Comprehensive multi-objective model to remote sensing data processing task scheduling problem

Scientific scheduling of limited resource plays an important role in the remote sensing data processing. The remote sensing data processing task scheduling is characterized as one novel comprehensive multi-objective model. In this proposed model, the remote sensing data processing task scheduling problem is divided into task dispensation and task scheduling sub-problem with hundreds of variables being considered in it. In order to effectively solve this problem, Bayes belief model is applied to generate the initial dispensation plan, and learnable ant colony optimization is proposed to solve task scheduling sub-problem. Experimental results suggest that the proposed comprehensive multi-objective model and its solving methods are feasible and efficient to remote sensing data processing task scheduling, and it also promotes processing centers interoperability among heterogeneous and dispersed processing center. The model and the method of this paper can provide a valuable reference for solving other complex scheduling problem.