Future Generation Computer Systems
Computational Optimization and Applications
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Three Scheduling Algorithms Applied to the Earth Observing Systems Domain
Management Science
Ant Colony Optimization
A multi-objective imaging scheduling approach for earth observing satellites
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Ant colony optimization combined with taboo search for the job shop scheduling problem
Computers and Operations Research
Ant colony optimization for the two-dimensional loading vehicle routing problem
Computers and Operations Research
A comparison of techniques for scheduling earth observing satellites
IAAI'04 Proceedings of the 16th conference on Innovative applications of artifical intelligence
Computers and Industrial Engineering
Computational Optimization and Applications
Strong formulation for the spot 5 daily photograph scheduling problem
Journal of Combinatorial Optimization
Russian doll search for solving constraint optimization problems
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Computers and Industrial Engineering
Imaging Order Scheduling of an Earth Observation Satellite
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Daily imaging scheduling of an Earth observation satellite
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Automated Synthesis of Data Paths in Digital Systems
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Hi-index | 0.01 |
Satellite observation scheduling plays a significant role in improving the efficiency of satellite observation systems. Although extensive scheduling algorithms have been proposed for the satellite observation scheduling problem (SOSP), the task clustering strategy has not been taken into account up to now. This paper presents a novel two-phase based scheduling method with the consideration of task clustering for solving SOSP. This method comprises two phases: a task clustering phase and a task scheduling phase. In the task clustering phase, we construct a task clustering graph model and use an improved minimum clique partition algorithm to obtain cluster-tasks. In the task scheduling phase, based on overall tasks and obtained cluster-tasks, we construct an acyclic directed graph model and utilize a hybrid ant colony optimization coming with a mechanism of local search, called ACO-LS, to produce optimal or near optimal schedules. Extensive experimental simulations demonstrate the efficiency of the proposed scheduling method.