Research Article Heuristic Scheduling Algorithm Oriented Dynamic Tasks for Imaging Satellites Maocai Wang, 1,2 Guangming Dai, 1 and Massimiliano Vasile 2 1 School of Computer, China University of Geosciences, Wuhan 430074, China 2 Department of Mechanical and Aerospace Engineering, University of Strathclyde, Glasgow G1 1XJ, UK Correspondence should be addressed to Maocai Wang; cugwmc@gmail.com Received 4 March 2014; Revised 13 June 2014; Accepted 13 June 2014; Published 17 July 2014 Academic Editor: Yi Chen Copyright © 2014 Maocai Wang et al. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Imaging satellite scheduling is an NP-hard problem with many complex constraints. Tis paper researches the scheduling problem for dynamic tasks oriented to some emergency cases. Afer the dynamic properties of satellite scheduling were analyzed, the optimization model is proposed in this paper. Based on the model, two heuristic algorithms are proposed to solve the problem. Te frst heuristic algorithm arranges new tasks by inserting or deleting them, then inserting them repeatedly according to the priority from low to high, which is named IDI algorithm. Te second one called ISDR adopts four steps: insert directly, insert by shifing, insert by deleting, and reinsert the tasks deleted. Moreover, two heuristic factors, congestion degree of a time window and the overlapping degree of a task, are employed to improve the algorithm’s performance. Finally, a case is given to test the algorithms. Te results show that the IDI algorithm is better than ISDR from the running time point of view while ISDR algorithm with heuristic factors is more efective with regard to algorithm performance. Moreover, the results also show that our method has good performance for the larger size of the dynamic tasks in comparison with the other two methods. 1. Introduction Because earth observation satellites (EOS) have many fea- tures such as wide coverage area and long duration and are without boundaries limitation, they have become an impor- tant means for exploring and researching earth resources and have been widely used in the felds such as land survey- ing, vegetation classifcation, crop growth trend assessment, natural disaster monitoring, and management of large-scale infrastructure projects as well as battlefeld reconnaissance and ground military target identifcation. Mission planning plays a key role in the whole process of earth observation. It directly afects the result of task completion. With the increase of the types of on-orbit satellites, as well as the increasingly complex requirements for observation data, how to optimize the scheduling of satellite resources to meet all types of observational requests has presented new challenges for satellite mission planning. Satellite scheduling is to allocate the observation resources and executing time to a series of imaging tasks. In the recent years, many researchers have focused on diferent types of scheduling problems for EOS. For example, Parish [1] adopted the genetic algorithm to schedule as many supports as possible by a schedule builder program for 24-hour satellite range schedules. Wolfe and Sorensen [2] described the priority dispatch algorithm and the look ahead algorithm and then presented a novel genetic algorithm with two additional binary variables. Vasquez and Hao [3] formalized the daily photograph scheduling problem of EOS as a generalized version of the well-known knapsack model and developed a tabu search algorithm to solve the problem. Vasquez and Hao [4] also designed a partition-based approach to get the tight upper bounds for the daily photograph scheduling problem of EOS, and then a simplex-based linear programming relaxation and a relaxed knapsack approach were presented to solve the problem. In addition, Barbulescu et al. [5] compared simple heuristic method, local search method, and genetic algorithm and showed that the genetic algorithm had the best performance in the three algorithms for the larger and more difcult Hindawi Publishing Corporation Mathematical Problems in Engineering Volume 2014, Article ID 234928, 11 pages http://dx.doi.org/10.1155/2014/234928