Double layer programming model to the scheduling of remote sensing data processing tasks

Remotely sensed data are widely used in disaster and environment monitoring. To complete the tasks associated with processing these data, it is a practical and pressing problem to match the resources for these data with data processing centers in real or near-real time and complete as many tasks on time as possible. However, scheduling remotely sensed data processing tasks has two phases, namely, task assignment and task scheduling. This paper presents a model using bilevel optimization, which considers task assignment and task scheduling as a single problem. Using this architecture, a mathematical model for both levels of the problem is presented. To solve the mathematical model, this paper presents a cooperative coevolution algorithm that combines the advantages of a very fast simulated annealing algorithm with a learnable ant colony optimization algorithm. Finally, the effectiveness and feasibility of the proposed approach compared with the conventional method is demonstrated through empirical results.