Satellite scheduling of large areal tasks for rapid response to natural disaster using a multi-objective genetic algorithm

Niu, Xiaonan; Tang, Hong; Wu, Lei

doi:10.1016/j.ijdrr.2018.02.013

Cited by 68 publications

(35 citation statements)

References 19 publications

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“…Lastly, the scheduling problems which is the most discussed category of MOCOPs consists of open shop scheduling problem [125], [126], job shop scheduling problem (JSSP) [127], [128], [129], [130], [131], [132], FSP [133], [134], [135], [136], [137], [138], project scheduling problem (PSP) [139], resource constrained PSP (RCPSP) [140], [141], [142], [143], [144], [145], timetabling problem [146], cross-docking scheduling problem [147], task scheduling problem [148], [149], [150], [151], [152], [153], [154], machine scheduling problem [155], [156], [157], [158], [159], [160], [161], satellite range scheduling problem [162], multi-objective satellite data transmission scheduling problem [163], satellite scheduling of large areal tasks [164], operating room scheduling [165], [166], harvest scheduling problem [167], energy-efficiency scheduling problem…”

Section: A Nsga-ii For Mocopsmentioning

confidence: 99%

A Comprehensive Review on NSGA-II for Multi-Objective Combinatorial Optimization Problems

2021

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This paper provides an extensive review of the popular multi-objective optimization algorithm NSGA-II for selected combinatorial optimization problems viz. assignment problem, allocation problem, travelling salesman problem, vehicle routing problem, scheduling problem, and knapsack problem. It is identified that based on the manner in which NSGA-II has been implemented for solving the aforementioned group of problems, there can be three categories: Conventional NSGA-II, where the authors have implemented the basic version of NSGA-II, without making any changes in the operators; the second one is Modified NSGA-II, where the researchers have implemented NSGA-II after making some changes into it and finally, Hybrid NSGA-II variants, where the researchers have hybridized the conventional and modified NSGA-II with some other technique. The article analyses the modifications in NSGA-II and also discusses the various performance assessment techniques used by the researchers, i.e., test instances, performance metrics, statistical tests, case studies, benchmarking with other state-of-the-art algorithms. Additionally, the paper also provides a brief bibliometric analysis based on the work done in this study.

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Section: A Nsga-ii For Mocopsmentioning

confidence: 99%

A Comprehensive Review on NSGA-II for Multi-Objective Combinatorial Optimization Problems

2021

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“…The area targets must be partitioned into a set of contiguous strips based on the different characteristics of the satellites and disaster emergent imaging requirements. Therefore, before satellite scheduling, we use a multi-objective optimizing model (Niu et al, 2018) to segment the emergent area tasks so as to obtaining a group of decomposing results, which are the inputs of the scheduling model.…”

Section: Objectivesmentioning

confidence: 99%

Multi-Satellite Observation Scheduling for Large Area Disaster Emergency Response

Niu

Tang

2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Timely acquiring remote sensing data is very important for rapid response to disasters. Satellite task scheduling aiming at making an optimal imaging plan, plays a key role in coordinating multiple satellites to monitor the disaster area. In the paper, to generate imaging plan dynamically according to the disaster relief, we propose a dynamic satellite task scheduling method for large area disaster response. First, an initial robust scheduling scheme is generated by a robust satellite scheduling model in which both the profit and the robustness of the schedule are simultaneously maximized. Then, we use a multi-objective optimization model to obtain a series of decomposing schemes. Based on the initial imaging plan, we propose a mixed optimizing algorithm named HA_NSGA-II to allocate the decomposing results thus to obtain an adjusted imaging schedule. A real disaster scenario, i.e., 2008 Wenchuan earthquake, is revisited in terms of rapid response using satellite resources and used to evaluate the performance of the proposed method with state-of-the-art approaches. We conclude that our satellite scheduling model can optimize the usage of satellite resources so as to obtain images in disaster response in a more timely and efficient manner.

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“…As for multi-objective models, to solve the problem of large-area multi-satellite imaging scheduling, Li [27] divided the target area using the equal distance grid method; then, he established the constraint satisfaction model with the maximum coverage of the target area, minimum overlap of the imaging strip, and shortest imaging time as the objective functions. Niu [28] used the multi-satellite large-area mission planning to solve the emergency response problem during natural disasters. First, the imaging strip sequence was obtained by discretizing the satellite swing angle to decompose the regional targets.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Objective Modeling Method of Multi-Satellite Imaging Task Planning for Large Regional Mapping

Chen

Shen

et al. 2020

Remote Sensing

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Regional remote sensing image products are playing an important role in an increasing number of application fields. Aiming at multi-satellite imaging task planning for large-area image acquisition, this paper proposes a multi-objective modeling method. First, we analyzed the core requirements of regional mapping for multi-satellite imaging mission planning: Full coverage of the target area and low consumption of satellite resources. Second, an optimization model with two objective functions, namely the maximum target area coverage and minimum satellite resource utilization, was established. Using the selection of imaging strips and their swing angles as two types of decision variables, the regional decomposition and satellite resource allocation were integrated into the planning model. Third, two efficient algorithms, Vatti and non-dominated sorting genetic algorithm (NSGA-II), were used for objective function calculation and model solving, respectively. Finally, the experiments used Hubei, Finland, and Congo as the target areas and GF1, GF6, ZY1-02C, and ZY3 as imaging satellites to verify the modeling method proposed in this paper. The experiments showed that the proposed multi-objective modeling method could complete the coverage of regional targets with fewer satellite resources and improve the satellite application efficiency significantly.

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Satellite scheduling of large areal tasks for rapid response to natural disaster using a multi-objective genetic algorithm

Cited by 68 publications

References 19 publications

A Comprehensive Review on NSGA-II for Multi-Objective Combinatorial Optimization Problems

A Comprehensive Review on NSGA-II for Multi-Objective Combinatorial Optimization Problems

Multi-Satellite Observation Scheduling for Large Area Disaster Emergency Response

A Multi-Objective Modeling Method of Multi-Satellite Imaging Task Planning for Large Regional Mapping

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