Using STK Toolkit for Evaluating a GA Base Algorithm for Ground Station Scheduling

Abstract

The satellite scheduling and its version of ground station scheduling are increasingly attracting the attention of researchers from aerospace and optimization domain. While in the recent past satellite mission arise from large aero-spacial agencies, nowadays even smaller companies are interested in satellite missions for basic tasks such as telemetry, imaging, remote sensing, etc. The ground station scheduling problem consists in computing an optimal planning of communications between satellites or spacecraft (SC) and operations teams of Ground Station (GS). The problem is highly complex and multi-objective and in its general formulation has been shown NP-hard. Therefore, its resolution is tackled by heuristic and meta-heuristic methods. Although heuristic and meta-heuristic methods are well understood, their evaluation for specific problems, like ground station scheduling, remain a challenge. The design and development of benchmarks of instances is thus needful to evaluate such methods and also to provide the community with means to reproduce the experimental study for the same benchmark under the same or different parameter setting. In this paper, we present an XML-based benchmark of instances for the ground station scheduling generated with the STK simulation toolkit. Then we show the experimental evaluation of a Basic Genetic Algorithm using the benchmark.

Multi-objective approaches to ground station scheduling for optimization of communication with satellites

“…As is the case with most of the scheduling problems, this is a very complex problem and is proven to be NP-hard (Barbulescu et al 2004). As mentioned above, satellite scheduling problems require taking into consideration multiple objectives, making the GSS problem multi-objective in its general formulation (Xhafa et al 2013). In simultaneous optimization, multi-objective problems are solved by considering solutions at the edge of the approximate Pareto front.…”

“…The formulation of the problem and its objectives used in this paper are taken from Xhafa et al (2013), Xhafa and Ip (2019), along with the benchmark problems. The benchmark problems are of 3 different sizes.…”

“…The 4 objectives are aggregated in one, by the use of specific weights, providing one single solution after the optimization. In the reference paper (Xhafa et al 2013) the following weights were used:…”

“…Population size is one of the key components of optimization algorithms (Mora-Melià et al 2017). For a single objective population size of 30 was used as in the reference paper (Xhafa et al 2013). For multi-objective cases, a larger population is required.…”

Mission scheduling optimization of SAR satellite constellation for minimizing system response time

“…Hall and Magazine (1994) [10] performed a study on a single-satellite mission using dynamic programming based on the Heuristic method. Xhafa et al (2012) [20] investigated the optimal scheduling methodologies for single-satellite using Genetic Algorithm. Vasques and Hao (2001) [15] studied the optimal scheduling for the French Spot satellite.…”