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相关概念视频

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

26
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...
26
Statically Indeterminate Problem Solving01:16

Statically Indeterminate Problem Solving

340
Statically indeterminate problems are those where statics alone can not determine the internal forces or reactions. Consider a structure comprising two cylindrical rods made of steel and brass. These rods are joined at point B and restrained by rigid supports at points A and C. Now, the reactions at points A and C and the deflection at point B are to be determined. This rod structure is classified as statically indeterminate as the structure has more supports than are necessary for maintaining...
340
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

27
Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
27
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

45
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
45
Multimachine Stability01:25

Multimachine Stability

101
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:
101
Reinforcement Schedules01:24

Reinforcement Schedules

115
Positive reinforcement is a powerful method for teaching new behaviors to both animals and humans. B.F. Skinner demonstrated this with his experiments using rats in a Skinner box. When a rat pressed a lever, it received a food pellet. This immediate reward encouraged the rat to repeat the behavior. This method, where a reward follows every instance of the behavior, is known as continuous reinforcement. It is highly effective for establishing new behaviors quickly.
Once a behavior is learned,...
115

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相关实验视频

Updated: May 11, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

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双层数据驱动的全企业优化与混合整数非线性调度问题

Hasan Nikkhah1,2, Zahir Aghayev1,2, Amir Shahbazi1,2

  • 1Department of Chemical & Biomolecular Engineering, University of Connecticut, Storrs, 06269, CT,USA.

Digital Chemical Engineering
|April 18, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了DOMINO框架的综合企业范围优化 (EWO),有效地解决复杂的规划和调度问题. 在优化生产和满足市场需求方面,DOMINO-NOMAD表现出卓越的表现.

关键词:
两级编程 两级编程 两级编程 两级编程数据驱动优化的优化.综合规划和日程安排.混合整数非线性编程.

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相关实验视频

Last Updated: May 11, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

12.9K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

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科学领域:

  • 运营研究 运营研究
  • 工艺系统工程 工艺系统工程
  • 优化优化 优化优化

背景情况:

  • 全企业优化 (EWO) 需要整体的决策,以便在工艺工业中高效地利用资源.
  • 顺序规划和调度通常会产生不切实际的解决方案,因为它们相互依赖.
  • 双层编程提供了综合解决方案,但面临着混合整数非线性编程 (MINLP) 配方的挑战.

研究的目的:

  • 开发和应用一个数据驱动的算法,DOMINO,用于解决单领导多追随者规划和调度问题.
  • 解决现有方法在处理复杂,相互依存的优化层方面的局限性.
  • 为了评估DOMINO在大型工业问题上使用不同的优化解决方案的性能.

主要方法:

  • 使用了双层混合整数非线性问题的数据驱动优化框架 (DOMINO).
  • 将DOMINO应用于多种产品甲基甲酸聚合过程 (旅行销售员问题的制定).
  • 将DOMINO扩展到一个高维非线性原油炼油厂运行问题,比较NOMAD和ARGONAUT优化器.

主要成果:

  • 多米诺成功地实现了接近最佳的可行解决方案,用于规划和调度.
  • 在解决方案质量和可行性方面,DOMINO-NOMAD组合始终优于DOMINO-ARGONAUT.
  • 该研究证明了DOMINO在优化生产目标和满足复杂EWO的市场需求方面的能力.

结论:

  • 多米诺框架为工艺工业的综合双层优化提供了一种有效的方法.
  • 多米诺可实现高效的资源配置,并改善了对大规模EWO问题的决策.
  • 优化解决方案的选择 (NOMAD与ARGONAUT) 在DOMINO框架内显著影响性能.