Genetic algorithms for satellite scheduling problems

Recently there has been a growing interest in mission operations scheduling problem. The problem, in a variety of formulations, arises in management of satellite/space missions requiring efficient allocation of user requests to make possible the communication between operations teams and spacecraft systems. Not only large space agencies, such as ESA (European Space Agency) and NASA, but also smaller research institutions and universities can establish nowadays their satellite mission, and thus need intelligent systems to automate the allocation of ground station services to space missions. In this paper, we present some relevant formulations of the satellite scheduling viewed as a family of problems and identify various forms of optimization objectives. The main complexities, due highly constrained nature, windows accessibility and visibility, multi-objectives and conflicting objectives are examined. Then, we discuss the resolution of the problem through different heuristic methods. In particular, we focus on the version of ground station scheduling, for which we present computational results obtained with Genetic Algorithms using the STK simulation toolkit.Peer ReviewedPostprint (published version

Remote Sensing Satellites Planning System

A Remote Sensing Satellites Planning system (RSSP) for satellite constellations is responsible for managing these satellites by assigning the imaging tasks to each satellite in the constellation such that the loads are balanced and the resources are well used. The proposed system can be used with heterogeneous constellations that consist of satellites whose different specifications, different orbits' types and/or different payload types. This problem is a combinatorial optimization NP-hard problem modeled in this paper as a Constraint Satisfaction Problem using the Constraint Programming Technique. The output plan is obtained using one of three objective functions (gain maximization, area maximization, and image quality maximization) using four search algorithms (simulated annealing, hill climbing, tabu search and late acceptance) and different planning horizons (one track, one day and one month)