station1 | station2 | ||||
PC1 | PC2 | PC3 | PC1 | PC2 | PC3 |
3420 | 3568 | 2954 | 2486 | 1471 | 1770 |
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.
Citation: |
Table 1. Distances between stations and processing
station1 | station2 | ||||
PC1 | PC2 | PC3 | PC1 | PC2 | PC3 |
3420 | 3568 | 2954 | 2486 | 1471 | 1770 |
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RSDPTCS module using bi-level programming
Cooperative coevolution algorithm
Maximum computation time comparison
Average resource node load comparison
Late completion comparison
Processing time comparison