MULTI-SATELLITE OBSERVATION SCHEDULING FOR LARGE AREA DISASTER EMERGENCY RESPONSE

The process of satellite mission scheduling, which plays a significant role in rapid response to emergent disasters, e.g. earthquake, is used to allocate the observation resources and execution time to a series of imaging tasks by maximizing one or more objectives while satisfying certain given constraints. In practice, the information obtained of disaster situation changes dynamically, which accordingly leads to the dynamic imaging requirement of users. We propose a satellite scheduling model to address dynamic imaging tasks triggered by emergent disasters. The goal of proposed model is to meet the emergency response requirements so as to make an imaging plan to acquire rapid and effective information of affected area. In the model, the reward of the schedule is maximized. To solve the model, we firstly present a dynamic segmenting algorithm to partition area targets. Then the dynamic heuristic algorithm embedding in a greedy criterion is designed to obtain the optimal solution. To evaluate the model, we conduct experimental simulations in the scene of Wenchuan Earthquake. The results show that the simulated imaging plan can schedule satellites to observe a wider scope of target area. 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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MULTI-SATELLITE SCHEDULING APPROACH FOR DYNAMIC AREAL TASKS TRIGGERED BY EMERGENT DISASTERS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xli-b1-475-2016 ◽

2016 ◽

Vol XLI-B1 ◽

pp. 475-481 ◽

Cited By ~ 1

Author(s):

X. N. Niu ◽

X. J. Zhai ◽

H. Tang ◽

L. X. Wu

Keyword(s):

Disaster Response ◽

Optimal Solution ◽

Dynamic Imaging ◽

Target Area ◽

Satellite Mission ◽

Efficient Manner ◽

Scheduling Model ◽

Proposed Model ◽

Disaster Situation ◽

Satellite Scheduling

The process of satellite mission scheduling, which plays a significant role in rapid response to emergent disasters, e.g. earthquake, is used to allocate the observation resources and execution time to a series of imaging tasks by maximizing one or more objectives while satisfying certain given constraints. In practice, the information obtained of disaster situation changes dynamically, which accordingly leads to the dynamic imaging requirement of users. We propose a satellite scheduling model to address dynamic imaging tasks triggered by emergent disasters. The goal of proposed model is to meet the emergency response requirements so as to make an imaging plan to acquire rapid and effective information of affected area. In the model, the reward of the schedule is maximized. To solve the model, we firstly present a dynamic segmenting algorithm to partition area targets. Then the dynamic heuristic algorithm embedding in a greedy criterion is designed to obtain the optimal solution. To evaluate the model, we conduct experimental simulations in the scene of Wenchuan Earthquake. The results show that the simulated imaging plan can schedule satellites to observe a wider scope of target area. 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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A multi-agent framework for cloud-based management of collaborative robots

International Journal of Advanced Robotic Systems ◽

10.1177/1729881418785073 ◽

2018 ◽

Vol 15 (4) ◽

pp. 172988141878507 ◽

Cited By ~ 1

Author(s):

Tooba Samad ◽

Sohail Iqbal ◽

Asad Waqar Malik ◽

Omar Arif ◽

Peter Bloodsworth

Keyword(s):

Disaster Response ◽

Traditional Approach ◽

Geographical Area ◽

Physical World ◽

Resource Assignment ◽

Large Area ◽

Disaster Situation ◽

Multi Agent ◽

Assignment Strategy ◽

Disaster Scenario

This article presents a cloud-based multi-agent architecture for the intelligent management of aerial robots in a disaster response situation. In a disaster scenario, a team of highly maneuverable quadcopters is deployed to carry out surveillance and decision support in disaster-affected areas. In Pakistan, such events usually result from sudden unpredictable calamities such as earthquakes. The aim of this work is to develop a robust mechanism to autonomously manage and react to sensory inputs received in soft real time from an unstructured environment. The immediate goal is to locate the maximum number of trapped, injured people within a large area, and help first responders plan rescue activities accordingly. To evaluate the proposed framework, a number of simulations are carried out using GAMA platform to emulate a disaster environment. Subsequently, algorithms are developed to survey an affected geographical area through the use of small flight drones. The key challenges in this work are related to the combination of the domains of multi-agent technology, robotics, and cloud computing for effectively bridging the cyber world with the physical world. Therefore, the proposed work demonstrates the effective use of a limited number of drones to capture inputs from a disaster situation in the physical world, and such inputs are used for timely planning of rescue efforts. The results of fixed resource assignment are compared with the proposed reactive assignment strategy, and it clearly shows a significant improvement in terms of resource usage compared to traditional approach.

