scholarly journals Robust Satellite Scheduling Approach for Dynamic Emergency Tasks

2015 ◽  
Vol 2015 ◽  
pp. 1-20 ◽  
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.

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

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1430 ◽  
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.

Vehicle Scheduling Model for Fresh Agriculture Products Pickup with Uncertain Demands

2014 ◽  
Vol 974 ◽  
pp. 282-287
Author(s):  
Li Xia Rong ◽  
Huan Bin Sha
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Optimal Solution ◽  
Vehicle Scheduling ◽  
Uncertain Measure ◽  
Uncertainty Distribution ◽  
Uncertain Variables ◽  
Scheduling Model ◽  
Agriculture Products ◽  
The Impact

A chance-constrained vehicle scheduling model for fresh agriculture products pickup with uncertain demands is proposed in this paper. The uncertain measure that vehicle loading will not exceed capacity constraint is presented in the model because of the uncertainty of demands. Based on uncertainty theory, when the demands are some special uncertain variables with uncertainty distribution such as linear, zigzag and normal uncertain distribution etc., the model can be transformed to a deterministic form and solved by genetic algorithm. When the demands are general uncertain variables, a hybrid genetic algorithm with uncertain simulation is presented to obtain the optimal solution. At last, to illustrate the effective of the model and algorithm, and to analyze the impact of parameters on model solution, an experiment is provided.

scholarly journals An Effective Heuristic-Based Approach for Partitioning

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Xibin Zhao ◽  
Hehua Zhang ◽  
Yu Jiang ◽  
Songzheng Song ◽  
Xun Jiao ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Embedded System ◽  
Simulated Annealing Algorithm ◽  
Optimal Solution ◽  
Local Optimal Solution ◽  
Annealing Algorithm ◽  
The Cost ◽  
Combined Algorithm ◽  
Search Capability

As being one of the most crucial steps in the design of embedded systems, hardware/software partitioning has received more concern than ever. The performance of a system design will strongly depend on the efficiency of the partitioning. In this paper, we construct a communication graph for embedded system and describe the delay-related constraints and the cost-related objective based on the graph structure. Then, we propose a heuristic based on genetic algorithm and simulated annealing to solve the problem near optimally. We note that the genetic algorithm has a strong global search capability, while the simulated annealing algorithm will fail in a local optimal solution easily. Hence, we can incorporate simulated annealing algorithm in genetic algorithm. The combined algorithm will provide more accurate near-optimal solution with faster speed. Experiment results show that the proposed algorithm produce more accurate partitions than the original genetic algorithm.

scholarly journals MULTI-SATELLITE SCHEDULING APPROACH FOR DYNAMIC AREAL TASKS TRIGGERED BY EMERGENT DISASTERS

2016 ◽  
Vol XLI-B1 ◽  
pp. 475-481 ◽  
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.

Multivariant Optimization Algorithm for the 0-1 Knapsack Problem

2014 ◽  
Vol 556-562 ◽  
pp. 3514-3518
Author(s):  
Lan Juan Liu ◽  
Bao Lei Li ◽  
Qin Hu Zhang ◽  
Dan Jv Lv ◽  
Xin Ling Shi ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Knapsack Problem ◽  
Heuristic Algorithm ◽  
Optimization Algorithm ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Global Search ◽  
Particle Swarm Algorithm ◽  
Data Sets ◽  
Better Than

In this paper, a novel heuristic algorithm named Multivariant Optimization Algorithm (MOA) is presented to solve the 0-1 Knapsack Problem (KP). In MOA, multivariant search groups (locate and global search groups) execute the global exploration and local exploitation iteratively to locate the optimal solution automatically. The presented algorithm has been compared with Genetic Algorithm (GA) and Particle swarm algorithm (PSO) based on five data sets, results show that the optimization of MOA is better than GA and PSO when the dimension of problem is high.

scholarly journals Hybrid Evolutionary-Heuristic Algorithm for Capacitor Banks Allocation

2010 ◽  
Vol 61 (6) ◽  
pp. 332-340 ◽  
Author(s):  
Marinko Barukčić ◽  
Srete Nikolovski ◽  
Franjo Jović
Keyword(s):  
Genetic Algorithm ◽  
Heuristic Algorithm ◽  
Optimal Allocation ◽  
Hybrid Methods ◽  
Optimal Solution ◽  
Distribution Networks ◽  
Computing Methods ◽  
Evolutionary Stage ◽  
Capacitor Banks

