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Bensana, Eric; Lemaı̂tre, Michel; Verfaillie, G.

doi:10.1023/a:1026488509554

Cited by 118 publications

(20 citation statements)

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“…The spot5 Model: The SPOT5 earth observation satellite management problem [3] tries to find a subset of a given set of photographs to take, given many different constraints, including minimum distance, non-overlapping, and recording capacity. The model encodes these constraints as a set of binary and ternary table constraints.…”

Section: Models and Their Reformulationmentioning

confidence: 99%

Core-Guided Model Reformulation

Leo

Gange

Banda

et al. 2020

Lecture Notes in Computer Science

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Constraint propagation and SAT solvers often underperform when dealing with optimisation problems that have an additive (or separable) objective function. The core-guided search introduced by MaxSAT solvers can overcome this weakness by detecting and exploiting cores: subsets of the objective components that cannot collectively take their lower bounds. This paper shows how to use the information collected during core-guided search, to reformulate the objective function for an entire class of problems (those captured by the problem model). The resulting (currently manual) method is examined on several case studies, with very promising results.

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Section: Models and Their Reformulationmentioning

confidence: 99%

Core-Guided Model Reformulation

Leo

Gange

Banda

et al. 2020

Lecture Notes in Computer Science

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“…Numerous studies have been developed on scheduling EOSs, and most of them concentrated on the single-satellite problem. This problem can be generalized or converted to more common problems, such as a single-machine scheduling problem [16,17], a network flow problem [18], the knapsack problem [19][20][21][22], dynamic programming [10,23], or constraint programming [7,10,24,25]. Many previous studies use suboptimal algorithms.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In this model, the constraints (18)(19)(20)(21)(22)(23) are added to the observation task scheduling model. Constraints (18) initialize the amount of stored data.…”

Section: Constellation Mission Schedulingmentioning

confidence: 99%

“…Constraints (19) show that the amount of data stored at the beginning of each interval is inside the lower and upper limits. Constraints (20) represent the amount of data stored at the beginning of each interval equal to the amount stored at the start of the preceding interval plus the amount acquired by scheduled observation tasks minus the amount downloaded to the ground stations. Constraints (21) initialize the amount of stored energy.…”

Section: Constellation Mission Schedulingmentioning

confidence: 99%

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Optimization-Based Scheduling Method for Agile Earth-Observing Satellite Constellation

Cho

Kim

Choi

et al. 2018

Journal of Aerospace Information Systems

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This Paper proposes a two-step binary linear programming formulation for task scheduling of a constellation of low-Earth-orbit satellites and demonstrates its applicability and scalability to obtain high-quality solutions using a standard mixed-integer linear programming solver. In this instance, the goal of satellite constellation task scheduling is to allocate each task for the satellites and to determine the task starting times in order to maximize the overall mission performance metric. The scheduling problem is formulated to find the solution by first finding a set of candidate communication time intervals for each satellite/ground-station pair as one of the key constraints and time tabling the observation task to acquire the user-requested data, with the incorporation of key constraints for satellite constellation operation. Numerical experiments are designed for investigating the trends, sensitivity, and characteristics of scheduling outputs based on multiple representative instances. The performance of the scheduling solutions by the proposed two-step binary linear programming method exhibits significant improvement of up to 35% in the number of assignments and the sum of profits over the general greedy algorithm.

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“…For dynamic emergency scheduling, one remarkable feature is the arrival of new emergency imaging missions. Some scholars have used the complete reprogramming algorithm to remodel and solve this problem [11,12,13]. However, the new mission scheduling plan generated is greatly different from the original one, which leads to difficulty in satellite rescheduling.…”

Section: Introductionmentioning

confidence: 99%

Application of a Multi-Satellite Dynamic Mission Scheduling Model Based on Mission Priority in Emergency Response

Cui

Zhang

2019

Sensors

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Emergency observations are missions executed by Earth observation satellites to support urgent ground operations. Emergency observations become more important for meeting the requirements of highly dynamic and highly time-sensitive observation missions, such as disaster monitoring and early warning. Considering the complex scheduling problem of Earth observation satellites under emergency conditions, a multi-satellite dynamic mission scheduling model based on mission priority is proposed in this paper. A calculation model of mission priority is designed for emergency missions based on seven impact factors. In the satellite mission scheduling, the resource constraints of scheduling are analyzed in detail, and the optimization objective function is built to maximize the observation mission priority and mission revenues, and minimize the waiting time for missions that require urgency for execution time. Then, the hybrid genetic tabu search algorithm is used to obtain the initial satellite scheduling plan. In case of the dynamic arrival of new emergency missions before scheduling plan releases, a dynamic scheduling algorithm based on mission priority is proposed to solve the scheduling problem caused by newly arrived missions and to obtain the scheduling plan of newly arrived missions. A simulation experiment was conducted for different numbers of initial missions and newly arrived missions, and the scheduling results were evaluated with a model performance evaluation function. The results show that the execution probability of high-priority missions increased because the mission priority was taken into account in the model. In the case of more satellite resources, when new missions dynamically arrived, the satellite resources can be reasonably allocated to these missions based on the mission priority. Overall, this approach reduces the complexity of the dynamic adjustment and maintains the stability of the initial scheduling plan.

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Cited by 118 publications

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Core-Guided Model Reformulation

Core-Guided Model Reformulation

Optimization-Based Scheduling Method for Agile Earth-Observing Satellite Constellation

Application of a Multi-Satellite Dynamic Mission Scheduling Model Based on Mission Priority in Emergency Response

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