ABSTRACT
The problem of earth observation satellites planning and scheduling contains many
objectives and constraints. The objectives include downloaded data, profit of
scheduled tasks and quality (weight) values. The constraints are related to energy,
data, targets (tasks) and ground stations. Many optimization methods have been
used to optimize this problem such as dynamic programming (DP), simulated
annealing (SA), ant colony optimization (ACO), genetic algorithm (GA) and constraint
programming approach (CPA). Each reported research used a harmonized
combination of objective(s), constrains, and optimization techniques. The presented
work investigates the anatomy of this optimization problem. It is also analyzes the
findings of the previous relation between the optimization problem elements and
gaps in past research. Furthermore shows that there is a gap between data's
objectives (maximizing the total amount of downloaded data) and the targets'
constraints (observation of tasks, scheduling and consecutive observation).The most
widely used optimization methods are dynamic programming and heuristic
algorithms, respectively. Moreover, the most widely objectives are those relative to
profit. Few discussions are also presented concerning the constraints which involve
data rate, range of ground stations and capacity of ground stations.