Ground Antenna Scheduling Algorithm for Multi-Satellite Tracking

2011 ◽  
Author(s):  
Sang-Hyuk Yun ◽  
Hyo-Sung Ahn ◽  
Sun-Ju Park ◽  
Ok-Chul Jung ◽  
Dae-Won Chung
Keyword(s):  
Weight Function ◽  
Greedy Algorithm ◽  
Scheduling Algorithm ◽  
Optimal Schedule ◽  
Satellite Tracking ◽  
Scheduling Problem ◽  
Depth First Search ◽  
Ground Station ◽  
A Priority

In this paper, we address the optimal ground antenna scheduling problem for multiple satellites when multiple satellites have visibility conflicts at a ground station. Visibility conflict occurs when multiple satellites have either overlapping visibilities at a ground station or difference with time of loss of signal (LOS) of a satellite and time of acquisition of signal (AOS) of another satellite is less than reconfiguration time of ground station. Each satellite has a priority value that is a weight function with various factors. Multi-antenna scheduling (MAS) algorithm 1 and Multi-antenna scheduling (MAS) algorithm 2 are proposed to find the optimal schedule of multi-antenna at a ground station using pre-assigned priority values of satellites. We use the depth first search (DFS) method to search the optimal schedule in MAS algorithm 1 and MAS algorithm 2. Through the simulations, we confirm the efficiency of these algorithms by comparing with greedy algorithm.

ONLINE SCHEDULING OF DYNAMIC TREES

1995 ◽  
Vol 05 (04) ◽  
pp. 635-646 ◽  
Author(s):  
MICHAEL A. PALIS ◽  
JING-CHIOU LIOU ◽  
SANGUTHEVAR RAJASEKARAN ◽  
SUNIL SHENDE ◽  
DAVID S.L. WEI
Keyword(s):  
Lower Bound ◽  
Competitive Ratio ◽  
Scheduling Algorithm ◽  
Optimal Schedule ◽  
Online Scheduling ◽  
The Other ◽  
Static Case ◽  
Scheduling Problem ◽  
Dynamic Trees ◽  
Dynamic Tree

The scheduling problem for dynamic tree-structured task graphs is studied and is shown to be inherently more difficult than the static case. It is shown that any online scheduling algorithm, deterministic or randomized, has competitive ratio Ω((1/g)/ log d(1/g)) for trees with granularity g and degree at most d. On the other hand, it is known that static trees with arbitrary granularity can be scheduled to within twice the optimal schedule. It is also shown that the lower bound is tight: there is a deterministic online tree scheduling algorithm that has competitive ratio O((1/g)/ log d(1/g)). Thus, randomization does not help.

scholarly journals Smart DAG Tasks Scheduling between Trusted and Untrusted Entities Using the MCTS Method

2019 ◽  
Vol 11 (7) ◽  
pp. 1826 ◽  
Author(s):  
Yuxia Cheng ◽  
Zhiwei Wu ◽  
Kui Liu ◽  
Qing Wu ◽  
Yu Wang
Keyword(s):  
Reinforcement Learning ◽  
Heterogeneous Computing ◽  
Heuristic Algorithms ◽  
Scheduling Algorithm ◽  
Optimal Schedule ◽  
Scheduling Problem ◽  
Scheduling Policies ◽  
Monte Carlo Tree Search ◽  
Reward Function ◽  
Tasks Scheduling

Task scheduling is critical for improving system performance in the distributed heterogeneous computing environment. The Directed Acyclic Graph (DAG) tasks scheduling problem is NP-complete and it is hard to find an optimal schedule. Due to its key importance, the DAG tasks scheduling problem has been extensively studied in the literature. However, many previously proposed traditional heuristic algorithms are usually based on greedy methods and also lack the consideration of scheduling tasks between trusted and untrusted entities, which makes the problem more complicated, but there still exists a large optimization space to be explored. In this paper, we propose a trust-aware adaptive DAG tasks scheduling algorithm using the reinforcement learning and Monte Carlo Tree Search (MCTS) methods. The scheduling problem is defined using the reinforcement learning model. Efficient scheduling state space, action space and reward function are designed to train the policy gradient-based REINFORCE agent. The MCTS method is proposed to determine actual scheduling policies when DAG tasks are simultaneously executed in trusted and untrusted entities. Leveraging the algorithm’s capability of exploring long term reward, the proposed algorithm could achieve good scheduling policies while guaranteeing trusted tasks scheduled within trusted entities. Experimental results showed the effectiveness of the proposed algorithm compared with the classic HEFT/CPOP algorithms.

