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Proceedings Paper

Multi-objective optimization to support rapid air operations mission planning
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Paper Abstract

Within the context of military air operations, Time-sensitive targets (TSTs) are targets where modifiers such, “emerging, perishable, high-payoff, short dwell, or highly mobile” can be used. Time-critical targets (TCTs) further the criticality of TSTs with respect to achievement of mission objectives and a limited window of opportunity for attack. The importance of TST/TCTs within military air operations has been met with a significant investment in advanced technologies and platforms to meet these challenges. Developments in ISR systems, manned and unmanned air platforms, precision guided munitions, and network-centric warfare have made significant strides for ensuring timely prosecution of TSTs/TCTs. However, additional investments are needed to further decrease the targeting decision cycle. Given the operational needs for decision support systems to enable time-sensitive/time-critical targeting, we present a tool for the rapid generation and analysis of mission plan solutions to address TSTs/TCTs. Our system employs a genetic algorithm-based multi-objective optimization scheme that is well suited to the rapid generation of approximate solutions in a dynamic environment. Genetic Algorithms (GAs) allow for the effective exploration of the search space for potentially novel solutions, while addressing the multiple conflicting objectives that characterize the prosecution of TSTs/TCTs (e.g. probability of target destruction, time to accomplish task, level of disruption to other mission priorities, level of risk to friendly assets, etc.).

Paper Details

Date Published: 19 May 2005
PDF: 9 pages
Proc. SPIE 5805, Enabling Technologies for Simulation Science IX, (19 May 2005); doi: 10.1117/12.609736
Show Author Affiliations
Paul G. Gonsalves, Charles River Analytics, Inc. (United States)
Janet E. Burge, Charles River Analytics, Inc. (United States)


Published in SPIE Proceedings Vol. 5805:
Enabling Technologies for Simulation Science IX
Dawn A. Trevisani; Alex F. Sisti, Editor(s)

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