This thesis considers the selection and scheduling problem of observations for agile Earth observing satellites. The mission of Earth observing satellites is to obtain photographs of the Earth surface to satisfy user requirements. Requests from several users have to be managed before transmitting an order, which is a sequence of selected acquisitions, to the satellite. The obtained sequence must optimize two objectives under operation constraints. The first objective is to maximize the total profit of the selected acquisitions. The second one is to ensure the fairness of resource sharing by minimizing the maximum profit difference between users. Two metaheuristic algorithms, consisting of a biased random key genetic algorithm (BRKGA) and an indicator-based multi-objective local search (IBMOLS), are proposed to solve the problem. For BRKGA, three selection methods, borrowed from NSGA-II, SMS-EMOA, and IBEA, are proposed to select a set of preferred chromosomes to be the elite set. Three decoding strategies, which are two single decoding and a hybrid decoding, are applied to decode chromosomes to become solutions. For IBMOLS, several methods for generating the initial population are tested and the neighborhood structure according to the problem is also proposed. Experiments are conducted on realistic instances based on ROADEF 2003 challenge instances. Hypervolumes of the approximate Pareto fronts are computed and the results from the two algorithms are compared.
Optimisation multi-objectif de missions de satellites d'observation de la Terre