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

Sensors ◽

10.3390/s19061430 ◽

2019 ◽

Vol 19 (6) ◽

pp. 1430 ◽

Cited By ~ 4

Author(s):

Jintian Cui ◽

Xin Zhang

Keyword(s):

Dynamic Scheduling ◽

Search Algorithm ◽

Earth Observation ◽

Impact Factors ◽

Scheduling Problem ◽

Model Based ◽

Scheduling Model ◽

Earth Observation Satellites ◽

Scheduling Plan ◽

Satellite Scheduling

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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Robust Satellite Scheduling Approach for Dynamic Emergency Tasks

Mathematical Problems in Engineering ◽

10.1155/2015/482923 ◽

2015 ◽

Vol 2015 ◽

pp. 1-20 ◽

Cited By ~ 7

Author(s):

Xuejun Zhai ◽

Xiaonan Niu ◽

Hong Tang ◽

Lixin Wu ◽

Yonglin Shen

Keyword(s):

Genetic Algorithm ◽

Heuristic Algorithm ◽

Optimal Solution ◽

Simulation Experiments ◽

Multiobjective Genetic Algorithm ◽

Scheduling Model ◽

Rapid Responses ◽

Earth Observation Satellites ◽

Satellite Scheduling ◽

Combined Algorithm

Earth observation satellites play a significant role in rapid responses to emergent events on the Earth’s surface, for example, earthquakes. In this paper, we propose a robust satellite scheduling model to address a sequence of emergency tasks, in which both the profit and robustness of the schedule are simultaneously maximized in each stage. Both the multiobjective genetic algorithm NSGA2 and rule-based heuristic algorithm are employed to obtain solutions of the model. NSGA2 is used to obtain a flexible and highly robust initial schedule. When every set of emergency tasks arrives, a combined algorithm called HA-NSGA2 is used to adjust the initial schedule. The heuristic algorithm (HA) is designed to insert these tasks dynamically to the waiting queue of the initial schedule. Then the multiobjective genetic algorithm NSGA2 is employed to find the optimal solution that has maximum revenue and robustness. Meanwhile, to improve the revenue and resource utilization, we adopt a compact task merging strategy considering the duration of task execution in the heuristic algorithm. Several experiments are used to evaluate the performance of HA-NSGA2. All simulation experiments show that the performance of HA-NSGA2 is significantly improved.

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Fair energy-efficient virtual machine scheduling for Internet of Things applications in cloud environment

International Journal of Distributed Sensor Networks ◽

10.1177/1550147717694890 ◽

2017 ◽

Vol 13 (2) ◽

pp. 155014771769489 ◽

Cited By ~ 6

Author(s):

Guowen Xing ◽

Xiaolong Xu ◽

Haolong Xiang ◽

Shengjun Xue ◽

Sai Ji ◽

...

Keyword(s):

Internet Of Things ◽

Virtual Machine ◽

Energy Efficient ◽

Energy Savings ◽

Virtual Machines ◽

Machine Scheduling ◽

Cloud Services ◽

Efficient Manner ◽

Scheduling Method ◽

Virtual Machine Scheduling

With the rapid resource requirements of Internet of Things applications, cloud computing technology is regarded as a promising paradigm for resource provision. To improve the efficiency and effectiveness of cloud services, it is essential to improve the resource fairness and achieve energy savings. However, it is still a challenge to schedule the virtual machines in an energy-efficient manner while taking into consideration the resource fairness. In view of this challenge, a fair energy-efficient virtual machine scheduling method for Internet of Things applications is designed in this article. Specifically, energy and fairness are analyzed in a formal way. Then, a virtual machine scheduling method is proposed to achieve the energy efficiency and further improve the resource fairness during the executions of Internet of Things applications. Finally, experimental evaluation demonstrates the validity of our proposed method.

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Intelligent Scheduling Method Oriented to Multi-Varieties and Small-Batch Production Mode

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.263-266.1269 ◽

2012 ◽

Vol 263-266 ◽

pp. 1269-1274

Author(s):

Dan Chen Zhou ◽

Liang Zeng

Keyword(s):

Load Balance ◽

Job Shop ◽

Job Shop Scheduling ◽

Additional Constraint ◽

Batch Production ◽

Production Mode ◽

Scheduling Model ◽

Proposed Model ◽

Scheduling Method ◽

Annealing Algorithm

In terms of characteristics of scheduling problem in multi-varieties and small-batch production mode, three additional constraint conditions including combination constraint, priority scheduling and machine load balance are supplemented based on classical Job-shop scheduling model in order to reflect the actual scheduling activities better. An improved genetic annealing algorithm is proposed aiming at the above model, and the requirement of supplemented constraint conditions is satisfied by means of improving the decoding process of chromosomes. The key technologies of algorithm are discussed in detail. The feasibility and validity of the proposed model and algorithm is demonstrated through simulated computation.

