Three Scheduling Algorithms Applied to the Earth Observing Systems Domain
介绍了三种地球观测卫星任务调度方法：优先级调度、前瞻算法和遗传算法，其中遗传算法通过引入两个额外二进制变量来优化调度，平均比前瞻算法提升3%性能。
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 occasionally skip a job in the queue and another to allow the dispatcher to occasionally allocate the worst 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.