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

A comparison study on meta-heuristics for ground station scheduling problem

In ground station scheduling problem the aim is to compute an optimal planning of communications between Spacecrafts (SCs) and operations teams of Ground Stations (GSs). While such allocation of tasks to ground stations traditionally is mostly done by human intervention, modern scheduling systems look at optimization and automation features. Such features, on the one hand, would increase the efficiency and productivity of the mission planning systems by handling a larger number of missions, achieve a higher usage of the infrastructure (grand stations' antennae) and, on the other, would avoid error-prone human allocation and reduce human labour costs. Designing such modern, automated scheduling/planning systems is however challenging due to the highly constraint and complex nature of the problem seeking to optimize along various objectives or system parameters. In this paper we present a study on the performance of several meta-heuristics methods for solving ground station scheduling problem. Local search methods (Hill Climbing, Simulated Annealing and Tabu Search) and population-based methods (GA, Steady State GA and Struggle GA) have been considered for the study. The performance of these resolution methods was measured by a set of instances of varying size and complexity generated by STK toolkit. The study revealed the strengths and weaknesses of the considered methods while solving different size instances and considering several objective functions, namely, windows fitness, clashes fitness, time requirement fitness, and resource usage fitness.Peer ReviewedPostprint (author's final draft

Optimisation problems and resolution methods in satellite scheduling and space-craft operation: a survey

The fast development in the production of small, low-cost satellites is propelling an important increase in satellite mission planning and operations projects. Central to satellite mission planning is the resolution of scheduling problem for an optimised allocation of user requests for efficient communication between operations teams at the ground and spacecraft systems. The aim of this paper is to survey the state of the art in the satellite scheduling problem, analyse its mathematical formulations, examine its multi-objective nature and resolution through meta-heuristics methods. Finally, we consider some optimisation problems arising in spacecraft design, operation and satellite deployment systemsPeer ReviewedPostprint (author's final draft

Resolution of an Antenna–Satellite assignment problem by means of Integer Linear Programming

Every day, ground stations need to manage numerous requests for allocation of antenna time slots by customers operating satellites. For multi-antenna, multi-site ground networks serving numerous satellite operators, oftentimes these requests yield conflicts, which arise when two or more satellites request overlapping time slots on the same antenna. Deconflicting is performed by moving passes to other antennas, shortening their duration, or canceling them, and has frequently been done manually. However, when many conflicts are present, deconflicting becomes a complex and time-consuming when done manually. We propose an automated tool that solves the problem by means of Integer Linear Programming. The models include operational constraints and mimic the manual process but consider the problem globally, thus being able to improve the quality of the solution. A simplified shortening model is also included to avoid excessive computation times, which is crucial given that the general problem has been reported NP-complete. Priorities are taken into account by tuning the cost function according to specifications of the requesting clients. Experiments with real-data scenarios using open-source software show that our tool is able to solve the Antenna Satellite assignment problem for a large number of passes in a short amount of time, thus enormously improving manual scheduling operations, even when performed by a skilled operator.The authors gratefully acknowledge the cooperation of Taitus Software (http://www.taitussoftware.com) and its team, and in particular its founder and CEO (Felipe Martin Crespo), which introduced this problem to us and provided integration with its orbital mechanics visual software SaVoir. We also acknowledge the cooperation of Kongsberg Satellite Services AS (KSAT). Jorge Galan acknowledges financial support through grants MTM2012-31821 and P12-FQM-1658. Federico Perea acknowledges financial support through grants FQM-5849 and P09-TEP-5022 (Junta de Andalucia and FEDER) and MTM2010-19576-C02-01 and MTM2013-46962-C2-1 (MICINN, Spain). Special thanks are due to three anonymous referees for their valuable comments.Vazquez, R.; Perea Rojas Marcos, F.; Galán Vioque, J. (2014). Resolution of an Antenna–Satellite assignment problem by means of Integer Linear Programming. Aerospace Science and Technology. 39:567-574. doi:10.1016/j.ast.2014.06.002S5675743