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Discussion of “Comparison of Linear Scheduling Model and Repetitive Scheduling Method” by Kris G. Mattila and Amy Park

Journal of Construction Engineering and Management ◽

10.1061/(asce)0733-9364(2004)130:3(461) ◽

2004 ◽

Vol 130 (3) ◽

pp. 461-463 ◽

Cited By ~ 4

Author(s):

Photios G. Ioannou ◽

I-Tung Yang

Keyword(s):

Scheduling Model ◽

Linear Scheduling ◽

Scheduling Method

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A DroneGo Disaster Relief Response System

Remote Sensing ◽

10.18282/rs.v9i1.1094 ◽

2020 ◽

Vol 9 (1) ◽

pp. 6

Author(s):

Ge Zhang ◽

Kangli Ma ◽

Chang Liu

Keyword(s):

Puerto Rico ◽

Disaster Response ◽

Computational Results ◽

Analysis Model ◽

Medical Supply ◽

Response System ◽

Delivery Routes ◽

Disaster Scenario ◽

Future Work ◽

The Greedy Algorithm

<p>To support the Puerto Rico hurricane disaster scenario, we develop a DroneGo disaster response system by establishing the following models. First, we establish a location analysis model for ISO containers based on the coverage of video reconnaissance and the priority comparison between the two required missions–medical supply delivery and video reconnaissance. According to the locations of 11 harbors in Puerto Rico, we select three suitable harbors to position three cargo containers called CON 1, 2 and 3 to conduct the missions. Second, we build two packing configuration models to design the packing configuration for containers. In one model, we recommend a drone fleet for CON 1 and 3 according to reconnaissance conditions, and then put drones into containers in order. In another model for CON 2, we determine the type of drones according to the medical supply demands of hospitals. For both models, the number of drones of each type is determined by the enumeration method and the packing placement is determined by the greedy algorithm. The algorithms are coded in Visual C++ and MATLAB. The computational results show that the space utilizations for the three containers are all above 80.8%. Third, we design a drone flight plan model based on graph theory. According to the time and space constraints of drones, we devise flight plans as well as delivery routes and schedule. The computational results show that the coverage of video reconnaissance is up to 70.1%. Finally, we carry out the error and sensitivity analysis, discuss the strengths and weaknesses of our models, and design the future work. In addition, a two-page memo that summarizes our modeling results, conclusions, and recommendations is given at the end of the paper.</p>

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Evolution and Recovery: Adaptable Housing Reconstruction in Post Disaster Scenarios

10.26686/wgtn.16992904.v1 ◽

2021 ◽

Author(s):

◽

Liam Shearer

Keyword(s):

Disaster Response ◽

Research Question ◽

Reconstruction Process ◽

Major Disaster ◽

Building Typology ◽

Displaced People ◽

Housing Reconstruction ◽

Disaster Scenario ◽

Design Proposal ◽

Post Disaster

<p>Every year disasters affect hundreds of millions of people, causing damage that can take months or years to recover from. The reality of carrying out the processes of reconstruction and recreating functionality is a complex and difficult task; too often it is measured in a time period of several years. The issue to be addressed through this research is the response of the built (or rebuilt) environment to the requirements of people who have been displaced following a major disaster. This thesis develops a building typology and process that can adapt to the changing requirements of the stages of the redevelopment process in a post‐disaster scenario. The research focuses on natural disasters, more vulnerable populations and regions and specifically on housing reconstruction. It explores the idea of a solution that can be applied widely, to many different climates and contexts; the research question then amounts to ‘can a solution be created that can ‘evolve’ to meet the needs at each stage of a post disaster reconstruction scenario?’ The thesis explores existing post‐disaster response and reconstruction models and discusses the focuses and priorities of each. The requirements of displaced people are studied, in terms of response by the built environment, and the benefits of staged development versus end product discussed. The roles that major groups, such as local authorities and NGOs, play in orchestrating the reconstruction process are discussed as well as the important, and sometimes overlooked, role that those affected by the disaster may have. The discussion and research then informs the design proposal. Four sites are selected and used as parameters for developing the built response to the first stage of reconstruction. The selected sites are then used to show how a generic shelter may first be adapted to be suitable for a specific climate and context and then how they may be added to and grown to become permanent and suitable housing for the displaced people. The staged redevelopment process from a partially generic emergency deployment presented in this thesis can provide a solution, or framework for a solution, to many of the problems raised by the research and here, but it cannot be a solution by itself; architecture or design in post‐disaster scenarios must be supported and driven heavily by planning and management from local, national and international sources to be successful and fully realised.</p>

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