基于分支定界和遗传算法的平台资源调度方法

Q1 Decision Sciences
Yanfen Zhang, Jinyao Ma, Haibin Zhang, Bin Yue
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引用次数: 0

摘要

平台资源调度是一个任务与平台资源匹配的运筹学优化问题,在生产或营销安排布局、作战任务规划等方面有重要应用。现有算法在任务规划序列上不灵活,稳定性差。针对这一缺陷,本文将分枝定界算法与遗传算法相结合。分枝定界算法可以自适应地调整下一个要计划的任务,并计算出各种可行的任务计划序列。遗传算法用于为所选任务分配平台组合。此外,我们还提出了一种新的下界计算方法和修剪规则。在直接后续任务处理时间的基础上,考虑了任务的资源需求对任务优先级的影响。数值实验表明,该算法在平台资源调度问题上具有良好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Platform Resource Scheduling Method Based on Branch-and-Bound and Genetic Algorithm

Platform resource scheduling is an operational research optimization problem of matching tasks and platform resources, which has important applications in production or marketing arrangement layout, combat task planning, etc. The existing algorithms are inflexible in task planning sequence and have poor stability. Aiming at this defect, the branch-and-bound algorithm is combined with the genetic algorithm in this paper. Branch-and-bound algorithm can adaptively adjust the next task to be planned and calculate a variety of feasible task planning sequences. Genetic algorithm is used to assign a platform combination to the selected task. Besides, we put forward a new lower bound calculation method and pruning rule. On the basis of the processing time of the direct successor tasks, the influence of the resource requirements of tasks on the priority of tasks is considered. Numerical experiments show that the proposed algorithm has good performance in platform resource scheduling problem.

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来源期刊
Annals of Data Science
Annals of Data Science Decision Sciences-Statistics, Probability and Uncertainty
CiteScore
6.50
自引率
0.00%
发文量
93
期刊介绍: Annals of Data Science (ADS) publishes cutting-edge research findings, experimental results and case studies of data science. Although Data Science is regarded as an interdisciplinary field of using mathematics, statistics, databases, data mining, high-performance computing, knowledge management and virtualization to discover knowledge from Big Data, it should have its own scientific contents, such as axioms, laws and rules, which are fundamentally important for experts in different fields to explore their own interests from Big Data. ADS encourages contributors to address such challenging problems at this exchange platform. At present, how to discover knowledge from heterogeneous data under Big Data environment needs to be addressed.     ADS is a series of volumes edited by either the editorial office or guest editors. Guest editors will be responsible for call-for-papers and the review process for high-quality contributions in their volumes.
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