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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

25
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...
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Area Computation by the Alternative Coordinate Method01:24

Area Computation by the Alternative Coordinate Method

29
The alternative coordinate method, also known as the Shoelace Formula, is a technique for determining the area of a traverse using Cartesian coordinates. This method relies on the sequential arrangement of x and y coordinates for each point of the shape, ensuring accuracy and ease of application.In this approach, each corner's x and y coordinates are listed as fractions, with the x-coordinate as the numerator and the y-coordinate as the denominator. These coordinates are arranged sequentially...
29
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

217
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
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Statically Indeterminate Problem Solving01:16

Statically Indeterminate Problem Solving

334
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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Randomized Experiments01:13

Randomized Experiments

6.6K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
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Woodward–Hoffmann Selection Rules and Microscopic Reversibility01:34

Woodward–Hoffmann Selection Rules and Microscopic Reversibility

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Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...
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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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一个改进的自适应变量邻里搜索算法用于随机顺序分配问题.

Zhenzhong Zhang1, Ling Zhang2, Weichun Li1

  • 1CAAC Academy, Civil Aviation Flight University of China, Chengdu, 618307, China.

Scientific reports
|January 3, 2025
PubMed
概括
此摘要是机器生成的。

本研究引入了智能随机优化的新框架,以改善供应链中的订单分配. 该方法有效地处理不确定性和高维度问题,提高效率和利.

关键词:
订单分配的分配方式订单不确定性 订单不确定性场景生成的场景生成.随机优化算法 随机优化算法

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科学领域:

  • 运营研究 运营研究
  • 供应链管理 供应链管理
  • 人工智能的人工智能

背景情况:

  • 实际的供应链运营面临着巨大的复杂性,原因是不确定性,如不可预测的客户订单.
  • 有效的订单分配对于提高整体供应链效率至关重要.
  • 现实世界的生产环境的特点是高维和随机条件.

研究的目的:

  • 开发一个数学模型,用于在订单分配中进行高维随机优化.
  • 为了解决生产中客户订单取消的不确定性.
  • 为了最大限度地提高订单处理中的预期利.

主要方法:

  • 为一个高维度随机优化问题开发了一个数学模型.
  • 集成了一个智能优化算法与一个场景生成方法.
  • 采用了一种适用于一般化分配问题和场景生成以进行评估的算法.

主要成果:

  • 拟议的框架有效地解决了高维随机订单分配问题.
  • 与现有方法相比,这种方法显示出更高的效率和能力.
  • 实验结果验证了智能随机优化框架的有效性.

结论:

  • 新的框架为复杂的订单分配问题提供了有效的解决方案.
  • 智能优化和场景生成是管理供应链不确定性的关键.
  • 该研究提高了在随机供应链环境中的效率和利最大化.