Genetic Algorithms for Satellite Scheduling Problems (original) (raw)
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A Constraint-Based Approach to Satellite Scheduling
2001
Satellite scheduling, like all scheduling, is the problem of mapping tasks (observation, communication, downlink, control maneuvers, etc.) to resources (sensor satellites, relay satellites, ground stations, etc.). Through our work on satellite scheduling problems, we have encountered many different constraints that are particular to the satellite-scheduling domain. In this paper, we will introduce the satellite mission-operation scheduling problem, describing the problem constraints that are particular to satellite scheduling, and then present the constraint-based techniques that we have used to address these problems.
In this paper, we propose a method that uses a genetic algorithm for the dynamic schedule optimization of imaging missions for multiple satellites and ground systems. In particular, the visibility conflicts of communication and mission operation using satellite resources (electric power and onboard memory) are integrated in sequence. Resource consumption and restoration are considered in the optimization process. Image acquisition is an essential part of satellite missions and is performed via a series of subtasks such as command uplink, image capturing, image storing, and image downlink. An objective function for optimization is designed to maximize the usability by considering the following components: user-assigned priority, resource consumption, and image-acquisition time. For the simulation, a series of hypothetical imaging missions are allocated to a multisatellite control system comprising five satellites and three ground stations having S-and X-band antennas. To demonstrate the performance of the proposed method, simulations are performed via three operation modes: general, commercial, and tactical.
A Web Interface for Satellite Scheduling Problems
2016 IEEE 30th International Conference on Advanced Information Networking and Applications (AINA), 2016
Mission planning plays an important role in satellite control systems, especially with increase of number of satellites and more complex missions to be planned. In a general setting, the satellite mission scheduling consists in allocating tasks such as observation, communication, etc. to resources (spacecrafts (SCs), satellites, ground stations). For instance, in ground station scheduling the aim is to compute an optimal planning of communications between satellites and operations teams of Ground Station (GS). Because the communication between SCs and GSs can be done during specific window times, this problem can also be seen as a window time scheduling problem. The required communication time is usually quite smaller than the window of visibility of SCs to GSs, however, clashes are produced, making the problem highly constrained. In this work we present a web interface for solving satellite scheduling problems through various heuristic methods. The web interface enables the users to remotely solve their problem instances through a selection of heuristic methods such as local search methods (Hill Climbing, Simulated Annealing and Tabu Search) and population-based methods (Genetic Algorithms and variants). The user can select to solve previously generated instances by the STK simulation toolkit or generate their own problem instances. The heuristic methods are easily configurable so that users can simulate a variety of scenarios, problem sizes, etc. The execution of the heuristics methods is done at a HPC Cluster infrastructure supporting efficient execution of various solvers. Additionally, the web application allows users to keep track of their executions as well as to share problem instances with other users.
Multi-objective optimization for multi-satellite scheduling task
Journal of Soft Computing Exploration
The satellites scheduling mission play an effective role in enhancing the role of ground station control and monitoring systems. In this search, SGSEO is re-formulated into a multi-objective optimization task. Therefore, the Gravitational Search Algorithm GSA is exploited to attain several essential objectives for generating tight scheduling. Moreover, particle swarm optimization model PSO is consolidated with GSA in a novel form for strengthening its ability of local search and slow the speed of convergence. On the other side, to make the most of the satellite resources in the right direction, we have observed targets that have fewer observational opportunities to keep them from being lost. The PageRank algorithm is used to fulfil this issue by ranking the candidate's strips. Finally, the effect of different parameters of the proposed approach was studied by experimental outcomes and compared with previous methods. It has shown that the performance of the proposed approach is s...
Aerospace Science and Technology, 2013
Resources scheduling in Low Earth Orbit (LEO) satellites is an important optimization problem because of the satellites' specific constraints. This article addresses a scheduling problem for LEO satellites missions to assign resources which could be satellites or ground stations to the most number of requested tasks by considering the tasks' priority and satisfying temporal and resource constraints. In this study, first, the scheduling problem is modeled using the graph coloring theory. Then, a new tabu search (TS) algorithm is applied to solve the problem. The proposed algorithm employs a new move function to enhance the exploration ability. Accordingly, an attempt is made to compare the result of the proposed TS with some well-known optimization algorithms. The computational results denote the efficiency of the proposed algorithm, as well as its ability to find schedules that are guaranteed to be near-optimal.
