Scheduling Space–Ground Communications for the Air Force Satellite Control Network

  • Authors:

  • Laura Barbulescu;Jean-Paul Watson;L. Darrell Whitley;Adele E. Howe

  • Affiliations:

  • Colorado State University, Fort Collins, CO 80523-1873, USA/ E-mail: laura@cs.colostate.edu;Colorado State University, Fort Collins, CO 80523-1873, USA;Colorado State University, Fort Collins, CO 80523-1873, USA;Colorado State University, Fort Collins, CO 80523-1873, USA

  • Venue:
  • Journal of Scheduling
  • Year:
  • 2004

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Abstract

We present the first coupled formal and empirical analysis of the Satellite Range Scheduling application. We structure our study as a progression; we start by studying a simplified version of the problem in which only one resource is present. We show that the simplified version of the problem is equivalent to a well-known machine scheduling problem and use this result to prove that Satellite Range Scheduling is NP-complete. We also show that for the one-resource version of the problem, algorithms from the machine scheduling domain outperform a genetic algorithm previously identified as one of the best algorithms for Satellite Range Scheduling. Next, we investigate if these performance results generalize for the problem with multiple resources. We exploit two sources of data: actual request data from the U.S. Air Force Satellite Control Network (AFSCN) circa 1992 and data created by our problem generator, which is designed to produce problems similar to the ones currently solved by AFSCN. Three main results emerge from our empirical study of algorithm performance for multiple-resource problems. First, the performance results obtained for the single-resource version of the problem do not generalize: the algorithms from the machine scheduling domain perform poorly for the multiple-resource problems. Second, a simple heuristic is shown to perform well on the old problems from 1992; however it fails to scale to larger, more complex generated problems. Finally, a genetic algorithm is found to yield the best overall performance on the larger, more difficult problems produced by our generator.