Upper bounds for revenue maximization in a satellite scheduling problem

Benoist, Thierry; Rottembourg, Benoît

doi:10.1007/s10288-004-0044-8

Cited by 25 publications

(18 citation statements)

References 7 publications

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“…Mathematical programming: Benoist et al [5], Habet et al [25], Lemaître et al [28] and Tangpattanakul et al [48] developed general mathematical programming models for EOS scheduling. Liao et al [31], Lin et al [32,33] and Marinelli et al [39] proposed the time-indexed formulation of EOS scheduling, and established integer programming models.…”

Section: Previous Workmentioning

confidence: 99%

“…Exact algorithms: Bensana et al [6] proposed a depth-first branch and bound algorithm for SPOT-5 satellite scheduling. Also, Benoist et al [5], Bensana et al [6] and Verfaillie et al [52] suggested Russian Doll search algorithms, which are based on branch-andbound but replace one search by n successive searches on nested subproblems, using the results of each search when solving larger subproblems, to improve the lower bound on the global valuation of any partial assignment. Besides, Gabrel et al [20] and Lemaître et al…”

Section: Previous Workmentioning

confidence: 99%

“…Constraints (4) enforce that each task can only be scheduled to the orbits that are available for it. There must be sufficient setup times between consecutive tasks for transformations, which is enforced in constraints (5). Constraints (6) check that the memory consumption of the scheduled tasks cannot exceed the memory capacity for each orbit.…”

Section: Expectation Modelmentioning

confidence: 99%

See 2 more Smart Citations

Proactive Scheduling Algorithms for Multiple Earth Observation Satellites Under Uncertainties of Clouds

2015

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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.

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Section: Previous Workmentioning

confidence: 99%

Section: Previous Workmentioning

confidence: 99%

Section: Expectation Modelmentioning

confidence: 99%

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Proactive Scheduling Algorithms for Multiple Earth Observation Satellites Under Uncertainties of Clouds

2015

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“…Bensana et al [4] structured a depth first branch and bound algorithm on the basis of constraint satisfaction model. Benoist and Rottembourg [13] introduced the Russian dolls approach to verify the upper bounds of the satellite scheduling problem with benchmark testing. Considering the EOS scheduling problem is NP-hard [9], the optimal solution is hardly trackable for large-scale EOS scheduling instances.…”

Section: Introductionmentioning

confidence: 99%

Scheduling Multiple Agile Earth Observation Satellites for Oversubscribed Targets Using Complex Networks Theory

et al. 2019

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The Earth observation satellites (EOSs) scheduling is of great importance to achieve efficient observation missions. The agile EOSs (AEOS) with stronger attitude maneuvering capacity can greatly improve observation efficiency while increasing scheduling complexity. The multiple AEOSs, oversubscribed targets scheduling problem with multiple observations are addressed, and the potential observation missions are modeled as nodes in the complex networks. To solve the problem, an improved feedback structured heuristic is designed by defining the node and target importance factors. On the basis of a real world Chinese AEOS constellation, simulation experiments are conducted to validate the heuristic's efficiency in comparison with a constructive algorithm and a structured genetic algorithm.

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“…The satellite image downlink scheduling problem and its variations have been studied by many authors. Some of these works were focused on case studies for specific satellites or space missions [11,14,20] whereas others are more general purpose in nature [9,10,12,15,16,29,31,32,33]. Literature from machine scheduling [7,25,28,27] and resource-constrained project scheduling [19,26] are also relevant in solving the satellite image downlink scheduling problem (SIDSP).…”

Section: Introductionmentioning

confidence: 99%

Satellite downlink scheduling problem: A case study

Karapetyan¹,

Minic²,

Malladi³

et al. 2015

Omega

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The synthetic aperture radar (SAR) technology enables satellites to efficiently acquire high quality images of the Earth surface. This generates significant communication traffic from the satellite to the ground stations, and, thus, image downlinking often becomes the bottleneck in the efficiency of the whole system. In this paper we address the downlink scheduling problem for Canada's Earth observing SAR satellite, RADARSAT-2. Being an applied problem, downlink scheduling is characterised with a number of constraints that make it difficult not only to optimise the schedule but even to produce a feasible solution. We propose a fast schedule generation procedure that abstracts the problem specific constraints and provides a simple interface to optimisation algorithms. By comparing empirically several standard meta-heuristics applied to the problem, we select the most suitable one and show that it is clearly superior to the approach currently in use.

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Upper bounds for revenue maximization in a satellite scheduling problem

Cited by 25 publications

References 7 publications

Proactive Scheduling Algorithms for Multiple Earth Observation Satellites Under Uncertainties of Clouds

Proactive Scheduling Algorithms for Multiple Earth Observation Satellites Under Uncertainties of Clouds

Scheduling Multiple Agile Earth Observation Satellites for Oversubscribed Targets Using Complex Networks Theory

Satellite downlink scheduling problem: A case study

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