use of the Earth/Mars L1 Lagrange point as an efficient Gatewayẗo deep Solar System Exploration. The Wes Huntress, etal., SEA emphasized the use of the Sun/Earth L2 Lagrange point as an ideal avenue for future Hubble, Kuiper, and Moon- based telescopes for deep Space Astrophysical exploration. As the model is applied to the two IAA Study Group-recommended Space Exploration Architectures SEAs it may be that the very sizeöf the problem is too large for the traditional BIP Branch and Cut algorithm to solve. This may be particularly true when the most recent list of vehicle designs to implement the SEA include various Crew Exploration Vehicle CEV designs, various reusable Earth-Orbit-to-Lunar-Orbit-and-Return Shuttle vehicle designs, and various up/down reuseable Lunar Lander designs, etc. If the problem cannot be solved by the Branch and Cut Algorithm, or even if the problem can be so solved, the authors will apply various Metaheuristic Solver algorithms such as Genetic Algorithms, Tubu Search, etc., to seek the most efficient method to make this Space Mission Planning tool practical. The results of applying these models will be presented at the 56th IAC in Fukuoka, Japan, October 17-21, 2005.
- Abstract document
- Manuscript document
IAC-05-D3.1.09.pdf (🔒 authorized access only).
To get the manuscript, please contact IAF Secretariat.