International Telemetering Conference Proceedings / October 28-31, 1996 / Town and Country Hotel and Convention Center, San Diego, California / An operational concept for how a Demand Access Multiple Assignment (DAMA) system could be configured for the NASA Space network is examined. Unique aspects of this concept definition are the use of the Multiple Access system within the Space Network to define an order wire channel that continuously scans the Low Earth Orbit space for potential users and the use of advanced digital signal processing technology to look for the Doppler-shifted carrier signal from the requesting satellite. After the reception of the signal, validation and processing of the request is completed. This paper outlines the concept and the ways in which the system could work.

Multiple-resource satellite scheduling problem (MuRRSP) is a complex and difficult scheduling problem, which schedules a large number of task requests onto ground-station antennas in order to communicate with the satellites. We first examined several exact algorithms that were previously implemented in the machine scheduling field. They are column generation and logic-based Benders decomposition. A new hybrid approach that combines both column generation and logic-based Benders decomposition is proposed. The hybrid performed well when there is a large number of machines. Next, we presented a connection between the parallel machine scheduling problem and MuRRSP in order to solve MuRRSP with exact algorithms. Furthermore, we proposed a strengthened cut in the sub-problem of the logic-based Benders decomposition. The resulting algorithm proved to be very effective. Barbulescu’s benchmark problems were solved and proved optimal with an average run-time less than one-hour. To the best of our knowledge, previous efforts to solve MuRRSP were all heuristic based and no optimal schedules existed.

Multiple-resource satellite scheduling problem (MuRRSP) is a complex and difficult scheduling problem, which schedules a large number of task requests onto ground-station antennas in order to communicate with the satellites. We first examined several exact algorithms that were previously implemented in the machine scheduling field. They are column generation and logic-based Benders decomposition. A new hybrid approach that combines both column generation and logic-based Benders decomposition is proposed. The hybrid performed well when there is a large number of machines. Next, we presented a connection between the parallel machine scheduling problem and MuRRSP in order to solve MuRRSP with exact algorithms. Furthermore, we proposed a strengthened cut in the sub-problem of the logic-based Benders decomposition. The resulting algorithm proved to be very effective. Barbulescu’s benchmark problems were solved and proved optimal with an average run-time less than one-hour. To the best of our knowledge, previous efforts to solve MuRRSP were all heuristic based and no optimal schedules existed.

The recent generation of Agile Earth Observation Satellite (AEOS) has emerged to be highly effective due to its increased attitude maneuvering capabilities. However, due to these increased degrees of freedom in maneuverability, the scheduling problem has become increasingly difficult than its non-agile predecessors. The AEOS scheduling problem consists of finding an optimal assignment of user-requested imaging tasks to the respective AEOSs in their orbits by satisfying the operational resource constraints in a specified time frame. Some of these tasks might require imaging the same area of interest (AOI) multiple times, while in some tasks, the AOIs are too large for the AEOS to image in a single attempt. Some tasks might even arise while the AEOSs are preoccupied with existing tasks. This thesis focuses on formulating the AEOS scheduling models where onboard energy and memory constraints while operating and the task specifications are diverse. A mixed-integer non-linear scheduling problem with a reward factor has been considered in order to handle multiple scan requirements for a task. Although initially, it is assumed that the AOIs are small, this work is extended to a three-stage optimization framework to handle the segmentation of large AOIs into smaller regions that can be imaged in a single scan. The uncertainty regarding scan failure is handled through a Markov Decision Process (MDP). These two proposed methods have significant benefits when tasks are available to schedule prior to the mission. However, they lack the flexibility to accommodate newly arrived tasks during the mission. When multiple new tasks arrive during the mission, predictive scheduling based on learning historical data of task arrivals is proposed, which can schedule tasks in an online manner faster than complete rescheduling and minimize disruption from the original schedule. Evolutionary optimization-based solution methodologies are proposed to solve these models and are validated with simulations. / Thesis / Doctor of Philosophy (PhD)

Thesis (M.Com.(quantitative Management)) / Satellite Scheduling Problems (SSP) are NP-hard and constraint programming and metaheuristics solution methods yield mixed results. This study investigates a new version of the SSP, the Satellite Constellation Time-Window Optimization Problem (SCoTWOP), involving commercial satellite constellations that provide frequent earth coverage. The SCoTWOP is related to the dual of the Vehicle Routing Problem with Multiple Timewindows, suggesting binary solution vectors representing an activation of time-windows. This representation fitted well with the MatLab® Genetic Algorithm and Direct Search Toolbox subsequently used to experiment with genetic algorithms, tabu search, and simulated annealing as SCoTWOP solution methods. The genetic algorithm was most successful and in some instances activated all 250 imaging time-windows, a number that is typical for a constellation of six satellites. / Quantitative Management

Thesis (M.Com.(quantitative Management)) / Satellite Scheduling Problems (SSP) are NP-hard and constraint programming and metaheuristics solution methods yield mixed results. This study investigates a new version of the SSP, the Satellite Constellation Time-Window Optimization Problem (SCoTWOP), involving commercial satellite constellations that provide frequent earth coverage. The SCoTWOP is related to the dual of the Vehicle Routing Problem with Multiple Timewindows, suggesting binary solution vectors representing an activation of time-windows. This representation fitted well with the MatLab® Genetic Algorithm and Direct Search Toolbox subsequently used to experiment with genetic algorithms, tabu search, and simulated annealing as SCoTWOP solution methods. The genetic algorithm was most successful and in some instances activated all 250 imaging time-windows, a number that is typical for a constellation of six satellites. / Quantitative Management