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相关概念视频

Multimachine Stability01:25

Multimachine Stability

128
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
128
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

38
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
38

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Corrigendum to ``Efficient scheduling of multiple software projects for work continuity and identical completion time'' [MethodsX 14 (2025) 103215].

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Updated: May 22, 2025

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
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多个软件项目的高效调度,以确保工作连续性和相同的完成时间.

Abdulrahman Aldhubaiban1, Ali AlMatouq1

  • 1Department of Engineering Management, Prince Sultan University, Riyadh, PO box 66863 Rafha Street, Riyadh, 11586, Riyadh, Saudi Arabia.

MethodsX
|March 17, 2025
PubMed
概括

本研究提出了一种用于优化软件项目调度的新模型. 它确保所有项目同时完成,尽量减少成本,并最大限度地利用资源,以实现高效的项目管理.

科学领域:

  • 运营研究 运营研究
  • 软件工程管理 软件工程管理

背景情况:

  • 有效地管理多个软件项目,共享资源,对于实现运营和战略目标至关重要.
  • 尽量减少项目成本和员工置时间,需要无地重新分配资源.

研究的目的:

  • 开发一个连续变量的非线性模型,以实现多个软件项目的最佳调度.
  • 确保所有项目的完成日期相同,同时保持资源连续性.

主要方法:

  • 开发一个连续变量非线性编程模型.
  • 利用基于云的架构进行在线优化解决方案集成.
  • 通过随机生成器对大型软件项目实例进行验证.

主要成果:

  • 该模型成功地安排了多达40个软件项目和100名员工.
  • 通过使用非线性编程算法,在不到21分钟的时间内找到最佳解决方案.
  • 证明了有效的资源重新分配和工作连续性.

结论:

  • 拟议的模型为大规模的多项目软件调度提供了有效的解决方案.
  • 基于云的优化可实现高效和及时的解决问题.
关键词:
自动软件管理自动化软件管理多个软件项目的有效调度,以确保工作的连续性和相同的完成时间.数学模型是一个数学模型.多目标优化多目标优化软件项目调度时间表

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  • 该方法将成本降至最低,并最大限度地利用软件开发中的资源.