Quantum bridge analytics I: a tutorial on formulating and using QUBO models

Agile satellites with advanced attitude maneuvering capability are the new generation of earth observation satellites (EOSs). The continuous improvement in satellite technology and decrease in launch cost have boosted the development of agile EOSs (AEOSs). To efficiently employ the increasing orbiting AEOSs, the AEOS scheduling problem (AEOSSP) aiming to maximize the entire observation profit while satisfying all complex operational constraints, has received much attention over the past 20 years. The objectives of this article are, thus, to summarize current research on AEOSSP, identify main accomplishments and highlight potential future research directions. To this end, general definitions of AEOSSP with operational constraints are described initially, followed by its three typical variations including different definitions of observation profit, multiobjective function and autonomous model. A detailed literature review from 1997–2019 is then presented in line with four different solution methods, i.e., exact method, heuristic, metaheuristic, and machine learning. Finally, we discuss a number of topics worth pursuing in the future.

pdf/agile-earth-observation-satellite-scheduling-over-20-years-1xr26bs2r1.pdf

Agile Earth Observation Satellite Scheduling Over 20 Years: Formulations, Methods, and Future Directions

An improved genetic algorithm for the integrated satellite imaging and data transmission scheduling problem

A competitive Markov decision process model and a recursive reinforcement-learning algorithm for fairness scheduling of agile satellites

As is well known, satellite resources are extremely scarce relative to observation demands. Consequently, the Earth observation satellite (EOS) scheduling becomes a remarkable problem which is of significant importance. As an NP-hard problem, It is difficult to get an optimal solution. Furthermore, real-time scheduling makes it even more challenging for researchers. Unfortunately, although fruitful results have been achieved in the category of EOS scheduling, there still exist a number of obvious limitations and drawbacks. For example, the response speed and stability are always limited in the scheduling of urgent tasks that appeared stochastically. To overcome this obstacle, a reinforcement learning algorithm, which is of the ability to make a fast response for the urgent task scheduling, has been proposed in this paper. In order to improve scheduling stability and reduce computational complexity, hierarchical architecture with two layers has been established for the proposed algorithm. In each hierarchical layer, we adopt an online learning paradigm to explore a scheduling strategy at the learning phase. According to the algorithm, the satellite takes a scheduling action when urgent tasks arrive randomly according to a certain strategy. The environment will feedback to the satellite by the corresponding rewards of the actions taken. After multiple feedback, the satellite will select the action that can obtain the greatest benefit. In practical space applications, the satellite can employ the learned strategy to operate the low orbit satellite selection and observation time window (OTW) assignment for urgent tasks in stochastic scenarios, which realize an immediate schedule and maximize scheduling stability at the same time. Finally, a numerical experiment has been performed. The simulation results demonstrate that, compared to the heuristic m-WSITF algorithm, the proposed algorithm possesses significant advantages in effectiveness and efficiency, especially in response speed and stability.

/pdf/hierarchical-reinforcement-learning-for-real-time-scheduling-46urjcvdf3.pdf

Hierarchical Reinforcement-Learning for Real-Time Scheduling of Agile Satellites

Agile Earth Observing Satellite (AEOS) scheduling problem consists of selecting a subset of tasks and developing observation plans for a set of agile satellite resources with the purpose of maximizing the total reward of arranged mission observations. This problem has attracted much attention in recent years since AEOS is a new generation satellite being developed all over the world. Due to its NP-hardness, heuristic methods are widely adopted when solving the AEOS scheduling problem (AEOSSP). In this paper, we propose a temporal conflict network-based heuristic algorithm (TBHA), for AEOSSP. The novelty of TBHA lies in the fact that the heuristics are extracted from a temporal conflict network, which characterizes the overlaps (conflicts) of the visible time windows of the problem. These heuristics are highly effective since they well address the time window conflicts which otherwise pose a significant challenge on the choice of the imaging start time for satellite observations. The extensive simulation experiments with the comparison to a number of heuristic algorithm variants and sophisticated meta-heuristic algorithms are conducted to show that the TBHA algorithm performs very well in terms of both solution quality and computational efficiency.

/pdf/a-heuristic-algorithm-based-on-temporal-conflict-network-for-20yyfpgi5w.pdf

A Heuristic Algorithm Based on Temporal Conflict Network for Agile Earth Observing Satellite Scheduling Problem

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A Heuristic Algorithm Based on Temporal Conflict Network for Agile Earth Observing Satellite Scheduling Problem