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- W2901478340 abstract "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." @default.
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- W2901478340 date "2019-01-01" @default.
- W2901478340 modified "2023-09-27" @default.
- W2901478340 title "Double layer programming model to the scheduling of remote sensing data processing tasks" @default.
- W2901478340 doi "https://doi.org/10.3934/dcdss.2019104" @default.
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