Sandia National Laboratories is a multiprogram laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy’s National Nuclear Security Administration under contract DEAC0494AL85000. Problem Sandia National Laboratories Christopher G. Valicka (presenting), William E. Hart, M. D. Rintoul, Scott A. Mitchell, Eric L. Pollard, Simon X. Zou, Stephen Rowe Preliminary Results Approach Significance MixedInteger Formulations for Constellation Scheduling SAND2015 Remote sensing systems have expanded the set of capabilities critical to national security. Constellations of agile, highfidelity sensing systems and growing mission applications have exponentially increased the set of potential schedules. Advanced scheduling tools are lacking and operators are overburdened. Nowhere is this more costly than in timecritical scheduling decisions. Assisted decisionmaking through identification and comparison of alternative schedules remains a challenging problem applicable across remote sensing systems. Using the Pyomo optimization modeling tool, we have developed software in Python and C++ that poses and solves mixedinteger models for foundational problems in scheduling constellations of remote sensors: Scheduling where activity demands outnumber available sensor resources Optimize according various criteria: Activity priority, Highfidelity measures of sensor performance Allow for customer tuning of criteria for acceptable activity performance Create schedules that obey periodic calibration and sensor safety constraints Sensor starepoint (footprint) placement optimization within individual activities and across geospatially separate activities Optimization of subfootprint sizing and placement, according to sensor and bandwidth constraints Optimization of starepoint placement according to optimal subfootprint solutions Scheduling to minimize disruptions caused by adhoc activities and weather • Framework designed and implemented to carefully trade schedule optimality for timely decision support • Support for arbitrary numbers of sensors and activities and variable precision timesteps • Framework is adaptable to changing schedule objectives, adhoc activities, and periodic calibration activities • Mixedinteger model provides access to valuable analytics Computed four sensor schedule. Darker blues denote higher activity priority. Calibration activities are black. Accessible draw band pass and cloud database Receiver Operating Characteristics (ROCs). Sensor Performance measures: 1. Probability of Detect (PD) 2. Geolocation Accuracy 3. Closely Spaced Objects (CSO) 4. Access 5. Geometric Coverage Screenshots from dynamic sensor footprint visualization. Examples of optimal (circular) footprint placements for individual activities. Footprint radius denoted in green, coverage points denoted in red.
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Sandia National Laboratories is a multi-‐program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy’s National Nuclear
Security Administration under contract DE-‐AC04-‐94AL85000.
Problem
Sandia National LaboratoriesChristopher G. Valicka (presenting), William E. Hart, M. D. Rintoul, Scott A. Mitchell, Eric L. Pollard, Simon X. Zou, Stephen Rowe
Preliminary Results
Approach
Significance
Mixed-‐Integer Formulations for Constellation Scheduling
SAND2015-‐
Remote sensing systems have expanded the set of capabilities critical to national security. Constellations of agile, high-fidelity sensing systems and growing mission applications have exponentially increased the set of potential schedules. Advanced scheduling tools are lacking and operators are overburdened. Nowhere is this more costly than in time-critical scheduling decisions. Assisted decision-making through identification and comparison of alternative schedules remains a challenging problem applicable across remote sensing systems.
Using the Pyomo optimization modeling tool, we have developed software in Python and C++ that poses and solves mixed-integer models for foundational problems in scheduling constellations of remote sensors:
Ø Scheduling where activity demands outnumber available sensor resources
Ø Optimize according various criteria:² Activity priority,² High-fidelity measures of sensor
performance
Ø Allow for customer tuning of criteria for acceptable activity performance
Ø Create schedules that obey periodic calibration and sensor safety constraints
Ø Sensor stare-point (footprint) placement optimization within individual activities and across geo-spatially separate activities
Ø Optimization of sub-footprint sizing and placement, according to sensor and bandwidth constraints
Ø Optimization of stare-point placement according to optimal sub-footprint solutions
Ø Scheduling to minimize disruptions caused by adhocactivities and weather
• Framework designed and implemented to carefully trade schedule optimality for timely decision support• Support for arbitrary numbers of sensors and activities and variable precision time-‐steps• Framework is adaptable to changing schedule objectives, adhoc activities, and periodic calibration activities• Mixed-‐integer model provides access to valuable analytics
Computed four sensor schedule. Darker blues denote higher activity priority.
Calibration activities are black.
Accessible draw band pass and cloud database Receiver Operating Characteristics (ROCs).
Sensor Performance measures:
1. Probability of Detect (PD)
2. Geolocation Accuracy
3. Closely Spaced Objects (CSO)
4. Access
5. Geometric Coverage
Screenshots from dynamic sensor footprint visualization.
Examples of optimal (circular) footprint placements for individual activities. Footprint radius denoted in green,
coverage points denoted in red.
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