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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

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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.
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Application of Nonlinear Inequalities01:29

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A nonlinear inequality describes a comparison involving an expression that curves or behaves more complexly than a straight line. These inequalities often appear in forms that include squares, products, or variables in the denominator.To solve such an inequality, one starts by rewriting it so that zero appears on one side. For example, the inequality:  can be factored as: This form makes it easier to identify the values that cause the expression to equal zero. In this case, the...
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Mathematical Modeling: Problem Solving01:29

Mathematical Modeling: Problem Solving

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Mathematical modeling transforms real-world scenarios into mathematical expressions, allowing for structured problem-solving and analysis. This process involves defining the situation, assigning variables to measurable quantities, selecting an appropriate model, and solving the resulting equation. Such models are invaluable in finance, providing precise methods to evaluate investments, loans, and repayment structures.A widely used example is the calculation of fixed monthly payments on a loan,...
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Statically Indeterminate Problem Solving01:16

Statically Indeterminate Problem Solving

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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...
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Optimization Problems01:26

Optimization Problems

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Optimization problems often involve identifying maximum or minimum values under specific constraints. A well-known example is determining the longest horizontal pipe that can be moved around a right-angled corner, where a 3-meter-wide hallway meets a 2-meter-wide hallway. This scenario, common in architectural design and industrial transport, can be understood conceptually through geometric and trigonometric reasoning.To visualize the problem, consider the pipe as a straight line that touches...
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Introduction to Nonlinear Inequalities

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相关实验视频

Updated: Jan 17, 2026

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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数据驱动的机会受限制的混合整数非线性双层优化Via Copulas:应用到集成规划和调度问题.

Syu-Ning Johnn1, Hasan Nikkhah2,3, Meng-Lin Tsai4

  • 1University College London, Department of Chemical Engineering, The Sargent Centre for Process Systems Engineering, London, WC1E 7JE, UK.

Systems & control transactions
|September 15, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种基于copula的框架,用于管理供应链规划和调度挑战,这些挑战是由需求相关性引起的. 它通过优化不确定性下的决策来确保更高的需求满足和更低的成本.

关键词:
两级优化 两级优化子理论 子理论数据驱动优化的优化.衍生式自由优化 衍生式自由优化规划和调度 计划和调度

相关实验视频

Last Updated: Jan 17, 2026

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

13.4K

科学领域:

  • 运营研究 运营研究
  • 供应链管理 供应链管理
  • 数据科学数据科学数据科学

背景情况:

  • 由于相关的多变量需求数据,供应链规划和调度面临挑战.
  • 考虑到数据依赖性的准确需求预测对于有效的优化至关重要.
  • 现有的方法可能无法充分解决决策中需求相关性的复杂性.

研究的目的:

  • 提出一个机会受限的优化框架,并与用于规划和调度问题的配方集成.
  • 预测不确定的需求水平,同时考虑指定的风险门和数据依赖性.
  • 提高综合规划和调度解决方案的质量和可行性.

主要方法:

  • 采用非参数技术的copula,用于不确定性下的需求预测.
  • 在综合规划和调度问题上采用双级优化公式.
  • 将需求预测集成到数据驱动的双层混合整数非线性问题的优化框架 (DOMINO) 中.

主要成果:

  • 拟议的框架成功地将需求相关性纳入了优化过程.
  • 与传统方法相比,实现了更高的联合需求满足率.
  • 在计算实验中证明了更低的总成本和更高的效率.

结论:

  • 基于copula的机会受限优化对于处理供应链中的需求相关性是有效的.
  • 该框架为在需求不确定性下进行决策提供了强有力的方法.
  • 这种方法提高了供应链的弹性和经济绩效.