A Dynamic Scheduling Method of Earth-Observing Satellites by Employing Rolling Horizon Strategy

Focused on the dynamic scheduling problem for earth-observing satellites (EOS), an integer programming model is constructed after analyzing the main constraints. The rolling horizon (RH) strategy is proposed according to the independent arriving time and deadline of the imaging tasks. This strategy is designed with a mixed triggering mode composed of periodical triggering and event triggering, and the scheduling horizon is decomposed into a series of static scheduling intervals. By optimizing the scheduling schemes in each interval, the dynamic scheduling of EOS is realized. We also propose three dynamic scheduling algorithms by the combination of the RH strategy and various heuristic algorithms. Finally, the scheduling results of different algorithms are compared and the presented methods in this paper are demonstrated to be efficient by extensive experiments

Imaging Tasks Scheduling for High-Altitude Airship in Emergency Condition Based on Energy-Aware Strategy

Aiming to the imaging tasks scheduling problem on high-altitude airship in emergency condition, the programming models are constructed by analyzing the main constraints, which take the maximum task benefit and the minimum energy consumption as two optimization objectives. Firstly, the hierarchy architecture is adopted to convert this scheduling problem into three subproblems, that is, the task ranking, value task detecting, and energy conservation optimization. Then, the algorithms are designed for the sub-problems, and the solving results are corresponding to feasible solution, efficient solution, and optimization solution of original problem, respectively. This paper makes detailed introduction to the energy-aware optimization strategy, which can rationally adjust airship’s cruising speed based on the distribution of task’s deadline, so as to decrease the total energy consumption caused by cruising activities. Finally, the application results and comparison analysis show that the proposed strategy and algorithm are effective and feasible

A pure proactive scheduling algorithm for multiple earth observation satellites under uncertainties of clouds

Most earth observation satellites (EOSs) are equipped with optical sensors, which cannot see through the clouds. Hence, observations are significantly affected and blocked by clouds. In this work, with the inspiration of the notion of a forbidden sequence, we propose a novel assignment formulation for EOS scheduling. Considering the uncertainties of clouds, we formulate the cloud coverage for observations as stochastic events, and extend the assignment formulation to a chance constraint programming (CCP) model. To solve the problem, we suggest a sample approximation (SA) method, which transforms the CCP model into an integer linear programming (ILP) model. Subsequently, a branch and cut (B&C) algorithm based on lazy constraint generation is developed to solve the ILP model. Finally, we conduct a lot of simulation experiments to verify the effectiveness and efficiency of our proposed formulation and algorithm.status: publishe

Cooperative Scheduling of Imaging Observation Tasks for High-Altitude Airships Based on Propagation Algorithm

The cooperative scheduling problem on high-altitude airships for imaging observation tasks is discussed. A constraint programming model is established by analyzing the main constraints, which takes the maximum task benefit and the minimum cruising distance as two optimization objectives. The cooperative scheduling problem of high-altitude airships is converted into a main problem and a subproblem by adopting hierarchy architecture. The solution to the main problem can construct the preliminary matching between tasks and observation resource in order to reduce the search space of the original problem. Furthermore, the solution to the sub-problem can detect the key nodes that each airship needs to fly through in sequence, so as to get the cruising path. Firstly, the task set is divided by using k-core neighborhood growth cluster algorithm (K-NGCA). Then, a novel swarm intelligence algorithm named propagation algorithm (PA) is combined with the key node search algorithm (KNSA) to optimize the cruising path of each airship and determine the execution time interval of each task. Meanwhile, this paper also provides the realization approach of the above algorithm and especially makes a detailed introduction on the encoding rules, search models, and propagation mechanism of the PA. Finally, the application results and comparison analysis show the proposed models and algorithms are effective and feasible

A pure proactive scheduling algorithm for multiple earth observation satellites under uncertainties of clouds

Most earth observation satellites (EOSs) are equipped with optical sensors, which cannot see through the clouds. Hence, observations are significantly affected and blocked by clouds. In this work, with the inspiration of the notion of a forbidden sequence, we propose a novel assignment formulation for EOS scheduling. Considering the uncertainties of clouds, we formulate the cloud coverage for observations as stochastic events, and extend the assignment formulation to a chance constraint programming (CCP) model. To solve the problem, we suggest a sample approximation (SA) method, which transforms the CCP model into an integer linear programming (ILP) model. Subsequently, a branch and cut (B&C) algorithm based on lazy constraint generation is developed to solve the ILP model. Finally, we conduct a lot of simulation experiments to verify the effectiveness and efficiency of our proposed formulation and algorithm.nrpages: 28status: publishe

Proactive scheduling algorithms for multiple earth observation satellites under uncertainties of clouds

This paper investigates the scheduling of multiple earth observation satellites (EOSs) under uncertainties of clouds. Firstly, we formulate the presence of clouds as stochastic events, transforming the problem into a stochastic programming problem. Based on different perspectives, we model the problem mathematically using both an expectation model and a chance constrained programming (CCP) model. Afterwards, for the first time, we employ a Dantzig-Wolfe decomposition and a column generation technique for the uncertain scheduling of EOSs. With respect to the expectation model, we devise a branch-and-price algorithm to solve the model optimally and efficiently. On the other hand, we first reformulate the CCP model as a mixed integer programming (MIP) model using sample approximation. Subsequently, considering the difficulties and the infeasibility of the branch-and-price algorithm for this MIP model, we suggest a column generation based heuristic algorithm to get \good" feasible solutions. By numerous simulation experiments, we verify the effectiveness and test the performance of our proposed formulations and approaches.nrpages: 25status: publishe

Exact and inexact scheduling algorithms for multiple earth observation satellites under uncertainties of clouds

Most earth observation satellites (EOSs) are equipped with optical sensors, which cannot see through clouds. Hence, many observations will be useless due to the presence of clouds. In this work, in order to improve the possibility of completing the tasks under uncertainties of clouds, we take the scheduling of each task to multiple resources into account and establish a novel non-linear mathematical model. To solve the problem efficiently under different scenarios, we propose an exact algorithm and some heuristic algorithms. With respect to the exact algorithm, we divide the complicated problem into a master problem and multiple subproblems, with a subproblem for each resource. A labeling-based dynamic programming algorithm is proposed to solve each subproblem. Afterwards, based on the solutions of the subproblems, we develop an enumeration algorithm to solve the master problem. Furthermore, we design five heuristics to solve the large-scale problems that generally fail to be solved by the exact algorithm due to the large space complexity. Experimental results show that the solutions of our model perform better than those of previous studies, and we also reveal the strengths and weaknesses of the proposed algorithms while solving different size instances.nrpages: 30status: publishe