Hybrid Evolutionary-Heuristic Algorithm for Capacitor Banks Allocation The issue of optimal allocation of capacitor banks concerning power losses minimization in distribution networks are considered in this paper. This optimization problem has been recently tackled by application of contemporary soft computing methods such as: genetic algorithms, neural networks, fuzzy logic, simulated annealing, ant colony methods, and hybrid methods. An evolutionaryheuristic method has been proposed for optimal capacitor allocation in radial distribution networks. An evolutionary method based on genetic algorithm is developed. The proposed method has a reduced number of parameters compared to the usual genetic algorithm. A heuristic stage is used for improving the optimal solution given by the evolutionary stage. A new cost-voltage node index is used in the heuristic stage in order to improve the quality of solution. The efficiency of the proposed two-stage method has been tested on different test networks. The quality of solution has been verified by comparison tests with other methods on the same test networks. The proposed method has given significantly better solutions for time dependent load in the 69-bus network than found in references.

scholarly journals MULTI-SATELLITE SCHEDULING APPROACH FOR DYNAMIC AREAL TASKS TRIGGERED BY EMERGENT DISASTERS

2016 ◽  
Vol XLI-B1 ◽  
pp. 475-481 ◽  
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.

scholarly journals SIMULATED ANNEALING GENETIC ALGORITHM-BASED HARVESTER OPERATION SCHEDULING MODEL

2021 ◽  
pp. 249-260
Author(s):  
Qingkai Zhang ◽  
Guangqiao Cao ◽  
Junjie Zhang ◽  
Yuxiang Huang ◽  
Cong Chen ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Scheduling Algorithm ◽  
Supply And Demand ◽  
Operating Cost ◽  
Optimal Solution ◽  
Agricultural Machinery ◽  
Simulated Annealing Genetic Algorithm ◽  
Scheduling Model ◽  
Operation Scheduling

To address problems involving the poor matching ability of supply and demand information and outdated scheduling methods in agricultural machinery operation service, in this study, we proposed a harvester operation scheduling model and algorithm for an order-oriented multi-machine collaborative operation within a region. First, we analysed the order-oriented multi-machine collaborative operation within the region and the characteristics of agricultural machinery operation scheduling, examined the revenue of a mechanized harvesting operation and the components of each cost, and constructed a harvester operation scheduling model with the operation income as the optimization goal. Second, we proposed a simulated annealing genetic algorithm-based harvester operation scheduling algorithm and analysed the validity and stability of the algorithm through experimental simulations. The results showed that the proposed harvester operation scheduling model effectively integrated the operating cost, transfer cost, waiting time cost, and operation delay cost of the harvester, and the accuracy of the harvester operation scheduling model was improved; the harvester operation scheduling algorithm based on simulated annealing genetic algorithm (SAGA) was able to obtain a global near-optimal solution of high quality and stability with high computational efficiency.

scholarly journals Energy-Efficient Multi-User Routing in a Software-Defined Multi-Hop Wireless Network

2019 ◽  
Vol 11 (6) ◽  
pp. 133 ◽  
Author(s):  
Ziqi Liu ◽  
Gaochao Xu ◽  
Peng Liu ◽  
Xiaodong Fu ◽  
Yang Liu
Keyword(s):  
Genetic Algorithm ◽  
Wireless Network ◽  
Energy Efficient ◽  
Routing Algorithm ◽  
Optimal Solution ◽  
Global Routing ◽  
Improved Genetic Algorithm ◽  
Efficient Management ◽  
Routing Strategy ◽  
Combined Algorithm

Software-defined networking (SDN) is an innovative architecture that designs a logical controller to manage and program the network based on the global view, providing more efficient management, better performance, and higher flexibility for the network. Therefore, applying the SDN concept in a multi-hop wireless network (MWN) has been proposed and extensively studied to overcome the challenges of MWN. In this paper, we propose an energy-efficient global routing algorithm for a software-defined multi-hop wireless network (SDMWN), which is able to get transmission paths for several users at the same time to minimize the global energy consumption with the premise of satisfying the QoS required by users. To this end, we firstly propose a Lagrange relaxation-based aggregated cost (LARAC) and K-Dijkstra combined algorithm to get the top K energy-minimum paths that satisfy the QoS in polynomial time. Then, we combine the alternative paths of each user obtained by K-LARAC and propose an improved genetic algorithm to solve the global routing strategy. The simulation results show that the proposed K-LARAC and genetic algorithm combined method has the ability to obtain an approximate optimal solution with lower time cost.

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