A NEW ALGORITHM FOR MULTI-SKILL RESOURCE CONSTRAINED PROJECT SCHEDULING PROBLEM BASED ON CUCKOO SEARCH STRATEGY

2020 ◽  
Vol 65 (6) ◽  
pp. 98-109
Author(s):  
Huu Dang Quoc ◽  
Loc Nguyen The ◽  
Cuong Nguyen Doan ◽  
Toan Phan Thanh
Keyword(s):  
Project Scheduling ◽  
Search Strategy ◽  
Scheduling Algorithm ◽  
Optimal Schedule ◽  
Cuckoo Search ◽  
Scheduling Problem ◽  
Resource Constrained ◽  
Start Time ◽  
Resource Constrained Project Scheduling ◽  
Project Scheduling Problem

The purpose of this paper is to consider the project scheduling problem under such limited constraint, called Multi-Skill Resource-Constrained Project Scheduling Problem or MS-RCPSP. The algorithm proposed in this paper is to find the optimal schedule, determine the start time for each task so that the execution time (also called makespan) taken is minimal. At the same time, our scheduling algorithm ensures that the given priority relationships and constraints are not violated. Our scheduling algorithm is built based on the Cuckoo Search strategy. In order to evaluate the proposed algorithm, experiments were conducted by using the iMOPSE dataset. The experimental results proved that the proposed algorithm found better solutions than the previous algorithm.

Scheduling Problem of Biochemical Analyzer and Exploration of Neural Network-Greedy Algorithm

2020 ◽  
Vol 10 (8) ◽  
pp. 1912-1918
Author(s):  
Xiaohui Huang ◽  
Shuxia Zheng ◽  
Shilong Li ◽  
Jinxiang Wu ◽  
Graham Spence
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Greedy Algorithm ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Scheduling Problem ◽  
The Neural Network ◽  
Biochemical Analyzer ◽  
Analysis System ◽  
The Mathematical Model

The mathematical model of biochemical analysis system was established based on neural network-greedy algorithm. The optimal task scheduling sequence was solved by neural network algorithm. At the same time, the local optimization was obtained by combining greedy algorithm. In this way, the task scheduling problem in biochemical analyzer was transformed into a mathematical problem, and the mathematical model of scheduling algorithm was established. On the platform of MATLAB, eight groups of simulation tests were carried out on the same task scheduling problem using the neural network-greedy scheduling algorithm and the traditional fixedperiod scheduling algorithm. The task-time Gantt charts of the two algorithms were compared under different scheduling orders. The results showed that the average speed of the neural network-greedy algorithm was improved by 31% compared with that of the fixed-period scheduling algorithm. The mathematical model of biochemical analysis system on scheduling problem established by neural network-greedy scheduling algorithm has high efficiency compared with the traditional fixed-period scheduling algorithm.

scholarly journals AN EFFICIENT TASK SCHEDULING IN DISTRIBUTED COMPUTING SYSTEMS BY IMPROVED GENETIC ALGORITHM

2013 ◽  
pp. 52-58
Author(s):  
K. SUNITHA ◽  
MRS. P V SUDHA
Keyword(s):  
Genetic Algorithm ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Optimal Schedule ◽  
Heterogeneous Systems ◽  
Simultaneous Optimization ◽  
Multiprocessor System ◽  
Scheduling Problem ◽  
Improved Genetic Algorithm ◽  
Distributed Computing Systems

