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Deep Space Network Scheduling Using Multi-Objective Optimization with UncertaintyWe have developed a novel technique to incorporate uncertainty modeling within an evolutionary algorithm approach to multi-objective scheduling, with the goal of identifying a Pareto frontier (tradeoff curve) that recognizes the likelihood of events that can impact the schedule outcome. Our approach is particularly applicable to the generation of multiobjective optimized robust schedules, where objectives are assigned a service level, for example that we require an objective value to be greater than or equal to X with Y% confidence. We have demonstrated that such an approach can, for example, minimize scheduling on less reliable resources, based solely on a resource reliability model and not on any ad hoc heuristics. We have also investigated an alternative method of optimizing for robustness, in which we add to the set of objectives a failure risk objective to minimize. We compare the advantages and disadvantages of these two approaches. Future plans for further developing this technology include its application to space-based observatory scheduling problems.
Document ID
20150014724
Acquisition Source
Jet Propulsion Laboratory
Document Type
Conference Paper
External Source(s)
Authors
Johnston, Mark D.
(Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Date Acquired
August 3, 2015
Publication Date
May 12, 2008
Subject Category
Space Communications, Spacecraft Communications, Command And Tracking
Ground Support Systems And Facilities (Space)
Meeting Information
Meeting: 2008 SpaceOps
Location: Heidelberg
Country: Germany
Start Date: May 12, 2008
End Date: May 16, 2008
Sponsors: American Inst. of Aeronautics and Astronautics
Distribution Limits
Public
Copyright
Other

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