Air Force Satellite Control Network
About: Air Force Satellite Control Network is a(n) research topic. Over the lifetime, 32 publication(s) have been published within this topic receiving 201 citation(s).
TL;DR: It can be shown that local search (and therefore metaheuristics based on local search) fail to compete with Gooley's algorithm and Genitor and it is suggested that minimizing schedule overlaps makes it easier to fit larger requests into the schedule.
Abstract: The Air Force Satellite Control Network (AFSCN) coordinates communications to more than 100 satellites via nine ground stations positioned around the globe. Customers request an antenna at a ground station for a specific time window along with possible alternative slots. Typically, 500 requests per day result in more than 100 conflicts, which are requests that cannot be satisfied because other tasks need the same slot. Scheduling access requests is referred to as the Satellite Range Scheduling Problem (SRSP). This paper presents an overview of three key issues: (1) how has the problem changed over the last 10 years, (2) what algorithms work best and (3) what objective function is appropriate for AFSCN. We compared data sets from 1992 and from 2002/2003 and found significant differences in the problems. Our evaluation of solutions focuses on three algorithms: local search, Gooley's algorithm from AFIT, and the Genitor genetic algorithm. It can be shown that local search (and therefore metaheuristics based on local search) fail to compete with Gooley's algorithm and Genitor. Finally, while all prior work on AFSCN minimizes request conflicts, we explore an alternative objective function. Because human schedulers must eventually schedule all requests, it might be better to optimize schedules for ''repairability''. Our results suggest that minimizing schedule overlaps makes it easier to fit larger requests into the schedule.
TL;DR: It is shown how hypothesis driven experimentation and search modeling can both explain algorithm performance and motivate the design of a new algorithm.
Abstract: The best performing algorithms for a particular oversubscribed scheduling application, Air Force Satellite Control Network (AFSCN) scheduling, appear to have little in common. Yet, through careful experimentation and modeling of performance in real problem instances, we can relate characteristics of the best algorithms to characteristics of the application. In particular, we find that plateaus dominate the search spaces (thus favoring algorithms that make larger changes to solutions) and that some randomization in exploration is critical to good performance (due to the lack of gradient information on the plateaus). Based on our explanations of algorithm performance, we develop a new algorithm that combines characteristics of the best performers; the new algorithm's performance is better than the previous best. We show how hypothesis driven experimentation and search modeling can both explain algorithm performance and motivate the design of a new algorithm.
TL;DR: An automated scheduling tool is presented using mixed integer programming, and insertion and interchange heuristics that can be used to generate schedules comparable to current schedules considerably quicker than the current method.
Abstract: Satellite systems play a vital role in our national defense. Military satellites generally require frequent contact with remote tracking stations for the transmission and receipt of information required for mission accomplishment and continued satellite operations. Satellite Range Scheduling (SRS) is a complex problem which involves assigning requested satellite supports to time windows at remote tracking stations. This process is currently a manual process, assisted by computer graphics and error checking. This paper presents an automated scheduling tool we have developed using mixed integer programming, and insertion and interchange heuristics. Our testing shows that this approach can be used to generate schedules comparable to current schedules considerably quicker than the current method.
12 May 2008
TL;DR: In this article, the authors present an introduction to ASPEN, a more in-depth discussion on its use on the Orbital Express mission, and other relative work, along with the flexibility of ASPEN tool to accommodate changes to procedures and the daily or long-range plan, which contributed to the success of the mission.
Abstract: The Orbital Express space mission was a Defense Advanced Research Projects Agency (DARPA) lead demonstration of on-orbit satellite servicing scenarios, autonomous rendezvous, fluid transfers of hydrazine propellant, and robotic arm transfers of Orbital Replacement Unit (ORU) components. Boeing's Autonomous Space Transport Robotic Operations (ASTRO) vehicle provided the servicing to the Ball Aerospace's Next Generation Serviceable Satellite (NextSat) client. For communication opportunities, operations used the high-bandwidth ground-based Air Force Satellite Control Network (AFSCN) along with the relatively low-bandwidth GEO-Synchronous space-borne Tracking and Data Relay Satellite System (TDRSS) network. Mission operations were conducted out of the RDTE autonomous free-flyer capture was demonstrated on June 22, 2007; the fluid and ORU transfers throughout the mission were successful. Planning operations for the mission were conducted by a team of personnel including Flight Directors, who were responsible for verifying the steps and contacts within the procedures, the Rendezvous Planners who would compute the locations and visibilities of the spacecraft, the Scenario Resource Planners (SRPs), who were concerned with assignment of communications windows, monitoring of resources, and sending commands to the ASTRO spacecraft, and the Mission planners who would interface with the real-time operations environment, process planning products and coordinate activities with the SRP. The SRP position was staffed by JPL personnel who used the Automated Scheduling and Planning ENvironment (ASPEN) to model and enforce mission and satellite constraints. The lifecycle of a plan began three weeks outside its execution on-board. During the planning timeframe, many aspects could change the plan, causing the need for re-planning. These variable factors, ranging from shifting contact times to ground-station closures and required maintenance times, are discussed along with the flexibility of the ASPEN tool to accommodate changes to procedures and the daily or long-range plan, which contributed to the success of the mission. This paper will present an introduction to ASPEN, a more in-depth discussion on its use on the Orbital Express mission, and other relative work. A description of ground operations after the SRP deliveries were made is included, and we briefly discuss lessons learned from the planning perspective and future work.
01 Jan 2015-Rairo-operations Research
TL;DR: A generalized version of Consistent Neighborhood Search is proposed, its performance according to various criteria is discussed, and successful adaptations of CNS to three types of satellite range scheduling problems are presented.
Abstract: Many optimization problems require the use of a local search to find a satisfying solution in a reasonable amount of time, even if the optimality is not guaranteed. Usually, local search algorithms operate in a search space which contains complete solutions (feasible or not) to the problem. In contrast, in Consistent Neighborhood Search (CNS), after each variable assignment, the conflicting variables are deleted to keep the partial solution feasible, and the search can stop when all the variables have a value. In this paper, we propose a generalized version of CNS, discuss its performance according to various criteria, and present successful adaptations of CNS to three types of satellite range scheduling problems. Such problems are motivated by applications encountered by the French National Space and Aeronautic Agencies and the US Air Force Satellite Control Network. The described numerical experiments will demonstrate that CNS is a powerful and flexible method, which can be easily combined with efficient ingredients.
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