Ant colony optimization with immigrants schemes for the dynamic railway junction rescheduling problem with multiple delays

Train rescheduling after a perturbation is a challenging task and is an important concern of the railway industry as delayed trains can lead to large fines, disgruntled customers and loss of revenue. Sometimes not just one delay but several unrelated delays can occur in a short space of time which makes the problem even more challenging. In addition, the problem is a dynamic one that changes over time for, as trains are waiting to be rescheduled at the junction, more timetabled trains will be arriving, which will change the nature of the problem. The aim of this research is to investigate the application of several different ant colony optimization (ACO) algorithms to the problem of a dynamic train delay scenario with multiple delays. The algorithms not only resequence the trains at the junction but also resequence the trains at the stations, which is considered to be a first step towards expanding the problem to consider a larger area of the railway network. The results show that, in this dynamic rescheduling problem, ACO algorithms with a memory cope with dynamic changes better than an ACO algorithm that uses only pheromone evaporation to remove redundant pheromone trails. In addition, it has been shown that if the ant solutions in memory become irreparably infeasible it is possible to replace them with elite immigrants, based on the best-so-far ant, and still obtain a good performance

Structured-chromosome GA optimisation for satellite tracking

This paper presents a novel optimisation approach, called Structured-Chromosome Genetic Algorithm (SCGA), that addresses the issue of handling variable-size design space optimisation problems. This is based on variants of standard genetic operators able to handle structured search spaces. The potential of the presented methodology is shown by solving the problem of defining observation campaigns for tracking space objects from a network of tracking stations. The presented approach aims at supporting the space sector in response to the constantly increasing population size in the around-Earth environment. The test case consists in finding the observation scheduling that minimises the uncertainty in the final state estimation of a very low Earth satellite operating in a highly perturbed dynamical environment. This is evaluated by coupling the optimiser with an estimation routine based on a sequential filtering approach that estimates the satellite state distribution conditional on received indirect measurements. The solutions found by employing SCGA are finally compared to the ones achieved using more traditional approaches. Namely, the problem has been reformulated to be faced using standard Genetic Algorithm and another variable-size optimiser, the "Hidden-genes" Genetic Algorithm variant

Trade-off between optimal design and operation in district cooling networks

Especially in densely populated areas, district cooling represents an opportunity to reduce energy consumption and emissions. Nevertheless, this technology is characterised by large capital costs which impede its diffusion. As a consequence, optimization tools can significantly help to unleash their potential. In this paper, a methodology is proposed to combinedly optimize the design and operation of a district cooling system based on a Mixed Integer Quadratic Programming. The model is compared to the design only optimization, based on a properly tailored heuristic approach. The models, when applied to a case study characterized by seasonal demand, provide similar solutions, which differ by 0.5 % in terms of objective value for a standard scenario. The simultaneous design and operation optimization does not provide sensible savings with respect to optimizing solely the design. A sensitivity analysis is performed to prove the robustness of the results. The results showed that the simulta- neous operation and design optimization would be limited to 1 % of total costs in the case of seasonal cooling demand. On the other hand, if the cooling demand persists throughout the year, as in tropical climates, the combined optimization provides significant benefits, since these savings reach 4.7 % of total costs

Resolució de "Ground-Station Scheduling" amb mètodes heurístics

Adjunto la memòria i el codi de la pràctica. Atès que el codi compilat ocupa masses megues com per adjuntar-lo, l'envio descompilat. Aquest està preparat per córrer en entorns linux. No es necessiten llibreries adicionals per compilar-lo, simlement un compilador de C++ com el G++