A new hybrid genetic algorithm for the collection scheduling problem for a satellite constellation
Journal of the Operational Research Society, 2019
Many heuristics and meta-heuristics problem-solving methods have been proposed so far to solve the NP-hard multi-satellite collection scheduling problem (m-SatCSP). In particular, genetic algorithms (GAs), well-suited for large scale problems, its simplicity and low cost implementation have been pervasive. However, most contributions largely emphasise simple variant or basic GA principles promotion, overlooking prior problem structure exploitation or potential problem-solving benefits that may be conveyed from similar combinatorial optimisation problems such as the vehicle routing problem with time windows (VRPTW). In fact, despite some recognised similarity with VRPTW and early investigation on limited exact methods, few efforts have been successfully reported to adapt efficient advanced specialpurpose problem-solving techniques to m-SatCSP. In this paper, a VRPTW-based hybrid genetic algorithm is proposed to tackle the single objective static m-SatCSP. The advocated approach combines and adapts well-known routing heuristics knowledge with standard genetic operator principles to efficiently explore promising search regions, manage constraint handling and improve solution quality. The hybrid strategy co-evolves two populations of solution plan individuals, maximising expected collection value while concurrently densifying collection paths to minimise orbit demand. Computational results show the approach to be cost-effective and competitive in comparison to some recent methods inspired from the best reported m-SatCSP heuristics.
Evaluation of struggle strategy in Genetic Algorithms for ground stations scheduling problem
Journal of Computer and System Sciences, 2013
Ground station scheduling problem arises in spacecraft operations and aims to allocate ground stations to spacecraft to make possible the communication between operations teams and spacecraft systems. The problem belongs to the family of satellite scheduling for the specific case of mapping communications to ground stations. The allocation of a ground station to a mission (e.g. telemetry, tracking information, etc.) has a high cost, and automation of the process provides many benefits not only in terms of management, but in economic terms as well. The problem is known for its high complexity as it is an over-constrained problem. In this paper, we present the resolution of the problem through Struggle Genetic Algorithms -a version of GAs that distinguishes for its efficiency in maintaining the diversity of the population during genetic evolution. We present some computational results obtained with Struggle GA using the STK simulation toolkit, which showed the efficiency of the method in solving the problem.
Optimization and Engineering
The ground station scheduling problem is a complex scheduling problem involving multiple objectives. Evolutionary techniques for multi-objective optimization are becoming popular among different fields, due to their effectiveness in obtaining a set of trade-off solutions. In contrast to some conventional methods, that aggregate the objectives into one weighted-sum objective function, multi-objective evolutionary algorithms manage to find a set of solutions in the Pareto-optimal front. Selecting one algorithm, however, for a specific problem adds additional challenge. In this paper the ground station scheduling problem was solved through six different evolutionary multi-objective algorithms, the NSGA-II, NSGA-III, SPEA2, GDE3, IBEA, and MOEA/D. The goal is to test their efficacy and performance to a number of benchmark static instances of the ground scheduling problem. Benchmark instances are of different sizes, allowing further testing of the behavior of the algorithms to different ...
Mission planning and scheduling for Earth observation space system
Mission planning and scheduling for Earth observation space system, 2020
Planning and scheduling systems are needed to manage Earth-observing satellites for satisfying the optimum usage of the constellation's resources. This is a combinatorial optimization NP-hard problem that is solved in this paper using the constraint programming technique. The proposed system can deal with a heterogeneous constellation that consists of satellites with different maneuverability, placed in different orbits, and loaded with different payloads. The system's user can choose one of six optimization objectives, three of them were not used before, for constructing the satellites' mission plan. Searching within the system is performed using one of five different search algorithms. The system produces plans with different planning horizons ranging from one track to more than one month. The obtained results depict that the proposed system behaves, comparatively, in a perfect manner even when dealing with a complicated case study consisting of three satellites, 2,500 targets, and a one-month planning horizon.
A Heuristic Algorithm for the Resource Assignment Problem in Satellite Telecommunication Networks
This paper proposes a heuristic algorithm for solving the scheduling of capacity requests and the periodic assignment of radio re-sources in a geostationary satellite network with a star topology. The network uses the Demand Assigned Multiple Access protocol in the link layer, and the Multi-Frequency Time Division Multiple Access (MF-TDMA) as well as the Adaptive Coding and Modulation protocols in the physical layer. The proposed algorithm allows processing a given traffic profile with message expiration time as delay constrains and a maximum packet-loss rate. The processing is completed using the minimum possible spectrum bandwidth. When there is not any structure imposed to the MF-TDMA super-frame, the resource-assignment problem becomes a combinatorial optimization problem which can be seen as a two-dimension (2D) oriented strip packing problem with additional constrains. The well-known Best Fit Decreasing heuristic for 2D packing is used as a basis for the proposed allocation al...