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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.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
149
Decision Making: P-value Method01:09

Decision Making: P-value Method

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The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
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Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

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Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
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Response Surface Methodology01:16

Response Surface Methodology

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Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
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Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

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The maximum power flow for lossy transmission lines is derived using ABCD parameters in phasor form. These parameters create a matrix relationship between the sending-end and receiving-end voltages and currents, allowing the determination of the receiving-end current. This relationship facilitates calculating the complex power delivered to the receiving end, from which real and reactive power components are derived.
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相关实验视频

Updated: Sep 12, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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一个多目标的多期数学编程模型,用于整合项目组合优化和承包商选择.

Mostafa Zahedirad1, Kaveh Khalili-Damghani1, Vahidreza Ghezavati1

  • 1Department of Industrial Engineering, ST.C., Islamic Azad University, Tehran, Iran.

MethodsX
|August 8, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了项目组合优化和承包商选择的两种方法. 一个同时优化两个方面的集成模型显著优于顺序方法,提供更好的结果和更快的计算.

关键词:
承包商能力规划 承包商能力规划选择承包商选择承包商选择综合规划 综合规划 综合规划项目组合优化项目组合优化项目风险规划 项目风险规划

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科学领域:

  • 运营研究 运营研究
  • 管理科学 管理科学
  • 数学优化的数学优化

背景情况:

  • 项目组合优化和承包商选择是关键而又复杂的决定.
  • 现有的方法往往将这些问题视为单独的问题,可能导致低于最佳的结果.
  • 在各种约束下,平衡利,风险和技术能力等多个目标是具有挑战性的.

研究的目的:

  • 开发和比较两个不同的建模方法,用于整合项目组合优化和承包商选择.
  • 为了评估同步优化模型与顺序方法的有效性.
  • 分析拟议方法的计算性能和结果.

主要方法:

  • 制定两个混合整数数学编程模型:一个顺序型和一个集成型.
  • 应用目标编程 (GP) 来解决多目标优化问题.
  • 考虑多个目标 (利,风险,能力,成本) 和限制 (关系,通货膨胀,资源).
  • 通过实践案例研究和时间复杂性的分析进行验证.

主要成果:

  • 综合模型 (情景2) 与顺序模型 (情景1) 相比,显示出更高的性能.
  • 场景2在实现项目和承包商选择目标方面取得了更好的总体结果.
  • 综合方法显著提高了CPU时间效率.

结论:

  • 通过综合模型同时优化项目组合和承包商选择比顺序方法更有效.
  • 目标编程为解决这个领域中复杂的多目标优化问题提供了一个强大的框架.
  • 综合模型为战略项目和承包商管理提供了计算效率高且实际上优越的解决方案.