A comparative analysis of algorithms for satellite operations scheduling

Scheduling is employed in everyday life, ranging from meetings to manufacturing and operations among other activities. One instance of scheduling in a complex real-life setting is space mission operations scheduling, i.e. instructing a satellite to perform fitting tasks during predefined time periods with a varied frequency to achieve its mission goals. Mission operations scheduling is pivotal to the success of any space mission, choreographing every task carefully, accounting for technological and environmental limitations and constraints along with mission goals.;It remains standard practice to this day, to generate operations schedules manually ,i.e. to collect requirements from individual stakeholders, collate them into a timeline, compare against feasibility and available satellite resources, and find potential conflicts. Conflict resolution is done by hand, checked by a simulator and uplinked to the satellite weekly. This process is time consuming, bears risks and can be considered sub-optimal.;A pertinent question arises: can we automate the process of satellite mission operations scheduling? And if we can, what method should be used to generate the schedules? In an attempt to address this question, a comparison of algorithms was deemed suitable in order to explore their suitability for this particular application.;The problem of mission operations scheduling was initially studied through literature and numerous interviews with experts. A framework was developed to approximate a generic Low Earth Orbit satellite, its environment and its mission requirements. Optimisation algorithms were chosen from different categories such as single-point stochastic without memory (Simulated Annealing, Random Search), multi-point stochastic with memory (Genetic Algorithm, Ant Colony System, Differential Evolution) and were run both with and without Local Search.;The aforementioned algorithmic set was initially tuned using a single 89-minute Low Earth Orbit of a scientific mission to Mars. It was then applied to scheduling operations during one high altitude Low Earth Orbit (2.4hrs) of an experimental mission.;It was then applied to a realistic test-case inspired by the European Space Agency PROBA-2 mission, comprising a 1 day schedule and subsequently a 7 day schedule - equal to a Short Term Plan as defined by the European Space Agency.;The schedule fitness - corresponding to the Hamming distance between mission requirements and generated schedule - are presented along with the execution time of each run. Algorithmic performance is discussed and put at the disposal of mission operations experts for consideration.Scheduling is employed in everyday life, ranging from meetings to manufacturing and operations among other activities. One instance of scheduling in a complex real-life setting is space mission operations scheduling, i.e. instructing a satellite to perform fitting tasks during predefined time periods with a varied frequency to achieve its mission goals. Mission operations scheduling is pivotal to the success of any space mission, choreographing every task carefully, accounting for technological and environmental limitations and constraints along with mission goals.;It remains standard practice to this day, to generate operations schedules manually ,i.e. to collect requirements from individual stakeholders, collate them into a timeline, compare against feasibility and available satellite resources, and find potential conflicts. Conflict resolution is done by hand, checked by a simulator and uplinked to the satellite weekly. This process is time consuming, bears risks and can be considered sub-optimal.;A pertinent question arises: can we automate the process of satellite mission operations scheduling? And if we can, what method should be used to generate the schedules? In an attempt to address this question, a comparison of algorithms was deemed suitable in order to explore their suitability for this particular application.;The problem of mission operations scheduling was initially studied through literature and numerous interviews with experts. A framework was developed to approximate a generic Low Earth Orbit satellite, its environment and its mission requirements. Optimisation algorithms were chosen from different categories such as single-point stochastic without memory (Simulated Annealing, Random Search), multi-point stochastic with memory (Genetic Algorithm, Ant Colony System, Differential Evolution) and were run both with and without Local Search.;The aforementioned algorithmic set was initially tuned using a single 89-minute Low Earth Orbit of a scientific mission to Mars. It was then applied to scheduling operations during one high altitude Low Earth Orbit (2.4hrs) of an experimental mission.;It was then applied to a realistic test-case inspired by the European Space Agency PROBA-2 mission, comprising a 1 day schedule and subsequently a 7 day schedule - equal to a Short Term Plan as defined by the European Space Agency.;The schedule fitness - corresponding to the Hamming distance between mission requirements and generated schedule - are presented along with the execution time of each run. Algorithmic performance is discussed and put at the disposal of mission operations experts for consideration