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

Wang, Jianjiang; Demeulemeester, Erik; Qiu, Dishan

doi:10.1016/j.cor.2016.04.014

Cited by 74 publications

(47 citation statements)

References 38 publications

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“…Closely related optimization problems that focus on scheduling ground station communications are studied in [57] and [4], and a rolling-horizon strategy is combined with different heuristics for dynamic scheduling of Earth observing satellites in [11]. A number of different scheduling models and algorithms for earth observing satellites are reported in the references of [51].…”

Section: Sensor Scheduling Problem Backgroundmentioning

confidence: 99%

“…However, the benefits of directly addressing uncertainty may be significant, further im-proving the utilization of expensive and over-subscribed remote sensing assets. The literature on models and techniques for proactively scheduling remote sensors against uncertainty stemming from cloud cover [51,29], movement of a target under observation, system bandwidth resources, or collection utility is limited. In [29], the authors develop a stochastic mixed-integer program and make use of novel heuristics to maximize the rewards of completed collections and maximize the likelihood of successful collections.…”

Section: Sensor Scheduling Problem Backgroundmentioning

confidence: 99%

“…The model considered therein builds a feasible schedule for the FORMOSAT-2 satellite. The authors of [51] use a chance-constrained programming model and associated heuristics to proactively schedule against uncertain cloud-cover conditions. Our major contribution in this paper is the introduction of an optimization model for collection scheduling under uncertainty, and subsequent evaluation of the overall utility of this model in improving collection schedules when executed in practice.…”

Section: Sensor Scheduling Problem Backgroundmentioning

confidence: 99%

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Dynamic Multi-Sensor Multi-Mission Optimal Planning Tool

Valicka¹,

Rowe²,

Zou³

et al. 2016

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Remote sensing systems have firmly established a role in providing immense value to commercial industry, scientific exploration, and national security. Continued maturation of sensing technology has reduced the cost of deploying highly-capable sensors while at the same time increased reliance on the information these sensors can provide. The demand for time on these sensors is unlikely to diminish. Coordination of next-generation sensor systems, larger constellations of satellites, unmanned aerial vehicles, ground telescopes, etc. is prohibitively complex for existing heuristicsbased scheduling techniques. The project was a two-year collaboration spanning multiple Sandia centers and included a partnership with Texas A&M University. We have developed algorithms and software for collection scheduling, remote sensor field-of-view pointing models, and bandwidthconstrained prioritization of sensor data. Our approach followed best practices from the operations research and computational geometry communities. These models provide several advantages over state of the art techniques. In particular, our approach is more flexible compared to heuristics that tightly couple models and solution techniques. First, our mixed-integer linear models afford a rigorous analysis so that sensor planners can quantitatively describe a schedule relative to the best possible. Optimal or near-optimal schedules can be produced with commercial solvers in operational run-times. These models can be modified and extended to incorporate different scheduling and resource constraints and objective function definitions. Further, we have extended these models to proactively schedule sensors under weather and ad hoc collection uncertainty. This approach stands in contrast to existing deterministic schedulers which assume a single future weather or ad hoc collection scenario. The field-of-view pointing algorithm produces a mosaic with the fewest number of images required to fully cover a region of interest. The bandwidth-constrained algorithms find the highest priority information that can be transmitted. All of these are based on mixed-integer linear programs so that, in the future, collection scheduling, field-of-view, and bandwidth prioritization can be combined into a single problem. Experiments conducted using the developed models, commercial solvers, and benchmark datasets have demonstrated that proactively scheduling against uncertainty regularly and significantly outperforms deterministic schedulers.

