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

摘要

Scientific scheduling of limited resource plays an important role in the remote sensing datarnprocessing. The remote sensing data processing task scheduling is characterized as one novelrncomprehensive multi‐objective model. In this proposed model, the remote sensing data processingrntask scheduling problem is divided into task dispensation and task scheduling sub‐problemrnwith hundreds of variables being considered in it. In order to effectively solve this problem, Bayesrnbelief model is applied to generate the initial dispensation plan, and learnable ant colonyrnoptimization is proposed to solve task scheduling sub‐problem. Experimental results suggest thatrnthe proposed comprehensive multi‐objective model and its solving methods are feasible andrnefficient to remote sensing data processing task scheduling, and it also promotes processingrncenters interoperability among heterogeneous and dispersed processing center. The model andrnthe method of this paper can provide a valuable reference for solving other complex schedulingrnproblem.