Satellite Range Scheduling: A Comparison of Genetic, Heuristic and Local Search
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Scheduling Space–Ground Communications for the Air Force Satellite Control Network
Journal of Scheduling
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
A multi-objective imaging scheduling approach for earth observing satellites
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Solving the swath segment selection problem through Lagrangean relaxation
Computers and Operations Research
Graph colouring approaches for a satellite range scheduling problem
Journal of Scheduling
Leap before you look: an effective strategy in an oversubscribed scheduling problem
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
A comparison of techniques for scheduling earth observing satellites
IAAI'04 Proceedings of the 16th conference on Innovative applications of artifical intelligence
Understanding algorithm performance on an oversubscribed scheduling application
Journal of Artificial Intelligence Research
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
Computational Optimization and Applications
A Lagrangian heuristic for satellite range scheduling with resource constraints
Computers and Operations Research
AFSCN scheduling: How the problem and solution have evolved
Mathematical and Computer Modelling: An International Journal
Computers and Operations Research
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This paper describes three approaches to assigning tasks to earth observing satellites (EOS). A fast and simple priority dispatch method is described and shown to produce acceptable schedules most of the time. A look ahead algorithm is then introduced that outperforms the dispatcher by about 12% with only a small increase in run time. These algorithms set the stage for the introduction of a genetic algorithm that uses job permutations as the population. The genetic approach presented here is novel in that it uses two additional binary variables, one to allow the dispatcher to occasionallyskip a job in the queue and another to allow the dispatcher to occasionally allocate theworst position to the job. These variables are included in the recombination step in a natural way. The resulting schedules improve on the look ahead by as much as 15% at times and 3% on average. We define and use the "window-constrained packing" problem to model the bare bones of the EOS scheduling problem.