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Section: Sensor Scheduling Problem Backgroundmentioning

confidence: 99%

Section: Sensor Scheduling Problem Backgroundmentioning

confidence: 99%

Section: Sensor Scheduling Problem Backgroundmentioning

confidence: 99%

See 1 more Smart Citation

Dynamic Multi-Sensor Multi-Mission Optimal Planning Tool

Valicka¹,

Rowe²,

Zou³

et al. 2016

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show abstract

“…Further examples for integer linear programming (ILP) applied to scheduling problems are the scheduling of sport events (Nemhauser and Trick 1998;Durán et al 2007) and the scheduling of physicians in the emergency room (Beaulieu et al 2000). More related, mathematical programming has also been applied for scheduling Earth observations via satellites, e.g., Marinelli et al (2011) and Wang et al (2016). Moreover, several authors have presented approaches to scheduling the observations of astronomical telescopes.…”

Section: Introductionmentioning

confidence: 99%

Combinatorial optimization applied to VLBI scheduling

Corbin

Niedermann

Nothnagel

et al. 2020

J Geod

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Due to the advent of powerful solvers, today linear programming has seen many applications in production and routing. In this publication, we present mixed-integer linear programming as applied to scheduling geodetic very-long-baseline interferometry (VLBI) observations. The approach uses combinatorial optimization and formulates the scheduling task as a mixed-integer linear program. Within this new method, the schedule is considered as an entity containing all possible observations of an observing session at the same time, leading to a global optimum. In our example, the optimum is found by maximizing the sky coverage score. The sky coverage score is computed by a hierarchical partitioning of the local sky above each telescope into a number of cells. Each cell including at least one observation adds a certain gain to the score. The method is computationally expensive and this publication may be ahead of its time for large networks and large numbers of VLBI observations. However, considering that developments of solvers for combinatorial optimization are progressing rapidly and that computers increase in performance, the usefulness of this approach may come up again in some distant future. Nevertheless, readers may be prompted to look into these optimization methods already today seeing that they are available also in the geodetic literature. The validity of the concept and the applicability of the logic are demonstrated by evaluating test schedules for five 1-h, single-baseline Intensive VLBI sessions. Compared to schedules that were produced with the scheduling software sked, the number of observations per session is increased on average by three observations and the simulated precision of UT1-UTC is improved in four out of five cases (6 µs average improvement in quadrature). Moreover, a simplified and thus much faster version of the mixed-integer linear program has been developed for modern VLBI Global Observing System telescopes. Keywords Combinatorial optimization • Mixed-integer linear programming • Geodetic VLBI • Scheduling • Local sky coverage • VGOS B A.

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“…Each paper has its own specific conditions. These conditions include types of satellite's operation (imaging, downloading or both) [1], maneuverability of satellites (not agile or agile) [2,3,4], number of tasks (single or multi) [2], number of satellites (single or constellation) [5,6,7,8,9,10,11,12,13], and number of ground stations (single or multi) [6,7].…”

Section: Introductionmentioning

confidence: 99%

Earth Observation Satellites Optimization; Survey and Analysis

Abbas

Meselhey

Mogheth

et al. 2018

The International Conference on Applied Mechanics and Mechanica

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The problem of earth observation satellites planning and scheduling contains many objectives and constraints. The objectives include downloaded data, profit of scheduled tasks and quality (weight) values. The constraints are related to energy, data, targets (tasks) and ground stations. Many optimization methods have been used to optimize this problem such as dynamic programming (DP), simulated annealing (SA), ant colony optimization (ACO), genetic algorithm (GA) and constraint programming approach (CPA). Each reported research used a harmonized combination of objective(s), constrains, and optimization techniques. The presented work investigates the anatomy of this optimization problem. It is also analyzes the findings of the previous relation between the optimization problem elements and gaps in past research. Furthermore shows that there is a gap between data's objectives (maximizing the total amount of downloaded data) and the targets' constraints (observation of tasks, scheduling and consecutive observation).The most widely used optimization methods are dynamic programming and heuristic algorithms, respectively. Moreover, the most widely objectives are those relative to profit. Few discussions are also presented concerning the constraints which involve data rate, range of ground stations and capacity of ground stations.

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

Cited by 74 publications

References 38 publications

Dynamic Multi-Sensor Multi-Mission Optimal Planning Tool

Dynamic Multi-Sensor Multi-Mission Optimal Planning Tool

Combinatorial optimization applied to VLBI scheduling

Earth Observation Satellites Optimization; Survey and Analysis

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