Task Scheduling problem for heterogeneous systems is concerned with arranging the various tasks to be executed on various processors of a system so that computing resources are utilized most effectively. Parallel processing refers to the concept of speeding-up the execution of a task by dividing the task into multiple fragments that can execute simultaneously, each on its own processor i.e. it is the simultaneous processing of the task on two or more processors in order to obtain faster results. It can be effectively used for tasks that involve a large number of calculations, have time constraints and can be divided into a number of smaller tasks. The scheduling problem deals with the optimal assignment of a set of tasks onto parallel multiprocessor system and orders their execution so that the total completion time is minimized. An Optimal scheduling of parallel tasks with some precedence relationship, onto a parallel machine is known to be NP-complete. This precedence relationship among tasks can be represented as Directed Acyclic Graph (DAG). In this paper, a scheduling algorithm has been proposed to schedule DAG tasks on Heterogeneous processor which uses Genetic algorithm to get optimal schedule. The scheduling problem is also considered. This study includes a search for an optimal mapping of the task and their sequence of execution and also search for an optimal configuration of the parallel system. An approach for the simultaneous optimization of all these three components of scheduling method using genetic algorithm is presented and its performance is evaluated in comparison with the Min-Min and Max-Min scheduling methods.

A multi-shop integrated scheduling algorithm with fixed output constraint

2021 ◽  
pp. 1-9
Author(s):  
Yingchun Xia ◽  
Zhiqiang Xie ◽  
Yu Xin ◽  
Xiaowei Zhang
Keyword(s):  
Constraint Programming ◽  
Programming Model ◽  
Scheduling Algorithm ◽  
Processing Parameters ◽  
Scheduling Problem ◽  
Integrated Scheduling ◽  
Scheduling Scheme ◽  
Wide Range ◽  
Constraint Programming Model ◽  
Output Constraint

The customized products such as electromechanical prototype products are a type of product with research and trial manufacturing characteristics. The BOM structures and processing parameters of the products vary greatly, making it difficult for a single shop to meet such a wide range of processing parameters. For the dynamic and fuzzy manufacturing characteristics of the products, not only the coordinated transport time of multiple shops but also the fact that the product has a designated output shop should be considered. In order to solve such Multi-shop Integrated Scheduling Problem with Fixed Output Constraint (MISP-FOC), a constraint programming model is developed to minimize the total tardiness, and then a Multi-shop Integrated Scheduling Algorithm (MISA) based on EGA (Enhanced Genetic Algorithm) and B&B (Branch and Bound) is proposed. MISA is a hybrid optimization method and consists of four parts. Firstly, to deal with the dynamic and fuzzy manufacturing characteristics, the dynamic production process is transformed into a series of time-continuous static scheduling problem according to the proposed dynamic rescheduling mechanism. Secondly, the pre-scheduling scheme is generated by the EGA at each event moment. Thirdly, the jobs in the pre-scheduling scheme are divided into three parts, namely, dispatched jobs, jobs to be dispatched, and jobs available for rescheduling, and at last, the B&B method is used to optimize the jobs available for rescheduling by utilizing the period when the dispatched jobs are in execution. Google OR-Tools is used to verify the proposed constraint programming model, and the experiment results show that the proposed algorithm is effective and feasible.

scholarly journals A two-agent one-machine multitasking scheduling problem solving by exact and metaheuristics

2021 ◽  
Author(s):  
Chin-Chia Wu ◽  
Ameni Azzouz ◽  
Jia-Yang Chen ◽  
Jianyou Xu ◽  
Wei-Lun Shen ◽  
...  
Keyword(s):  
Simulated Annealing ◽  
Completion Time ◽  
Simulated Annealing Algorithm ◽  
Optimal Schedule ◽  
Computational Studies ◽  
Scheduling Problem ◽  
Optimal Solutions ◽  
Large Size ◽  
Annealing Algorithm ◽  
One Machine

AbstractThis paper studies a single-machine multitasking scheduling problem together with two-agent consideration. The objective is to look for an optimal schedule to minimize the total tardiness of one agent subject to the total completion time of another agent has an upper bound. For this problem, a branch-and-bound method equipped with several dominant properties and a lower bound is exploited to search optimal solutions for small size jobs. Three metaheuristics, cloud simulated annealing algorithm, genetic algorithm, and simulated annealing algorithm, each with three improvement ways, are proposed to find the near-optimal solutions for large size jobs. The computational studies, experiments, are provided to evaluate the capabilities for the proposed algorithms. Finally, statistical analysis methods are applied to compare the performances of these algorithms.

Sign in / Sign up

Export Citation Format

Share Document